Driven by a love for engineering (and solving complicated problems), I went on to pursue a B.Tech in Petroleum Engineering from the Indian Institute of Technology (ISM) Dhanbad, graduating in 2017. Those four years shaped not only my technical foundation but also my ability to survive on hostel mess food — a skill every engineer secretly prides themselves on.

Fun fact: In my first year of college, I initially joined the Petroleum Engineering branch — only to have it changed to Electrical Engineering due to internal sliding. But with some hard work (and a lot of chai-fueled study nights), I managed to earn the grades to slide back into Petroleum Engineering. So technically, I’ve switched branches twice before even starting my core courses!

Career Journey So far…

My career kicked off at PetroMinds Consulting LLP, where I joined as an engineering intern. This is where I built a strong base in reservoir and production engineering, fluid thermodynamics, and modelling.

After a year, I moved to Wood as a Flow Assurance Consultant, a role that played a huge part in shaping my technical expertise. Over five years, I got to work on steady‑state and transient flow assurance projects for major clients like Saudi Aramco, ADNOC, CNOOC, Woodside Energy, and Shell. I worked across concept select, FEED, and detailed design phases, dealing with all kinds of challenging (and fun) engineering puzzles around flow behaviour.
Fun fact: Flow assurance engineers often get excited about wax and hydrates — not your everyday dinner conversation topic.

After Wood, I joined KBR, Inc. as a Technical Professional in flow assurance, continuing my FEED and detailed design work for ADNOC and Shell. This deepened my exposure to large‑scale design environments and cross‑functional engineering workflows.

I then pivoted towards digital innovation by joining SLB as a Senior Production Engineer. Here, I worked on building an integrated digital surveillance tool for ONGC, focusing heavily on digital twin modelling and workflows such as network model validation, optimization, and well rate estimation.

Most recently, I had the chance to work with ExxonMobil as a Flow Assurance Engineer in operations. This experience gave me hands‑on exposure to real‑world challenges like hydraulic analysis, hydrate management, pigging operations, and WAG procedures. Working closely with principals and advisors helped me sharpen both technical and practical problem‑solving skills.

With diverse experience across consulting, digital innovation, and operator roles, I’m excited to have joined Pontem Analytics. Working with industry specialists and learning from their experience is truly energizing, and I’m looking forward to contributing meaningfully to the team.
Fun fact: “Pontem” means “bridge” in Latin — fitting, considering they connect deep expertise with real‑world impact.

Outside of work, I’m married and love spending quality time with my family. I also enjoy swimming, sports, and traveling, which help me stay active and balanced.

Fundamentally, nothing changes in the thermodynamics when dealing with hydrate formation or dissociation, and models are quite clear in determining stable or unstable hydrates for a given system. There are several models to evaluate the thermodynamics of a system that are accepted as standards in the oil and gas industry, such as Multiflash, PVTSim, and CSMGem.

Chemicals will shift the equilibrium curve. Depressurizing the system or providing latent heat will move the system’s thermodynamics relative to the hydrate equilibrium curve. One must, obviously, consider kinetics when asking how quickly a hydrate plug will form or melt. Colorado School of Mines Hydrate Plug Dissociation Tool (CSMPlug) predicts how quickly a hydrate plug will dissociate by solving transient heat-transfer and moving boundary (Stefan-type) problems with hydrate thermodynamic equilibrium constraints.

This post will provide an overview on the principle mathematic equations to solve the “how long” question and we’ll look at some examples to highlight how this may apply in the field.

Math Is Everywhere

As one may imagine, hydrate dissociation kinetics is quite dynamic and there is not a single equation to describe this process. Within CSMPlug, for example, there are multiple equations coupled together: transient cylindrical heat conduction, hydrate equilibrium thermodynamics, moving boundary, latent heat balance, and prescribed pressure history.

The model works by solving heat conduction at the surface of a hydrate cylinder while estimating dissociation due to temperature changes and pressure drops. Then it predicts how long these changes will take to fully dissociate the solid hydrate plug at the boundary. Once enough heat has been put into the system to fully dissociate hydrates at the surface, a new boundary is set with a smaller radius. This process continuously repeats until the plug is fully dissociated. Two limitations within the model are it does not simulate axial convection nor consider fluid flow dynamics (which makes sense to put this limitation in place as the hydrate plug is typically immobile!). In other words, the dissociation front moves radially inward as conditions change.

Math Fun (don’t worry, it’s Type 2 fun)

For a given hydrate plug, the series of continuously solved equations include:

A critical component for this model (and really the equation that determines how fast the hydrate dissociates) is at the phase boundary (shaded equation in the table), which is moving. The final solution is presented (hydrate is fully dissociated) when

s(t) = 0

What does this mean?!

For a hydrate plug, dissociation rate is controlled by Heat flux vs Latent heat requirement.

If the heat supply is small then dissociation is slow, which is the case if the flow line is insulated. A primary example of this that many will be (unfortunately) familiar with is melting a hydrate via depressurization alone. A flow line with a hydrate plug may begin with a dual-side depressurization, which initially takes the surface of the plug outside the hydrate equilibrium curve. There is an initial rapid dissociation (at the hydrate surface). However, this is an endothermic reaction and temperatures decrease locally (at the new surface) because heat is absorbed. This forces the conditions back across the equilibrium curve and hydrates are once again stable. The temperature can only warm up from heat provided from outside the flowline. If this is subsea, it will not warm up very much and will do so slowly. If the line is insulated, then that process is even slower.

This is why depressurization-only remediation can take several weeks in a subsea system with an insulated flow line. The seabed temperature is cold and does not provide much heat to the system and the U-value of the insulation severely limits the transfer of thermal energy.

Do I Really Need A Fancy Model For This?

There are sound engineering first-principle calculations out there, so won’t those work? That’s a great question. Let’s go through some examples and compare. Let’s look at a hydrate plug that has formed in a 20-mile subsea tieback. Here’s the system:

Let’s assume a 50 m full-bore plug forms in the line. The volume of this plug can be calculated as

V = (pi)r²L = p(0.127)²(50) = 2.54 m³

We’ll also assume hydrate density to be ~900 kg/m³, so the mass of the hydrate is 2,286 kg. This is a very conservative estimate, as it assumes 0% porosity and full conversion of hydrates (no free water/gas within this volume).

First Principle Calculations for Depressurization

As mentioned previously, depressurization is a viable method to dissociate hydrates because depressurization makes hydrates thermodynamically unstable. However, dissociation absorbs latent heat (it is an endothermic process and is self-cooling). After the initial flash dissociation, the plug temperature drops toward equilibrium. The heat to warm the system back above equilibrium temperature must then come from the surrounding seawater through a layer of insulation.

The heat flux from seawater is estimated by considering the surface area around the plug:

A = 2(pi)rL = 2pi(0.127)(50) = 39.9 m²

If we assume an insulted pipeline, h = 8 W/m²K (1.4 BTU/hr^.ft^2.°F), and a small driving force after depressurization (DT = 5°C) then,

Q = hADT = 8 x 39.9 x 5 = 1,596 W

Assuming the latent heat of a methane hydrate is 450 kJ/kg then the energy required to melt the hydrate is

Q_total = 2,286 x 450,000 = 1.03 x 10⁹ J

The dissociation time calculation becomes

t = (1.03 x 10⁹) / 1,596 = 645,000 sec = 179 hours = 7.5 days

If there is a more effective insulation and the heat transfer value is only 3 W/m²K (0.52 BTU/hr^.ft^2.°F), the dissociation time increases to 20 days. Technology giveth and technology taketh away! What was preserving thermal energy from the reservoir to carry along the flow line is now severely limiting thermal transfer from the seabed temperature into the system. This obviously helps to reduce the risk of hydrate formation, however once hydrates are present the remediation becomes more challenging. The chart showing various flow line insulations below highlights the differences and drives the point home.

Another important factor to consider is when pressures become very low (for a subsea tieback). Let’s consider a system that has managed to be blown down to 150 psi. The hydrate equilibrium temperature for Green Canyon Gas (a representative Gulf of America gas composition) is shown below. At equilibrium the system will slowly warm to 40°F by the surrounding ambient temperature, which will melt the hydrates. This will, in turn, cool the system down below freezing temperatures. The operation then risks ice formation!

This Catch-22 practical example is one reason why there are usually additional methods that are used in parallel for hydrate dissociation when depressurization is employed, which we’ll discuss below.

First Principle Calculations for Direct Electrical Heating

If we consider DEH for our dissociation example and assume 150 W/m is applied to the flowline, the total heating power applied to the hydrate plug is

P = 150 x 50 = 7,500 W

Let’s also assume there is only 85% effective heat applied, so P = 6,375 W. The same energy is required to dissociate the hydrate, which makes the time requirement

t = (1.03 x 10⁹) J / 6,375 W = 161,568 sec = 1.9 days

Obviously, reducing deferred production from 3 weeks to 2 days is a huge advantage. Unfortunately, installation CAPEX and OPEX costs to provide power in order to maintain the system for a 20-mile tieback must be considered against the risk of a hydrate plug occurring (once or multiple times).

How is a model like CSMPlug different from what we just did with first principle calculations?

The impact of these differences can be significant. CSMPlug will produce:

· Slower early-stage dissociation (based on internal gradients)

· Nonlinear time evolution

· Self-cooling effects

· Interface motion

The main deviations come down to three main sources:

Internal thermal gradient – In reality a hydrate plug, much like an ice cube in your Manhatten, will melt from the outside inward. While the surface warms relatively quickly, the core remains cold. In our simplified example, the equations assume a uniform plug temperature, constant heat flux, and no moving boundary resistance. There really is an internal thermal gradient where in the early stages the surface warms quickly while the core remains cold. This assumption produces an error ranging + 10 – 25% for the dissociation time.

Self-cooling during depressurization – During a pressure drop, the surface temperature drops, therefore DT shrinks and heat flux decreases. A lumped model assumes a constant DT. This produces a larger error – typically ranging + 20 – 50%.

Variable heat transfer coefficient – As the plug shrinks, the surface area decreases and the contact resistance changes. Our simplified calculations assumed a constant area. This difference accounts for +/- 10 – 20% error.

Long plug lengths – For very long plugs, pressure gradients matter more and dissociation may start at the ends. Lastly, melt rates are not uniform. A simplified model will under or over predict the dissociation time by up to 2x in some cases.

The first principle calculations tend to break down when considering hydrate plugs that form in subsea tiebacks because:

· Strong depressurization (large subcooling)

· Thick insulation

· Very low external DT

· Thick plugs (> 0.2 m radius with low conductivity)

· Partial dissociation with trapped gas pockets

In our DEH example, we estimated 45.6 hrs (1.9 days), whereas incorporating a full Stefan solutions (solid melting to a liquid) will give a solution ranging 52 - 62 hrs (+15 - 35%).

Bonus Topic: Chemical Options

Methanol and MEG (mono ethylene glycol) are often used to prevent hydrates from forming as a thermodynamic inhibitor (THI), and they both can be used to dissociate hydrate plugs. Without going into the thermodynamics too much, a safe approximation for the volume of methanol required for hydrate equilibrium temperature suppression is ~ 0.8°C / wt% MeOH and ~0.3°C / wt% MEG. The concentration component is an important factor to consider because free water is released from the hydrate as it dissociates, which dilutes the inhibitor. In practice this means THI must be continually fed into the flow line to fully dissociate the hydrate. This also means there must be communication across the hydrate plug or else the injection side of the line will keep increasing in pressure and the amount of THI will be limited (or the line will burst!).

In 2024, Koh et al. reported a model to describe a two-step mechanism to dissociate a hydrate plug. In the first step, heat is applied to begin the hydrate dissociation process and create annulus spacing between the pipeline wall and the hydrate plug (images 1 - 3 below; this is critical as one needs to establish communication across the plug). In the second step (images 4 - 6), THI is delivered to one side of the hydrate to accelerate the dissociation of the hydrate.

In addition to heat, the annulus spacing can be established through one-sided depressurization. The second step is dependent on the THI concentration gradient. The model calculates the accumulation of water and THI over time based on the dissociation. The model also takes into account the transient axial convection of the THI flow rate and associated thermodynamic changes caused by THI concentration changes. Other considerations include the porosity and permeability of the hydrate plug, heat transfer coefficient, hydrate plug length, and annulus spacing.

When applied to the field, the model predicted a hydrate dissociation time of 1 - 2 days, depending on THI flow rates, hydrate porosity assumptions, and time to create an annulus spacing (shown below).

In practice, the field was able to utilize multiple techniques to minimize safety risks and reduce the downtime to a minimum level. The super interesting point of this example is a model was developed to predict what is possible, yet unknown variables still existed: plug length, porosity, and location to name a few. The model validation still worked and by incorporating actual field data the remediation times provide valuable options to get the hydrate safely melted and production up and running.

In other words, the best solutions (in our humble opinions!) come about when combining scientific theory (what’s possible) with actual limitations / real world scenarios (what’s practical).

Subscribe now

My wife and I are deep in the “pickup and drop-off” days of parenting (IYKYK). The big one each day is the afternoon pickup. We’re lucky to have grandma help several days a week, but when it falls to me or my wife, there’s one big decision:

My wife’s answer:

“Get there 30 minutes early and be one of the first to get our daughter.”

My answer:

“Get there closer to actual pickup so I minimize the time commitment.”

Now, I really don’t want my daughter to be the last one picked up (too many days in a row of that and you start getting looks from the teachers). But I’m also generally very busy and want to avoid a big break in my afternoon if I can.

After enough firsthand pickup-line experiences at my daughter’s school, I’ve built a decent intuition for how to minimize my wait and that’s how I came to my answer above. But the data scientist in me is curious to try and prove it with statistical simulation and modeling.

Simulation Approach and Key Assumptions

To try and settle this, I wanted to build a model of the pickup process that enables me to simulate thousands of pickup days and see what happens on average. Below are some of the details of the model as well as its key assumptions.

The Model

What I’m looking to model would be classified as a discrete-event simulation. Within that, the pickup queue is modeled as an M/G/k queue (a common type of queue process):

• Cars arrive randomly following a non-homogeneous Poisson process — meaning they don’t all show up at once, but cluster around the bell in a bell-shaped pattern (no pun intended). The peak arrival rate actually hits about 5 minutes before the official pickup time.

• Service times are random too meaning how long each car takes to pull up, load a kid, and pull away follows a log-normal distribution (most cars are quick, but every now and then someone needs to track down a missing lunchbox, I’ve even seen a shoe fall off).

• Multiple loading spots operate in parallel meaning just like the real curb, several cars can load at the same time in a first-in, first-out order.

I then ran 3,000 Monte Carlo simulations — each one representing a full school day — to build up a statistical picture of wait times across every possible arrival time.

Key Assumptions

• ~240 cars arrive per pickup session (roughly 60 kids per grade after accounting for after school activities for some of them × 4 grades)

• 4 parallel loading spots at the curb

• Average service time of 30 seconds per car with a standard deviation of 15 seconds, including pull-up, load, and pull-away (our school seems pretty efficient)

• Arrival peak is 5 minutes before the bell, with ~68% of all cars arriving within ±13 minutes of that peak

• The simulation window spans from 40 minutes before to 30 minutes after the bell (picking your kid up 30 minutes after the bell would be pretty bad)

• Cars are served first-come, first-served — no cutting, no VIP lane for parents who “just need to grab one kid real quick”

• Each simulation day is independent — no memory of yesterday’s chaos

Results

We’ll start first with car arrival distribution which shows when most cars tend to arrive. A quick sense check confirms this is reasonable. Most parents tend to arrive before the bell gets out, which is why we see the peak before +0. Having said that, there are still some arrivals after the bell gets out too (like me).

Next is what the queue (carpool line) looks like over time, factoring in the loading of kids once we reach +0. There’s always someone who shows up as soon as the doors open (~40 minutes before pickup). The line builds until it peaks right around +0 when kids start getting loaded. From there, cars start leaving the queue, but a few stragglers (again, like me) are still arriving so it stays somewhat flat before dropping off.

Finally for the most important part, combining the two pieces of information above, we can get a sense of the total time you spend getting your kid, depending on when you arrive. We can also look at this in distribution form for an early arriver vs. later arriver further below.

What This Means for the “Arrive Late” Strategy

The data tells an interesting story:

Arriving early is the worst strategy for total time spent (sorry honey). If you show up 30 minutes before the bell you’re sitting in line for over 31 minutes on average. Yes, you picked up your kid almost first, but you waited at least 30 minutes up front to be able to do that.

The wait time begins to taper off right when the bell goes off to ~20 minutes. This is when the queue is at its maximum point, but you’re only waiting for the cars in front to pick up their kids because pick up has already started.

The magic spot: if you wait it out and arrive 10 minutes after the bell, your average wait drops to just 16 minutes which is shorter than someone who arrived 20 minutes early.

Wrapping Up

You’ll notice that arriving 20 or even 30 minutes after the bell means you can almost pull right up and grab your kid. That makes sense intuitively, but I choose to live by the “happy wife = happy life” motto and 10 minutes after the bell is about as far as I’ll push it.

P.S. Other Scenarios are Possible Here

What I showed above, was custom tailored to my daughter’s school and from my sense checks on it, it seems to match. But if we increase the number of kids to be picked up, or we change how long it takes to pick someone up, or any of the other parameters, we can end up with quite different results. And in particular, ones where you would want to get there extra early if you can.

Here’s an example of loading 400 cars (vs. 240) and a slightly longer pickup time per kid. In this case, there’s a sweet spot about 20 minutes before pickup starts.

While Pontem works on the premise “Prevention is always better than the Cure” we also have excellent experience in the Hydrate Remediation process. In our latest post we share some fundamental processes and tools required to safely locate and remove a hydrate plug.

The “safely” part is super important because there are numerous examples of where hydrate plugs have become dislodged and caused catastrophic damage to flowlines, pipelines and equipment causing serious injury sometimes and in very rare occasions loss of life.

The removal of a hydrate plug is so dangerous because if you dislodge a heavy, compact hydrate plug with enough pressure behind it these can travel at speeds up to 200- 500 miles/hr. In one example, we modeled depressurization procedures involving a 200’ hydrate and found one option to be particularly disastrous for the subsea infrastructure.

Hydrate plugs come in all shapes and sizes. Below is a picture of compacted hydrates taken from a lab test with 100ml of fluids. Just imagine what a 1000 lbs hydrate plug would look like.

Unsurprisingly it can be devastating to whatever and whoever it runs into. The cartoon graphics below give a hint of the damage it can do.

This shows why it is so important to consider all the safety implications when managing the hydrate remediation process.

The aim now is to share with you the hands-on skills needed to safely remove a hydrate plug. For this post we will focus on:

1. How to locate the hydrate plug

2. Dissociate and remove the plug safely

3. Then restart the system without forming another hydrate plug.

While not all encompassing, this post should prompt enough questions to begin discussing the most appropriate next steps to get your system back online!

Subsea Hydrate Blockage Location Detection Technologies

How the hydrate plug is detected is very much down to the length of the line and whether you can easily access the outside of the pipeline. Accordingly, a topside pipeline is the easiest, whereas buried onshore and subsea pipelines are far more difficult.

It is also worth saying that no single plug locator method is perfect, a combination of approaches is often recommended to achieve precise results.

Recent advancements have introduced non-invasive tools specifically for deepwater flowlines where access is limited. The table below summarizes some of the available detection techniques.

Acoustic Pulse Detection Method - Image courtesy of Find-Block® Paradigm Group

Pontem are in discussions with a supplier of one of the acoustic detection tools to combine our modelling expertise in the use of physics based models to predict hydrate plug locations.

Hydrate Blockage Remediation Techniques

There are several different techniques that can be used to remove hydrate blockages but the most commonly used are:

Thermodynamic Hydrate Inhibitor (THI) Injection

THI’s such as Methanol, MEG and TEG are used to melt the hydrate. Most facilities will have access to at least one of the chemicals, if not two.

THI’s work by altering the freeze point of hydrates forcing it to melt at the system pressure and temperature.

For a THI to work it must actually contact the hydrate plug, which is not always possible. There is almost always fluids between the plug and either end of the flow line, which makes THI placement a non-trivial exercise. Additionally, it is not recommended to use Methanol to melt a hydrate plug in a vertical pipe as Methanol will sit on top of the free water that is released as the hydrate starts melting due to it being lighter than water. This barrier stops the melting process.

Depressurization

If the injection of a THI has failed to remove the hydrate plug, the next option is to depressurize the pipeline to move the system outside the hydrate envelope. When it comes to depressurization there are two possible options:

• Dual sided depressurization - The most common and preferred method used is two-sided depressurization because it minimizes the chance for a large pressure differences across the hydrate plug occurring, thus preventing the hydrate plug becoming a high velocity projectile.

• Single sided depressurization - Usually not recommended as you run a greater risk of having a high pressure differential across the hydrate plug (i.e. >10 bar / 145 psi). However, there are instances where single sided depressurization has been used successfully but it needs to be done in a controlled manner and requires a thorough risk assessment before it is performed.

Impact of multiple plugs in a pipeline

In the case where multiple plugs have formed in the pipeline, extra caution needs to be taken even with two-sided depressurization. This is because in the case of multiple plugs, high pressure gas may still be trapped between two impermeable plugs so unbeknown to the operator there may be a significant dP across one of the plugs causing it to become a projectile if loosened due to the depressurization process.

Cooling during Depressurization

One of the draw backs of using depressurization to dissociate a hydrate plug is that sub-zero temperatures can be generated. This is manageable in uninsulated flowlines as the ambient temperatures warm up the system within hrs/days allowing the plugs to melt. However, many deepwater oilfields use highly insulated flowlines (e.g. U-values <10 W/m²K), which prevent the internally generated cold temperatures from being dissipated prolonging the hydrate dissociation process. This may mean that the ambient temperatures take weeks / months to move the system outside the hydrate region.

In flowlines that sit in sub-zero seawater temperatures this problem is exacerbated as it can lead to ice formation. This is a problem because no amount of depressurization will remove an ice plug. The only way to remove it is to somehow add some external heat to the pipeline, which we will talk about more later in this post.

All of this hopefully shows the complex thermodynamic process that occurs during hydrate plug dissociation and what needs to be evaluated upfront.

Use of heat to warm up the pipe

There are a number of heating options that have either been tried or proposed. These are:

1. The Petrobras SGN method

2. Subsea Active Electrical Heating

3. Electric Blankets and Heat tracing for topsides piping

Petrobras SGN Method

If you are a chemistry geek this one is for you. SGN stands for Self-Generated Nitrogen. SGN is a patented, thermo-chemical technique primarily used by the Brazilian National Oil company, Petrobras to remove hydrate and organic (paraffin) plugs in subsea flowlines, trees, and wells. This method relies on an exothermic redox reaction between two specialized, nitrogen-containing salt solutions (referred to as “C” and “N” solutions) in the presence of a catalyst to generate a high enough temperature to melt the hydrate plug. This is delivered via Coiled Tubing (CT)

• Reaction: Ammonium chloride (NH₄Cl) and sodium nitrite (NaNO₂) react to produce nitrogen gas, sodium chloride, and water, releasing a significant amount of heat (∆H = -75kcal/mol).

Active Electrical Heating

Direct Electrical Heating (DEH): This is the most commonly used active heating method, which uses the steel pipe as an electrical resistor to generate heat. An AC current is fed via a cable to the far end of the wet insulated pipeline, where it connects to the pipe, causing current to flow back through the steel, generating heat through both Joule and skin effects. This heat then passes from the metal pipe wall into the fluid in the pipe. A drawback of this method is that the heating process is not always uniform due to the resistive properties of each section of the pipe being slightly different.

Electrically Trace Heated Pipe-in-Pipe (ETH-PiP): This works by using up to 3-phase trace heating cables strapped to the outside of the inner pipe of the P-i-P set-up and within the insulation layer of a flowline. Powered by subsea transformers, these cables generate heat via the Joule effect, which involves passing an electric current through the heat trace cables, generating heat that warms the inner production pipe and fluids to above the hydrate dissociation temperature. The copper cable achieves much better temperature control compared to DEH because the heat generated is consist throughout the length of the cable.

Subsea Hydrate Remediation Skids

When it comes to hydrate blockage removal, time is money because as long as the hydrate plug is there no production can happen. This is why the use of Active Electrical Heating is so useful. You can virtually flick a switch and the hydrate plug will start melting.

However not every field can afford active electrical heating and in a lot of instances the use of THI and/or depressurization just doesn’t work. This has driven the subsea service provider community to develop Hydrate Remediation Skids or Composite Coiled Tubing units, which attach to deepwater flow lines to help remove hydrate blockages quickly.

Hydrate Remediation Skids for System Depressurization

These are designed to connect to subsea manifolds and/or Production XTrees via a ROV to allow for quick dual sided depressurization of the system to <10bar and to inject THI into the system. Below are Hydrate Remediation Skids supplied by Subsea7 and Oceaneering that have been developed for this purpose. There are other providers of this type of skid as well.

Composite Coiled Tubing Units

There are a number of specialized coiled tubing technologies that use composite coiled tubing and specially designed heads to transport the coiled tubing along subsea pipelines. The lightweight and more flexible nature of the composite tubing allows the technology to do things the traditional stainless steel coil tubing can’t. Like reaching up to 20kms along subsea pipelines and being able to navigate through multiple 5D bends.

The composite coiled tubing can be inserted directly into the outlet of the subsea riser on the topsides process or via multiple subsea intervention access points (i.e. 2” ‘hot-stab’s) on subsea equipment. After insertion, the coiled tubing can be used in multiple ways to remediate hydrates. These are:

• To dose THI directly onto the hydrate blockage. The coiled tubing is fed down the riser or via the intervention points in the subsea equipment to the immediate vicinity of the blockage and THI is injected directly onto the hydrate.

• To inject hot fluids (e.g. diesel) directly onto the hydrate formation for thermal dissociation using the same methods described above.

• To inject gas to the base of the riser to unload the liquids, and remove the subsequent static head, from the system, attaining hydrate free pressures subsea. This is only required if the system cannot be depressurized via the usual flare path due to high liquid hold-up in the riser.

Hydrate Remediation Successes

As with everything it is important to know that there are effective solutions out there with respect to getting rid of hydrates. Below is a small list of successful cases where hydrate blockages were remediated successfully.

The above data demonstrates that most recorded hydrate remediation cases are gas or gas condensate systems and that the majority of the blockages occurred due to mal-operation or failure of the chemical injection and/or gas dehydration systems. Furthermore for uninsulated lines, hydrate plug removal took hours / days but for the only insulated line case included, hydrate plug removal increased to days / weeks. This corroborates the description previously supplied regarding what happens when trying to dissociate a hydrate plug in an insulated line.

The Hydrate Plug is gone so what to do next

Finally, there is no point in successfully removing the hydrate blockage without preparing the system properly for restart. Otherwise, you will probably just re-block the line.

To get the system up and running again Pontem recommend the following:

1. Remove the uninhibited water that caused the hydrate plug in the first place. This can be done by running a low-pressure pig at this point to remove the uninhibited fluids.

2. If you can’t run a pig, flood the system with as much Methanol or MEG as possible, with one eye on the costs and the other on how the aqueous phase will be disposed off once it comes out of the pipeline.

3. Once you think you have eliminated/inhibited the water in the line, make sure the chemical injection system or gas dehydration system is back up and running.

4. For the systems that normally operate outside the hydrate region during steady state flow, it is recommended to restart the system at as low a pressure as possible after dosing with Methanol or MEG until the flowing temperatures are above the hydrate dissociation temperature.

If you follow those points you will give yourself every chance to get your system up and running again but unfortunately there are never any guarantees in this game.

We hope this post has improved your understanding of the Hydrate Remediation process and as ever there is always more to these things than you think. It is not until you experience having to do this that the gravity of the process sinks in. This is where Pontem can help because we have that experience and are more than happy to share it so don’t be shy, contact us.

Subscribe now

Background

Pigging offshore pipelines is expensive, disruptive, and not without risk. A single pig run can cost hundreds of thousands of dollars, require careful operational coordination, and — at low flow velocities — introduce a very real risk of stalled or stuck pigs.

So when production declines in a late-life asset, a familiar question comes back into focus: Can we safely do this less often?

For this study we were involved in, the motivation was clear. The pipeline had been operating at low rates for years, pigging returns showed consistently small wax volumes, and yet pigging was still being carried out on a relatively frequent basis. Reducing pigging frequency would deliver immediate cost and risk reduction — if it could be done with confidence.

But there was a catch.

The fluids in the line were no longer the same as in previous years. Production was increasingly dominated by different wells, with different (higher) wax risks. Even at low rates, the concern was simple and valid:

Are we still seeing low wax because the system is benign — or just because we haven’t been caught out yet?

Answering that question — properly — was the real objective of the study.

What the Historical Data Did (and Didn’t) Say

Looking back at pigging history, the headline story appeared reassuring.

Over the last several years, pig returns consistently showed around 1 kg of wax or less per run, even with long intervals between pigs. On the surface, that trend alone might prompt someone to say “what’s the problem here?”.

However, relying on history alone masked two important uncertainties:

• Earlier pigging data reflected different production mixes, not the fluids currently dominating the line

• Historical low wax recovery did not necessarily bound what could happen next, particularly if waxier streams became more influential

In other words, the question was not “Has wax been low?”
It was “Does the historical behaviour still apply to today’s fluids and tomorrow’s operations?”

That distinction is what pushed this study beyond a simple trend review.

Letting the Data Define the Problem

Rather than starting with a model and asking it to predict behaviour, the workflow deliberately started with data aggregation and reconciliation across numerous sources, something we do often at Pontem.

In this specific case, it included:

• Daily averaged pressures, temperatures, and flowrates

• Detailed pig launch, receipt, and wax recovery records

• Laboratory wax appearance temperatures and heavy-end content

• Well-by-well production contributions over time

Only once the data was pieced together and told a consistent story was modelling used — not to invent outcomes, but to stress-test what the data implied under current and future conditions.

Turning Data into Bounded Predictions

A transient multiphase flow and wax deposition model was then used as an interpretive tool.

Instead of focusing on internal modelling parameters, results were framed in terms of expected and conservative outcomes — effectively P10 and P90 estimates — bounded by what had already been observed in the field.

The calibration logic was straightforward:

• Higher historical wax recovery cases were used to define conservative behaviour

• Recent low-wax pigging cycles anchored the expected case

• Historical fluid blending based on waxy and non-waxy wells was taken into account

• Predictions were only accepted if they remained consistent with above

This approach ensured the model stayed tethered to reality, rather than drifting into theoretical extremes.

What the Results Showed

Across current and forecasted production conditions, both the data trends and the calibrated simulations told the same story:

• Wax accumulation rates remained very low

• Predicted wax layers were fractions of a millimetre thick, even over long durations

• Resulting internal diameter reduction was negligible, with no meaningful impact on hydraulics or operability

Low flow velocity was recognised as a potential risk, but analysis showed it to be acceptable for this system. Predicted wax layers remained extremely thin, with no meaningful impact on hydraulics or blockage risk, and pigging could still be executed safely when required.

Crucially, the results did not rely on optimistic assumptions. Conservative scenarios still showed wax behaviour that was slow, stable, and non-threatening.

The Moment of Truth: Field Validation

The strongest test came at the end of the year, when the line was pigged under the very conditions the study sought to assess.

Approximately 3 kg of wax slurry was recovered, which was 3 times more than what any of the last 5 years data showed. The pig showed no damage, and the wax itself was soft and mobile — entirely consistent with expectations.

This matched closely with the predicted wax accumulation P10/P90 range for that operating window.

That alignment between prediction and physical recovery was the most important outcome of the work. It demonstrated that:

• The predictions were realistic

• The system behaviour was understood

• Future decisions could be made with confidence rather than caution

Conclusions

With data trends, calibrated modelling, and field evidence all pointing in the same direction, the conclusion was clear: routine quarterly pigging was no longer technically justified.

The recommendation was to extend pigging frequency to once per year, with the option to reduce further if future runs continue to show minimal wax recovery. Pigging was reframed as something done for inspection readiness, not routine wax control.

As previously mentioned, the economic and operational implications are significant. Reducing pigging frequency from quarterly to annual delivers material cost reduction and, just as importantly, reduces operational exposure. Fewer runs mean:

• Less exposure to pigging at marginal velocities

• Lower risk of stuck pigs and recovery operations

• Reduced operational complexity offshore

This case matters because wax risk does not scale neatly with declining flowrate, and legacy practices can persist long after their original justification has faded. By grounding decisions in data, evidence, and validated predictions, it is possible to move from conservative habit to confident optimisation — and in some cases, the safest option is not another pig run, but a better understanding of what’s actually happening inside the pipe.

The most honest theme for us is something much simpler: show up

As Pontem has grown these last 1000+ days, there is a lot to reflect on. While there are definitely highlights, the trip is best summarized in the aggregate. Taking small - sometimes unnoticeable, always intentional - steps to build the company we want to work for (and the people we want to work with).

This is article #167 in the series. By consistently showing up, we “have a stack of undeniable proof that we are who we say we are” (thanks Hormozi). And while showing up applies to everything we do, one place it is visible is in these articles, where we make a point to get everyone to contribute with regular cadence. That’s once/week consistently for the last 3 years that someone at Pontem has “showed up”, put effort in, kept their word, and delivered on what they were asked to do (and we didn’t even make a “67” joke, because that’s so last year…)

So in honor of our 3rd birthday, we thought we would reflect back on a few of our Greatest Hits.

Top of the Pops: Most Popular Topics

We have covered a range of topics, some technical and some editorial, but we can reliably count on three main themes if we want to drive clicks. Any guesses? Our top 3 topics (measured by views) have been:

• Hydrates

• OLGA

• Sports Analytics

Especially if we can find a good picture, our audience always seems to show up for thought-provoking and thirsty pictures of hydrates. Lucky for us, we have some of the most well-respected hydrate experts in the world under our umbrella and have collaborated with others (academia / industry) frequently in the past. Keep your eyes open for more - gotta give the people what they want and play the hits sometimes.

Chart Toppers: Most Viewed Posts

Most major sports have specific numerical milestones (i.e. 500 Home Run Club, sub-4 minute mile, etc.) that guarantee induction into their Hall of Fame. The best of the best. We are no exception, as we have a short-list of top-performing posts inducted into ours. In order, here are our most popular posts since inception.

Congratulations to Stephen Hamilton for having the #1 read post in company history. He truly is Mr. Social Media.

Deep Tracks: Down the Rabbit Hole

Not surprising, the list of topics has grown considerably since our 1st birthday recap. In pulling this together, it was fun to go back and re-read some of our earlier work, as a lot has happened in the world these last 3 years. We were also reminded that Pontem’s founding date coincided closely with another startup you might have heard of: ChatGPT.

It’s fair to look at many companies operating today as BC (Before ChatGPT) and AC, as the way we process data, look at tech stacks, and build companies has been forever altered. Launching right on the cusp of this offers a unique perspective. In fact, we even opined on this early with our thoughts on ChatGPT, if you want to see if we hit the mark:

Aside from that, poking around our Substack reveals thoughts on Machine Learning, NBA gambling outcomes, controlling OPEX in low commodity price environment, and various other good reads - many of which were written before the AI craze went ballistic - so interesting to see some of these data-driven feral ideologies play out. We will share some of the deep tracks throughout 2026 that you might have missed when they were originally released. It’s fine, much like Taylor Swift, we own all our masters, so we can reshare.

So, if you keep reading, we will keep showing up and writing (hopefully interesting stories). And while you are down the rabbit hole, stop, click around, and read a few of the classics…

Subscribe now

At Pontem, we are getting excited for the upcoming Winter Olympic Games, hence the catchy subtitle to talk about creating a smooth sampling and analysis program.

At its core, a good analytical program will provide enough leading and lagging indicators to know if a system is operating efficiently and when it is going off track. The trick, of course, is for the data to be useful and accurate, and while it sounds easy enough, the truth is collecting and running samples must be done “by the books” to avoid contamination or erroneous results. Here, our post explores some best practices to ensure your sampling and analysis program hits the bullseye.

All manned oil and gas facilities have a requirement to routinely sample and analyze the oil, gas and water to ensure that specifications are met to allow the hydrocarbon products to be exported for sale and the water to be discharged overboard. Failure to meet these specifications can result in fines, devalued product, or possibly outright rejection of the hydrocarbon product from the receiving company. Additionally, internal quality targets are often used to maintain asset integrity (i.e. corrosion and fouling) and/or maximize production rates. Failure to routinely sample and analyze the production fluids can significantly impact oil and gas production, hitting the bottom line of the operation.

In addition to the above, there will be process upsets that require numerous samples to be taken for analysis and troubleshooting to return the system to baseline. This can be a very important part of the sampling and analysis process because getting it right may mean the difference between producing and not producing hydrocarbons. Taking on deferred production due to unplanned shutdowns is definitely not “Cool Runnings”. See what we did there?! 😊

The ideal scenario is one in which samples are collected, analyzed, and results are in hand in a matter of minutes to hours. This requires the ability to do the majority of the analyses required on-site. To do this effectively means you need a lab. Sometimes it can be a bit of a squeeze to fit a decent lab onto offshore facilities but not quite as bad as this:

Correspondingly, the size of the lab limits what you can do and the equipment you can fit into the lab, but you still have a wide range of analyses to perform. Large footprint analyzers, rare or difficult to source solvents or disposables, and highly specialized training are all things that will limit the applicability offshore. We will go into exactly what type of analyses are needed later in this post.

So, with this limitation, it is unlikely that there will be much state-of-the-art equipment and sometimes you do feel like you are performing “bucket chemistry.”

The good news is that with the development in portable chemical analytical equipment there is still a lot that can be done and to a reasonable accuracy.

Taking that into account we will now go into a bit more detail on what analysis you need to do for oil, gas and water streams.

Sampling and Analysis Requirements

This process should be started during the design phase where the sample locations are initially identified. This process needs to make sure of the following:

1. The sample point location and orientation is set correctly in the pipe or vessel. It is critical to understand what type of sample is being pulled at the sample point location so it can be spatially placed correctly. This includes understanding of the flow regime and/or how the vessel is configured. Nothing worse than getting water when you wanted oil! This includes placing the sample point at the 12 o’clock vs 6 o’clock position. Or placing the sample point near the oil outlet or water outlet for a vessel.

2. The sample point is accessible. In many instances, sample points have been located where they can’t be reached or that they are in such a small space you can’t get a sample bottle to fit underneath them.

3. Is the sample point in a horizontal or vertical pipe? This will make a difference on what type of sample you get as gravity will play a part in what type of flow regime you will have, which in turn will affect the phase that you will sample depending on where the sample point is located.

Acceptable and Not acceptable Oily Water sampling points

The pictures below also give examples of where to locate sample points in vertical and horizontal pipes

Safety First

Taking oil and gas samples can be a hazardous process and needs to be a managed accordingly. With sometimes having to break into high pressure and temperature systems. This may result in the sampling process requiring a work permit / risk assessment or as a minimum be supervised by an Operator. Things to look out for are:

1. Venting of hydrocarbons
   This may cause small volumes of gas to be released, so this gas needs to be vented safely.

2. Adequate training for the person taking the sample
   The person taking the samples must know the HSE risks and follow properly established protocol to remove or minimize risks to a manageable level.

3. Sample point integrity
   Sample points should have double valve isolation and bleed valve assembly. i.e. double block and bleed with ideally a pressure gauge.

   

4. Personal Safety Requirements
   This involves the use of safety glasses/goggles/face shield, hard hat, appropriate gloves, safety shoes, clothing and breathing apparatus if gas samples with high levels of H₂S are to be sampled.

5. Impact of Static Electricity
   Static electricity is a risk during sampling. Appropriate earthing procedures should be carried out. Refer to ASTM D4865-09 Standard Guide for Generation and Dissipation of Static Electricity in Petroleum Fuel Systems for details.

6. Correct Sample Labelling and Sample Containers
   For transportation it is important to make sure any sample taken is labelled correctly and is in the right size and type of container for the transportation method used. Proper labelling in the US, for example, includes DOT hazard diamonds with symbols and numbers, alongside GHS pictograms, signal words, and statements.

7. Radioactivity of the Sample
   For most liquid and gas samples this is not an issue due to the low levels of radioactivity present but if a solid sample has been taken, it is important to understand whether the solid contains NORM (Naturally Occurring Radioactive material).

As well as taking samples safely, you also need to take them correctly. There are good and bad ways to take a sample. Ideally you want to make sure:

• The sample bottle is compatible with the sample to be taken. E.g. oil samples should be taken in lined metal cans or glass bottles and not plastic containers as the oil can react with the plastic container depending on the type of plastic used. (We’re looking at all you milk carton users!!).

  

• The sample bottle is clean and uncontaminated.

• The sample line from the sample point to the sample container is adequately flushed to an appropriate drain or waste container beforehand to ensure a homogenous sample is obtained.

I once went offshore to survey several unmanned and manned platforms in the North Sea. The operator had reported some stubbornly high OIW figures on a couple of platforms. On one of the manned platforms, I went out on the plant with the operator, who normally took the water samples for OIW analysis, and got him to take a sample as he normally would. Straight away he took the clean sample bottle, rinsed it out with the first water that came out of the sample point and then disposed of that water. Once that was done, he started taking the actual sample to be analyzed.

I immediately realized why the OIW levels were higher than they should be. This was due to the operator washing the clean sample bottle with the sample line flush. This water wash step left a film of oil on the inside of the bottle even though it was emptied out prior to taking the sample. The analyzed water sample produced an OIW value that was a combination of the fluids from the flush and the actual water sampled. It turned out this increased the OIW value by 20-30%. The corrective action was to flush the line into a suitable disposal bucket or waste stream and then collect the water sample with the clean, previously unused collection bottle. The OIW values immediately returned within required specifications.

So what samples should you take

Once you know where and how the sample will be taken, it is important to understand why you want to take the sample in the first place. Most samples are taken to verify that the oil, gas, and water are within specifications. It is important to note this often requires more than one analysis per fluid, as several parameters need to be monitored. The following sections give an indication of what samples should be taken and what analysis to perform.

Oil Samples

The main variables requiring monitoring in crude oil producing operations are:

With respect to sand, measuring the amount of sand being produced is a lot more difficult than you would think. It needs a specialized sand sampling system. See the picture below:

Samples of sand need to be taken from sand removal systems to measure the amount of entrained oil before the sand is discharged overboard. There is a risk for oil sheens if oil levels on the discharged sand are too high similar to the one in the picture below.

Gas Samples

As with oil, there is a lot of effort put into monitoring the quality of the gas to make sure it is suitable for export. To get the right gas specification there may be various gas treatment systems being used such as:

1. Gas dehydration

2. Acid gas removal (i.e. CO₂ and H₂S)

3. Condensate dew-pointing

4. Mercury removal

To ensure these gas processes are working as effectively as they should be, gas samples need to be taken and analyzed at regular intervals. This may involve the use of online sampling and analysis systems.

Even with online systems being in place, there is a need for manual sampling and analyses to be performed offline to validate the performance of the online systems.

Operations that are relatively simple and/or where plant upsets are infrequent or low impact may require only periodic sampling and analysis of the gas streams to be performed. For example, this may mean only a simple chromatographic analysis to C8 and/or a dew point measurement is sufficient.

Where more in-depth analysis is required, gas samples may be further tested for the following gas properties:

• Calorific value

• Relative density

• Hydrocarbon and water dew points

• Mercury

• Concentrations of sulphur compounds

• BTEX

• Inerts

As well as the above gas properties periodic monitoring of the glycol used for gas dehydration or hydrate inhibition may also be required.

Finally, some of the above gas properties can be used for monitoring the gas process performance or for quality control of the gas being exported. These properties are used as key features in the quality specifications attached to gas sales agreements, so any analysis needs a high degree of accuracy.

Water Samples

Although water is not a revenue generator, there are multiple system health checks dependent on proper and routine water analyses. These are:

It is worth noting that when it comes to water, the environmental analysis requirements are based on where the water is being discharged. If it is offshore (>12 miles from shore), well away from the coast line, then the analysis requirements are much less onerous compared to water discharged onshore, in a river/lake or near-shore (<12 miles from shore). This is because the impact the discharged water will have on humans and the indigenous flora and fauna is likely to be negligible for offshore scenarios thus requiring less tests. However, when the discharge is onshore, near-shore and in rivers or lakes then you will likely need to analyze the composition of the water far more closely. You can see these differences in the lists below .

Environmental requirements - offshore

• Total Oil and Grease (TOG) - aka Oil-in-Water (OIW)
  Depending on the global location that can just be measuring the dispersed (insoluble) oil in the water or the total hydrocarbon content of the water that includes both the dispersed and water-soluble hydrocarbons

• Monitoring “grey water” / sewage discharges offshore

Environmental requirements – onshore / near-shore

• Total Oil and Grease (TOG) - aka Oil-in-Water (OIW)

• Total Suspended Solids (TSS)

• Total Dissolved Solids (TDS)

• Total Organic Carbon (TOC) and/or Chemical Oxygen Demand (COD)

• Total Nitrogen

• Total Phosphorous

• Escherichia (MPN/100ml)

• Faecal enterococci (MPN/100ml)

• pH

• True colour (Wavelength/ Maximum spectral absorption coefficient)

• Acute toxicity (UT)

It is worth noting that the above list of tests are some of the most common tests that need to get performed. Depending on where you are in the world will determine how many of them you will need to do.

Process Performance requirements

• Total Oil and Grease (TOG) - aka Oil-in-Water (OIW)

• TSS

• Bacteria ATP

• Bacteria qPCR

• CI Residuals

• SI Residuals

• Ca/Mg ratio Mn²⁺ and Fe²⁺ conc.

• pH

• Water Composition

The above analyses are the requirements for the produced water when managing things like corrosion, scaling and water quality.

If you are also using seawater injection into the reservoir to help enhance production, then there is a whole new set of analyses that need to be performed to ensure the seawater is the right quality for injection. Specifically, the seawater injection stream should be tested for:

• Dissolved Oxygen

• Free and Total Chlorine

• TSS

• Water Quality

• SO₄^2- (this is particularly important if sulfates are a concern and/or a Sulfate Reducing Unit (SRU) is in place)

These are the sampling and analysis requirements for the main production and injection streams but there are a number of other parts of the process that need to be sampled on a frequent basis. These include but are not limited to:

• Off Spec Oil Tanks

• Fuel Gas

• Diesel Storage Tanks

• Closed Loop Cooling Medium

• Closed Loop Heating Medium

• Lean TEG

• Rich TEG

• Fresh Water Generators

• Slop Oil Tanks

• Slops and Bilge Water

• Subsea Chemicals

• Subsea Hydraulic Control Fluid

• Sewage Treatment Package

As you can see most oil and gas facilities need to take and analyze large numbers of samples just on a routine basis. This increases further when something unexpectedly goes wrong and may even start to include solid samples but how to deal with solid samples is probably another Substack post in itself.

Sample Data Management – It’s our Thing!!

As we just described, there are a multitude of oil, gas and water samples that need to be taken on a routine daily, weekly or monthly basis. As well as the manual samples there are online analyzers that you can add to the system that will analyze in real time things such as

• BS&W

• OIW

• Water content of gas

• O₂ content of injected seawater

• Corrosion

• Particle counts

Even when you have online analyzers, manual samples are still required to ensure the analyzers remain calibrated.

When you have this instrumentation and its combined with the manual sampling and analysis data this provides integrated data feedback with the data generated directly via the analyzers and instrumentation, which is then used to maximize the efficiency of the overall system by managing the data generated.

Doing something useful with the data to improve processes is really the point to all the hard work of sampling and analyzing properly. The following two examples highlight the difference between collecting samples in a single point in time to develop a risk analysis compared to a monitoring system that delivers trends and actionable insights over time for an entire system. The table below shows 7 risks identified from sampling a Permian system. Framing those risks in a likelihood and consequence (or level of urgency) matrix provides a framework for creating an effective mitigation or risk removal program. Corrosion and souring risks are identified and a path forward is recommended, however it will be up to the field to maintain these programs and to set threshold levels for a monitoring program.

In contrast, online dashboards that trend systems continuously are able to identify risks as fluid properties change. In the example below, 7 wells are monitored on a continuous basis with plots of P/T/chemical injection rates highlighted on a dashboard, along with thermodynamic risks for asphaltene instability and a simulated deposition rate. The system provides real-time feedback, and in the case of W-BF-2 well the dashboard is recommending a 25ppm increase in AI to reduce the risk from medium to low and examine BH pressure data to recalibrate the system.

Yeah, But I Don’t Have Time For All That…

We get it: sampling, analyzing, and processing all that data can feel daunting and cumbersome. And it can often appear that a system is running smoothy. Going through the motions without a real understanding of what is truly happening will eventually bite you. The US bobsled team just found this out at the World Cup Bobsled event while tuning up for the Winter Games. Good timing for our post…bad timing for our Olympic hopefuls!

Without those leading and lagging indicators that will crop up in routine sample analyses, bigger problems will cause massive issues, making it more difficult and more expensive to right the ship.

The good news is you don’t have to go it alone. Partnering with Pontem Analytics is a sure fire way to set up and establish a robust testing regime that includes all the metrics you will need without the ones you don’t. Reach out and start the conversation. We’re happy to help.

Thanks for reading Data and Discipline! Subscribe for free to receive new posts from Pontem Analytics.

Last year, we shared round 1 of Pontem x SPE, which was a piece showing our longstanding involvement with SPE and a few recent announcements at that time. If you missed it, you can read that piece here:

At Pontem Analytics, we rely on the same spirit that SPE was built on: sharing hard-won lessons, staying current on the best technical thinking in the industry, and helping the next generation of engineers grow into confident professionals.

That is why we are excited to share two more updates that deepen Pontem’s involvement with SPE—one in the United States and one in the Eastern Mediterranean.

Andrew Yule joins the SPE Fort Worth Section Board as Communications Chair

Andrew Yule has joined the SPE Fort Worth Section board as Chair of Communications and Outreach.

The Fort Worth Section is a highly active technical community, with a clear focus on:

• Strong member engagement through well-attended technical talks and events.

• High-impact technical programming, including the Distinguished Lecturer series that brings leading experts to local SPE sections around the world.

• Community-building events, including a December happy hour that keeps the section connected even outside the standard luncheon format.

• Meaningful student support, with scholarship funding that helps petroleum engineering students pursue their education.

In the communications role, Andrew’s focus will be to help make sure members hear about what is happening, why it matters, and how to get involved—whether that is attending a luncheon, supporting scholarships, volunteering for open board roles, or simply showing up and participating.

Kostas Poursanidis steps into Chairperson role at the SPE Cyprus Section

Kostas Poursanidis has been elected Chairperson of the SPE Cyprus Section, continuing his service in the Section’s leadership.

The Cyprus Section’s goals are centered on building and sustaining a unified petroleum engineering community in the country. In particular, the section exists to:

• Enable technical knowledge sharing within the Exploration and Production sector.

• Support professionals and students through learning, connection, and development opportunities.

• Operate as a non-profit, apolitical, and nonreligious professional community.

• Advance SPE’s broader mission to collect, disseminate, and exchange technical knowledge across the exploration, development, and production of oil and gas resources, for public benefit.

• Provide opportunities for professionals and students to enhance both technical and professional competency.

That combination of technical rigor and community support is exactly the kind of ecosystem that helps good engineers become great ones—and we are proud to see Kostas stepping into a leadership role that will help strengthen that ecosystem.

What this means for Pontem

These two board appointments reflect something we care about deeply: building organizations that keep the industry learning.

SPE is one of the few professional societies that consistently delivers real technical value at every career stage—from early-career engineers looking for mentorship and exposure, to seasoned practitioners who want a strong community and high-quality technical discourse.

We are looking forward to supporting both sections in practical ways—by showing up, contributing to technical discussions, helping promote events and knowledge-sharing, and supporting opportunities for students.

If you are based near Fort Worth or Cyprus (or you just want to get more involved in SPE), we would love to see you at an upcoming event.

Don’t be lulled into a false sense of security with respect to water samples just because water isn’t usually seen as a revenue stream. It doesn’t mean that you don’t need to make sure you have got the right water composition.

But right from the get go, it can be difficult sometimes persuading your drilling team the importance of taking a water sample because any sampling exercise takes time and that can mean a lot of money especially for deepwater wells in harsh environments. The whole sampling exercise could run into a few million $’s for a handful of water samples. Taking that into account be prepared to do a cost benefit analysis showing the drillers why the water samples need to be taken.

The reality of the situation is that the composition of the water can make or break an oil and/or gas development. So, with that in mind you want to make sure the water composition used for design purposes is correct. If you get it right this is what happens:

1. Identification of the most suitable chemical injection locations

2. Selection of the right materials

3. Determination of the most optimum treatment methods

4. Optimum MEG Reclamation Unit (MRU) design and hydrate management strategy

5. Identifying the correct baseline from which operations can begin trending data

If you don’t get the water composition right, there are a number of things that can go wrong. Such as:

1. Inorganic scaling (mainly associated with CaCO₃, BaSO₄ and Halite scale formation)

2. Corrosion

3. Calcium Naphthenates and Carboxylate Soaps

4. MEG reclamation unit fouling

5. Emulsions

Obtaining Clean water samples

Hopefully it is now clear why using the right formation water composition for design and operational purposes is important. Therefore, the next step is to know how to take a downhole water sample with the least amount of drilling fluid contamination in the shortest period of time possible.

This is important as the drilling fluid, especially water-based drilling fluids are likely to have components which will contaminate any formation / aquifer water sample taken. Potential contaminants are NaCl, KCl, CaCl₂, acetic acid, BaSO₄^2-, etc. To minimize drilling fluid contamination there are downhole tools available that identify when the fluid sample to be taken has been cleaned up enough to minimize drilling fluid contamination. One example of this type of tool is the “Quicksilver probe” supplied by SLB. Other service companies have their own version of this type of tool. You just need to ask them about it.

These tools are not always 100% efficient so you can still get a small amount of contamination that needs to be taken into account. To understand how much the sample is contaminated one can ask for a chemical tracer to be added to the drilling fluid, with the aim for it to show up in the water sample. By measuring the chemical tracer, you can accurately determine the amount of drilling fluid contamination there is. If you know the level of contamination and the composition of the drilling fluid, you can then back calculate the contamination out of the analyzed water sample.

If you are to use a chemical tracer, it is important that the tracer has a chemical composition that would not naturally occur in the water sample being taken. One example of a tracer the author has used in the past is sodium thiocyanate (NaSCN).

Once you have gone through the effort of getting a nice clean water sample, the last thing you want to happen is for the sample’s integrity to be compromised during its journey to the laboratory for analysis. In many instances, the samples age and that process can potentially cause a change in composition even for pressurized samples in many instances. Taking that into account when a pressurized downhole water sample is taken it is recommended that this is blown down offshore and preserved for transport. In the case where you want to keep a water sample long term then it is probably better if you keep the sample pressurized.

For me if I have a choice I prefer to analyze the water sample as quickly after it has been taken and preserved. This way you minimize the chance of the sample aging and affecting the results.

With sample preservation being so important Pontem has developed a comprehensive water sample preservation process.

Water Sample Preservation Requirements

When taking water samples from any oil or gas well it is important that they are appropriately preserved offshore to maintain their integrity before they are analysed. This is because without sample preservation, changes in temperature and ageing of the sample may cause the composition of the water to change causing misleading results to be obtained.

To help with the above process we have provided some information on the recommended water preservation techniques to use

Water Preservation Techniques

1) WATER SAMPLE PRESERVATION FOR CATIONS (METALS) ANALYSIS

If possible, use an acid-washed glass bottle for this sample. This can be done on-site, by rinsing the bottle with deionised water with acid added.

Immediately after sampling, add 3 mL of 35% nitric acid (1 part conc. acid to 1 part water) to 1 litre of sample, or 2 ml to 500 mL, or 1 mL to 100 mL.

Check the pH if possible using indicator paper – it should be below pH 2. If not, add another 1 mL of acid.

2) UNPRESERVED WATER SAMPLE FOR OTHER ANALYSES (pH, ANIONS, etc)

Fill the bottle with sample (leave minimal air space). Keep cool (below original sample temperature, if possible). Submit for analysis without delay. A minimum volume of 100 ml is desirable.

To keep water samples cool I have used an Esky to great effect. They can be used for storing water samples as well as beer 😊(probably best not to do it at the same time though).

3) WATER SAMPLE PRESERVATION FOR VOLATILE FATTY ACIDS (VFA) ANALYSIS

Add 1 mL of 6N sodium hydroxide solution to XXX mL water sample (labelled 6N NaOH) – CAUTION: STRONGLY CAUSTIC, WILL BURN SKIN.

Cap the bottle tightly and invert it several times to mix the contents.

Check the pH if possible using indicator paper – it should be above pH 9. If not, add another 1 mL of NaOH and shake again.

Only a small volume of water (10 ml) is needed for VFA analysis. However, if the sample volume is less than 100 ml, write the actual volume of NaOH solution added on the label of the bottle. A correction can then be made to the result.

Once in the hands of the laboratory that is performing the water analysis. It is important to use the correct analytical methods to minimize any analytical errors in the final concentrations measured.

Now for the Science Bit

Once you have acquired the water samples, minimized contamination, preserved the water for transportation, and got them to the lab for analysis. The next thing we need to do is work out what analytical chemistry techniques to use. For Chemistry Nerds like me I would say this is the fun part.

Figure 1: Young Stephen Hamilton…not on Halloween.

Strap-in for an analytical chemistry lesson.

The analysis of the formation / aquifer water samples involves a number of sophisticated analytical techniques that are not available everywhere so it is important you know what is required so you can go and find a lab to do the analysis that is needed. The main companies that do this in the oil and gas industry in alphabetical order are:

1. Corelab

2. Expro

3. Intertek

4. SLB

However, you might not be able to always access this expertise so you may need to look closer to home. If that is the case, then in-country Universities and NOC / Oil and Gas Industry Research labs are good alternative options.

Here are the things that you would want to analyze for to get a workable formation water composition:

• Cations (Metal Ions such as Na⁺, K⁺, Ca²⁺, Mg²⁺, Ba²⁺, Sr²⁺, etc)

• Anions (eg. Cl^-, SO₄^2-, HCO₃^-)

• Volatile Fatty Acids (e.g. formic acid, acetic acid, propanoic acid, butanoic acid, etc)

  • VFA’s are probably one of the most overlooked and under reported analytes in proper brine characterization. Without these values in hand, several risk analysis profiles are left with wider uncertainties, which can impact start-up costs and future OPEX.

• pH – Ideally the pH should be performed onsite as soon as the water sample has been blown down as changes in dissolved CO₂ and exposure to the atmosphere will change the pH over time.

• Salinity / Conductivity

• Density

Cations Analysis Method

Metal ions such as Na⁺, K⁺, Ca²⁺, Mg²⁺, Ba²⁺, Sr²⁺, Fe²⁺ are analyzed with Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP AES).

ICP AES is an analytical chemistry technique used to detect and quantify the concentrations of chemical elements in a wide variety of samples. The method is known for its high sensitivity, accuracy, and ability to analyze multiple elements simultaneously, from trace to major component levels.

How ICP-AES works

The ICP-AES process involves several key steps:

1. Sample preparation: The sample must first be converted into a liquid form. Solids are typically digested using a strong acid or fusion techniques, while liquids may be diluted or used directly.

2. Aerosol generation: The liquid sample is pumped into a nebulizer, which turns it into a fine mist or aerosol with the help of a flowing argon gas.

3. Introduction to plasma: The aerosol is carried into the core of an argon plasma, a superheated (6,000–10,000 K) ionized gas that serves as the excitation source.

4. Excitation: Inside the plasma, the heat evaporates the solvent, breaks molecular bonds, and excites the atoms and ions, raising their electrons to higher energy levels.

5. Emission: As the excited electrons relax and fall back to their ground state, they emit light at specific wavelengths characteristic of each element.

6. Separation and detection: The emitted light is directed into a spectrometer, which uses a diffraction grating to separate the light into its individual wavelengths. Detectors, such as charge-coupled devices (CCDs), then measure the intensity of the light at each specific wavelength.

7. Quantification: By comparing the intensity of the measured light to calibration standards with known concentrations, the instrument calculates the concentration of each element in the sample.

Key features and advantages of ICP AES

• Multi-element analysis: Can measure up to 70 different elements in a single analysis run, which dramatically speeds up testing.

• Wide dynamic range: Can accurately measure elemental concentrations from parts-per-billion (ppb) to high percentages within the same sample, reducing the need for multiple runs.

• High sensitivity: Offers excellent detection limits, especially for metals and semimetals.

• Minimal interferences: The high-temperature plasma minimizes the chemical interferences that affect other techniques like atomic absorption spectroscopy (AAS).

The use of ICP takes care of the metal cations and some of the other elements such as phosphorous (P), Sulfur (S), Arsenic (As), etc. Now we measure pH and conductivity of the water. This can be done using ASTM D1293 and ASTM D1125 respectively.

Anions Analysis Methods

The next in line should be the analysis of the anions such as Cl^-, SO₄^2- and HCO₃^-. The first anion, we will discuss is HCO₃^-. This can be determined by measuring the total alkalinity of the water sample. This is measured using a potentiometric titration by titrating hydrochloric acid into the water sample until the equivalence point is reached.

Total alkalinity is usually reported as HCO₃^- concentration. However, you need to be aware that although most commonly reported as HCO₃^- , it is actually a mix of carbonate, carboxylate and hydroxide species.

Total alkalinity equation is

A_T = [HCO₃^-]+[CO₃^2-]+[Acetate]+[OH^-]-[H⁺]

In most cases the vast majority of the alkalinity is associated with the HCO₃^-ion but where there are high levels of volatile fatty acids you may get excess HCO₃^- causing the formation water to be supersaturated in CaCO₃ at reservoir conditions. If that happens you just need to remove enough HCO₃^- ions from the water so that at worst CaCO₃ is at equilibrium saturation at reservoir temperature and pressure.

Although a tried and tested method, the use of a titrator does provide a greater spread of results than compared to the cation results you get from the ICP. There is an alternative method that is not so commonly used and that is the use of Ion Chromatography (IC) which can be used to differentiate between the various alkaline species thus providing some added confidence in the HCO₃^- results obtained. There are some advantages in knowing the “true” bicarbonate numbers, however the main point of emphasis is to understand what values the laboratory is reporting back so subsequent calculations and modeling will be appropriate and accurate. We have provided some additional information on the use of Ion Chromatography later in this post.

Therefore, it is important to look more carefully at the results and not just assume the number generated by the lab is the correct value. Where possible it is good to get a result from a second analysis technique or lab.

Finally, one little tip I think is worth sharing regarding the HCO₃^- concentration is that if your analysis gives you > 2,000 mg/l of HCO₃^- then there is something probably wrong.

For the other anions (Cl^-and SO₄^2-) that are needing analyzed; it is recommended to use ASTM D4327 or better which uses Ion Chromatography (IC also known as Dionex). Ion chromatography can also be used to analyse for Volatile Fatty Acids but first they must be converted to their carboxylate salts to make sure they do not flash off from the water sample during the sample transportation process. (See Water preservation procedure)

Ion Chromatography (IC) separates ionic substances by exploiting their different charge properties. A sample is introduced into a column containing a stationary phase with fixed charged groups. Ions in the sample then compete with the stationary phase’s counterions for binding sites, retaining them based on the strength of their ionic interactions. A mobile phase (eluent) then washes the ions through the column, with different ions eluting at different times, allowing for their separation and identification.

Here’s a breakdown of the key principles:

1. Charged Stationary Phase: The column is packed with a material that has immobilized charged groups.
   Anion Exchange: The stationary phase contains positively charged groups to attract negatively charged ions (anions).
   Cation Exchange: The stationary phase contains negatively charged groups to attract positively charged ions (cations).

2. Sample Introduction: The sample, containing the ions to be separated, is injected into the column.
   

3. Ion Exchange: Ions in the sample compete with exchangeable counterions for binding sites on the stationary phase.
   Ions with stronger coulombic (ionic) interactions with the stationary phase will bind more tightly.
   Factors such as charge density and ion size influence the strength of these interactions.
   

4. Elution: A mobile phase (eluent), which is a solution of ions, is passed through the column.
   The eluent displaces the ions bound to the stationary phase, causing them to separate. Ions that bind weakly are washed away first, while more strongly bound ions require altered conditions (e.g., higher ionic strength) to elute.
   

5. Detection: As the separated ions exit the column, they are detected, most commonly by conductivity, which measures how well the solution conducts electricity.

If an Ion Chromatogram is not available, the following methods to analyse for SO₄^2- and Cl^- can be used:

SO₄^2- - APHA 4500-SO4 E (colorimetric) or another option is to use ICP-AES to measure total sulfur content in the formation water and then assume that all the sulfur is associated with SO₄^2-. This is not ideal as you can overestimate the level of SO₄^2- ions present if there are other forms of sulfur ions present such as sulfides.

Cl^- - APHA 4500-Cl B (titration). Although a commonly used method, it is probably less accurate than ion chromatography.

Evaluation of the Results

All of the above analytical methods are effective at analyzing the various components in a formation water sample. However, it is the authors opinion that ICP-AES is the most reliable method for measuring the various cations and elemental species in the water and that Ion Chromatography is the most reliable method for analyzing the anions compared to the various titration techniques.

It is also worth noting that the quality of the results can vary from lab to lab so it is important to have a reasonable amount of skepticism when reviewing the results of any analysis that has been performed. When it comes down to it you can send exactly the same water sample to three different labs, and you will probably get three different answers. Trying to fathom what is right in those instances really comes down to experience and understanding what “good” should look like.

Once all the analysis is done you usually get a results table that looks a bit like this. This table has the minimum data you need to allow you to do the analysis such as determining the scaling and corrosion risks associated with the water. In many instances you can get a lot more cations and anions analyzed depending on your requirements. If you are worried about heavy metals such as lead, cesium, vanadium, etc. They can be analyzed but you may have to tell the lab that you would like those included as they don’t always get automatically measured / reported.

Quick QA/QC checks

Once you see the water analysis results from the lab there are a few things you can do to check if the results look “right”. These are:

1. Check what type of geology the water has come from. For example if it is a lacustrine environment, the water salinity may be very low (TDS < 5,000 mg/l) or if it is near a “salt dome” the formation water may be virtually saturated in NaCl (TDS >250,000 mg/l).

2. Are the K⁺ and/or Ca²⁺ concentrations in the water similar or higher than the Na⁺ concentration? If they are, check what brine was used in the drilling fluid. There is a good chance the sample has been contaminated.

3. Unusually high levels of VFA usually may mean some contamination from acetic acid contained in the drilling fluid or well clean-up fluids but worth checking whether other fields in the same area have high levels of VFA because there are formation waters that do have high levels of VFA but they aren’t very common. I talk a little bit more about this later in this post.

4. Does the water sample have a high concentration of Fe²⁺ (>20 mg/l)? If it does it may be caused by corrosion of the sample bottle or well tubing. Also worth checking if the rock, the water has come from contains high levels of iron ore just in case the Fe²⁺ values are correct.

Finally, as well as the above, one of the best things you can do is to make sure that the water composition makes sense from a geological perspective. This involves determining whether there is any scaling components either supersaturated or undersaturated in the formation water that in reality should be saturated in the water at worst. You do this because at reservoir temperatures and pressures experienced, water that has been sitting in the rock for millions of years at pretty much the same conditions should not be supersaturated. That is because any inorganic scale that could have dropped out would have dropped out and left whatever dissolved salt that was left as saturated in the formation water. Also, for CaCO₃ the expectation is that in the CO₂ rich environments we have in the oilfield, the default is to expect any formation water to be saturated in CaCO₃ at reservoir conditions. Sometimes this can be a fairly straight forward process, or it can take some time to get right but it is certainly worth doing. If you want to learn more about this process Pontem have already written an excellent Substack post looking at this exact process.

This process gets easier the more experienced you get because after seeing hundreds, if not thousands of different formation / aquifer water compositions you get to understand what “good” looks like. But it is still not easy as you have such a wide variation of water compositions. These can range from virtually mineral water samples with Total Dissolved Solids (TDS) no more than 1,000 – 2,000 mg/l to formation waters that are nearly saturated in NaCl with TDS contents of up to 300,000 mg/l.

In fact, one case I looked at last year was for a formation water that had >1,000 mg/l of acetic acid in its composition. Even though I had worked on fields that had these levels of acetic acid previously I still thought the acetic acid concentration was so high that there must have been some drilling fluid contamination as the drilling / clean-up fluid did contain acetic acid.

However, I knew I had to get this right because at these levels of acetic acid this could have a significant impact on the inorganic scaling (in a good way) and corrosion risks (in a bad way) associated with producing the formation water under evaluation.

Consequently, I did some more homework and fortunately for me I had formation water compositions for two nearby fields to compare against. Both fields had formation water compositions that had levels of acetic acid that were also > 1,000 mg/l. When you see that you know it isn’t drilling fluid contamination but a real effect as it is just not plausible that all three fields could have the same level of drilling fluid contamination when taking their water samples over a time period of multiple years.

Finally, once you are confident in the water composition you can start to use it to determine whether inorganic scale will deposit in the system. The level of the scaling ions like Ba²⁺, Ca²⁺, SO₄^2- and HCO₃^- are critical components from the water analysis, and with respect to CaCO₃ scaling, the pH is very important so you need to look at the HCO₃^- and VFA concentrations along with the CO₂ content at the various pressures and temperatures in the system.

When assessing the corrosion risks the most important parameters taken from the water sample are; Chlorides, VFA concentration, pH and CO₂. It is also worth noting that it is not the end of the world if you don’t have a reliable pH measurement because as long as you have the CO₂ mol% in the gas phase and the system temperatures and pressures there are plenty software packages that can work out what the system pH is.

Usually for scaling, the more CO₂ and less HCO₃^- in the system, the better it is for CaCO₃ scaling as the pH is more acidic. For corrosion, the opposite is true, as low pH drives higher corrosion.

When it comes to evaluating whether a system has a calcium naphthenate or carboxylate soap problem. Here are a couple of helpful rules of thumb that can be invaluable when assessing a new field’s formation brine for Calcium Naphthenate and Carboxylate soap risks. A few include:

• Calcium Naphthenates and Carboxylate Soaps can become an issue if the pH is greater than 6. Solids and soaps will begin to form in these higher pH scenarios.

• Water compositions with high HCO₃^- and VFA concentrations (>1,000 mg/l, each for example) are indicative of oil with ARN acids that form the Calcium Naphthenates and Carboxylate Soaps. This combination of high levels of HCO₃^- and VFA frequently occurs when you have problems with Calcium Naphthenates and Carboxylate Soaps. It is not infallible but shouldn’t be ignored.

Hopefully this shows how much goes into obtaining a representative water samples and what sort of information you can get from it once it has been analyzed properly. If you want help from Pontem Analytics feel free to contact us.

Subscribe now

When high CAPEX and OPEX decisions hinge on a few degrees or a few bars, every small deviation can carry real financial and operational consequences.

That said, not every situation calls for full-scale thermodynamic rigor. Sometimes, the goal is to get a quick, reliable estimate — something that gives engineers an immediate sense of hydrate risk without hours spent on setup or simulation. And that’s a trade-off we’ve been exploring over the past week: the balance between speed and accuracy in gas hydrate prediction tools.

Our work at Pontem Analytics often involves gas hydrate modelling as part of broader flow assurance and thermodynamic evaluations. During our ongoing work, we came across the p2f-hydratecalc, a Gas Hydrate Equilibrium Predictions Calculator, which is based on the Klauda-Sandler fugacity model (PR-SV equation of state) and HLS correlation, developed at the Phases to Flow Lab led by Professor Amadeu Sum at the Colorado School of Mines (CSM). Throughout this post, we will refer to it as P2F (short for Phases to Flow).

P2F is freely accessible online, offering anyone the chance to explore and experiment: https://p2f-hydratecalc.streamlit.app/

Given our strong academic and professional connections with CSM, and our shared enthusiasm for gas hydrate science, we saw an opportunity to benchmark P2F against the industry-standard Multiflash software, which we know first-hand has a very good match to lab-measured hydrate curves.

As a team of self-proclaimed hydrate nerds, we wanted to explore how this simplified, fast-running app performs compared to a full thermodynamic simulator across various fluids and inhibitor systems representative of real-world production conditions.

Basic Inhibitor Comparisons

The initial stage of the study focused on the simplest hydrate control systems — methanol, MEG, and salinity. These are the fundamental inhibitors used in field operations and serve as a clear benchmark for evaluating how the two software tools perform and compare. Starting with these single-inhibitor cases gave a solid baseline before introducing more complex combinations later on. It is also important to keep in mind that all hydrate system comparisons were evaluated using the CPA (Cubic-Plus-Association) and CPA + Electrolytes equations of state, for freshwater and saline systems, respectively, which have proven to be the most accurate EoS in the case of gas hydrates.

The comparison was carried out across three fluid systems:

• a lean gas condensate

• a rich gas condensate

• and an STP gas flashed from an oil fluid

The choice of fluids was deliberate. We wanted to see how each model responds as the composition shifts from methane-dominant systems to those poorer in methane. Gas condensates represent lighter, more volatile fluids, while the oil, after being flashed to STP, introduces a higher percentage of C2-C5 into the mix.

Hydrate Curves with Methanol

Across all fluids, methanol produced a strong level of agreement between Multiflash and P2F results.

For both lean and rich gas condensates, the hydrate curves were very much in agreement, with only minor negative deviations by P2F. The largest deviations appeared below 500 psi — corresponding to regions at the lower end of typical operating conditions.

The STP gas from an oil fluid, however, showed slightly larger deviations, though remaining within the general range observed across systems. A consistent P2F positive deviation emerged across all methanol concentrations, with an average difference of ~2 °C between P2F and Multiflash results when the methanol is present. This phenomenon is briefly explored in the Theory Discussions section later on. In summary, we believe that this might be due to the way each package deals with gas and inhibitor solubility, as Multiflash, being the more robust of the two, accounts for that, hence why its curves are less conservative than P2F.

Hydrate Curves with MEG

MEG showed a similar story to MeOH in the lower to medium concentration range.

Across all fluids, the base, 20% and 40% inhibition levels showed an excellent match between Multiflash and P2F. The hydrate dissociation curves overlapped closely, indicating that for moderate glycol loadings, the simplified P2F model can produce thermodynamic behavior that greatly agrees with Multiflash.

Differences became noticeable only at the upper end of the inhibition range. At the maximum MEG concentration, P2F showed an average negative deviation of ~6 °C relative to Multiflash. While still capturing the overall curve shape, the magnitude of this deviation indicates that at higher glycol concentrations, non-ideal effects in the aqueous phase — represented differently across the two models — start to become more pronounced.

It is also possible that these effects are influenced by the extremely low temperatures associated with higher MEG inhibition levels. While these temperatures are low, they are still within the range routinely handled in MEG-based low-temperature separation systems.

Hydrate Curves with Electrolytes (Salinity)

For better or for worse, formation water never comes as pure fresh water; salinity is always part of the picture. Before moving on to more advanced combinations, it was essential to first understand how both models respond to saline systems.

The comparison for a wide range of salinities showed near-perfect agreement across all fluids. Both Multiflash and P2F captured the expected shift in the hydrate equilibrium towards lower temperatures.

On average, deviations were within 0.7 °C, representing a small difference under typical operating conditions. This consistency provides confidence that both models can be used for base hydrate curve generation — cautiously, yet confidently — when dealing with typical formation water salinities.

Advanced Comparisons

Having explored the effects of individual inhibitors and salinity in isolation, it was time to move forward towards a more realistic field scenario — one where inhibitors interact with formation water rather than pure, deionized systems. This step also raises the modelling complexity, testing how each simulator handles overlapping inhibition mechanisms.

MEG or MeOH + Salinity

When salinity was introduced alongside methanol and MEG across all investigated fluids, the results revealed remarkably consistent trends. Both Multiflash and P2F reproduced the same relative behavior observed in the freshwater cases. The only noticeable shift came from the additional inhibition properties of salts, slightly lowering the hydrate curve temperature — a trend both models captured with precision.

In short, whether inhibitors were modelled in freshwater or saline environments, the consistency across all fluids reaffirms the robustness of the thermodynamic frameworks behind both tools.

Unfortunately, the same cannot be said when methanol and MEG were combined. When two inhibitors were introduced into the system, the deviation between P2F and Multiflash increased, reflecting differences in how each model represents mixed inhibition mechanisms.

For the freshwater systems, a pronounced positive deviation trend emerged from P2F — the higher the combined inhibitor concentration, the greater the deviation. At maximum inhibitor concentration levels, the difference reached ~16 °C, representing a deviation magnitude larger than typically observed in hydrate curve modelling.

With that said, however, it is important to keep in mind that under very rare circumstances will the two inhibitors be combined. If both solvents are to be used, they are usually injected at different times across the lifetime of the operation. Even so, it can still sometimes be the case, and it is therefore worth having a look at how the two systems compare under these solvent conditions.

When the same mixtures were tested in saline systems, the deviations decreased substantially. Here, P2F showed a modest negative deviation trend, with deviations around ~3 °C. This pattern may indicate that ion–inhibitor interactions influence the model’s thermodynamic behavior.

Comparison Summary (MFL vs P2F)

In summary, P2F provides a balance between simplicity and predictive accuracy, whereas tools like Multiflash provide additional modelling flexibility — particularly for more detailed hydrate studies.

Theory Discussions

The Hu–Lee–Sum (HLS) correlation used in P2F performs well given its simplicity; several theoretical and methodological limitations remain, many of which are acknowledged by its authors. Understanding these is crucial for interpreting its results and improving the tool.

• Cross-Interaction Simplification Between Salts and Organics

  One of the key simplifications in the HLS model is how it treats salts and organic inhibitors separately. Their contributions to water activity are simply summed

  (ln aw_salt + ln aw_organic). This assumes that cross-interaction effects between ions and organic molecules are negligible.

  Suggested Improvement: Introduce a cross-interaction correction term (fitted parameter derived from mixed brine + THI datasets). This retains the simplicity of the algebraic form but captures non-ideal behavior in real-world mixtures more faithfully.

• Gas Solubility Effects Not Explicitly Included

  Gases like CO₂ and H₂S dissolve significantly in water and alter the water activity — an effect the current HLS formulation does not explicitly account for. As a result, the model tends to show larger deviations in acid-gas–rich systems.

  Suggested Improvement: Include a gas solubility correction term that would extend HLS applicability into CO₂ and H₂S bearing systems without resorting to full van der Waals–Platteeuw complexity.

The following references were used in the investigation of the Hu–Lee–Sum (HLS) correlation:

1. 1. Kunneman, K. & Sum, A. K. (2025) p2f-hydratecalc: A Web Python-Based Tool for the Prediction of Natural Gas Hydrate Equilibrium and Inhibition, SoftwareX, 32, 102422. https://doi.org/10.1016/j.softx.2025.102422

2. Hu, Y., Sa, J.-H., Lee, B. R., & Sum, A. K. (2018). Universal correlation for gas hydrates suppression temperature of inhibited systems: III. Salts and organic inhibitors. AIChE Journal, 64(11), 4097–4109. https://doi.org/10.1002/aic.16369

3. Zhang, X., Vijayamohan, P., Hu, Y., Sum, A. K., & Subramanian, S. (2020). Gas hydrates phase equilibria for brine blends: Measurements and comparison with prediction models. Fluid Phase Equilibria, 521, 112688. https://doi.org/10.1016/j.fluid.2020.112688

Looking Ahead

P2F is notable for its accessibility — free, simple, and fast — providing an efficient route to hydrate prediction while reducing opportunities for manual error. In practice, that means engineers can move from concept to insight in minutes, without the overhead of configuring complex thermodynamic models.

But this isn’t where the story ends. We’re already exploring ways to embed this capability into our own digital workflows, integrating a quick hydrate-screening method for flow assurance solutions:

• Integrating Fast Hydrate Tools: Turn academic hydrate models like P2F into practical field tools through smart integration with live data systems.

• Real-Time Field Monitoring: Connect P2F to live platform data (temperature, pressure, salinity, inhibitor concentration) to calculate hydrate risk instantly — visualized as green/yellow/red status with no manual input.

• Smart Inhibitor Dosing: Automatically adjust inhibitor injection within set limits when hydrate risk increases — minimizing overuse, cutting costs, and maintaining safety.

• Transient Event Monitoring: During operations like start-up or pigging, P2F recalculates hydrate risk continuously, adapting dosing in real time and managing transient risks predictively.

• Fleet-Wide Risk Dashboard: Aggregate live data from multiple platforms into a central heat map to identify hydrate risk trends, prioritize interventions, and shift from reactive to predictive management.

• Predictive Scenario Simulation: Run “what-if” analyses for planned operations (e.g., pig runs or flow changes) to forecast hydrate risk and recommend proactive measures before execution.

Closing Remarks

In summary, P2F, powered by the HLS model, is an accurate, fast, and user-friendly solution for most practical hydrate assessments. However, Multiflash remains the more flexible, fully thermodynamic platform, capable of handling and designing complex systems. Overall, when compared, both tools generally show an excellent match within a reasonable deviation margin.

Together, they represent the trade-off between speed and precision — and understanding that balance allows engineers to choose the right tool for the right task.

The goal isn’t to replace comprehensive simulators like Multiflash — it’s to complement them, providing a first-pass diagnostic layer that helps identify hydrate risks early and intuitively.

Discussions are also underway with Dr. Sum and his team at the Colorado School of Mines to explore potential collaborations that could enhance both the thermodynamic model and its integration into field-ready applications. It’s an exciting opportunity to bridge academic precision with operational practicality — and keep pushing hydrate modelling towards tools that are smarter, faster, and built for engineers.

Subscribe now

The afternoon highlights included the keynote address, two panel discussions, and closed with a networking happy hour. COGA serves as an advocacy group for the oil & gas industry, operating in the political and regulatory arenas within the state of Colorado. The Energy Summit is worth attending, as topics for discussion are important and interesting for anyone in the Energy space.

Rocky Mountain High

Folks outside Colorado may not realize the importance of Colorado in the O&G arena. It is a net energy exporter, is the #4 oil producing state and is the 8th highest gas producing state in the country.

Colorado is quite unique because of its abundant mineral resources, beautiful mountains and parks, and high population density all in close proximity. It is a challenging needle to thread when seemingly competing agendas collide: lots of people want to live in the state, they want easy access to pristine and untouched nature, and want affordable energy that provides the state a lot of jobs and revenue! I say seemingly because Colorado oil and gas is some of the cleanest oil and gas out there. Operators in the state have led the charge in curbing emissions, keep the smallest footprint possible with amazing technology, and continuously work together with community leaders and legislators for the best approach to providing affordable energy. It is no wonder COGA puts together an interesting set of topics each year.

The State of Energy in Colorado

The keynote presentation was given by Jimmy Fortuna (Enverus), who offered a wholistic perspective of Colorado Energy. Many of our engineer readers understand how messy things can get when looking at dynamic systems, yet Jimmy gave an excellent synopsis of Colorado against the backdrop of mineral production, reserves, and consumption; renewable energy production increases and total sources of electricity; and Colorado policy. His analyses were thorough and his graphics helped tell a story in which Colorado will have some interesting decisions to make. The answers to 3 big questions will tell what Colorado will look like five years from now:

• How will operators continue to develop “easily accessible” crude oil inventories remaining in the Colorado basins (i.e. <$50/bbl crude)? And how will “lower tier” reservoirs be developed creatively to remain competitive? We’ve helped clients with their networks as they are expanding to handle the many complexities that come with larger networks.

  

• Will we be able to access the large gas basins in Colorado due to proximity to communities and nature areas with existing regulations in place? Over 60% of Colorado’s electricity comes from coal. The current policy calls for 100% decommissioning of all coal plants by 2031, with zero plans to replace that energy. It feels like there’s a solution that could alleviate a lot of concerns.

• Are the goals set by current policy for renewable energy, electrification, and reduction/elimination of coal sources achievable in the face of consumption, generation, and grid expansion needs? Wind and solar power are continually being introduced into state grids (all states, including Colorado AND Texas) and everywhere electricity prices are getting astronomically more expensive. That’s a disconnect. The current path is not in alignment with affordable power.

Pontem has been involved in the DJ, Powder, San Juan, and Green River basins since 2013, as AFS (shout out to anyone who attended our first Thinking Beyond… Symposium held at the Broncos stadium).

We continue to help clients maximize opportunities with our digital tools and domain expertise in production and multiphase fluid flow.

AI In The Oilfield

The first panel discussion topic was artificial intelligence and addressing fundamental questions around how it impacts operators, how it has been implemented thus far, and what role will AI play in the future? The consensus was AI is here to stay and all panelists believe there are benefits to utilizing AI in some capacity. There were suggestions that it was likely necessary for continued success, although the panel had different definitions around what guardrails are necessary to keep AI within the framework that fits best within the company guidelines. We have seen this in practice with our projects and it has been critical that we are nimble enough to operate within our clients boundaries.

Another interesting point discussed was the many ways companies are utilizing AI. There is definitely not one-size-fits-all applications and operators discussed several different philosophies in which they have found success with AI. As companies navigate the roles AI plays, it seems the most successful operators and service companies will be the most nimble and adaptable.

Reshaping Colorado’s Regulatory Landscape: SB19-181

SB19-181 was put in place six years ago and the afternoon’s second panel discussion offered a look back on the impacts of the legislation. One of the consequences of the legislation was to make permitting more strict, and the process has had the effect of much longer permitting time, although over the years that trend has started to decline. A profound statement was made during the discussion in that the legislation was to create a shift in thinking: prior to the new regulations the starting point was with the minerals in the ground, and we figured out how to get them to the surface. Now, the regulations want the surface to be the primary focus and from there figure out how to get to some of those minerals.

Closing Thoughts

Many of the challenges facing Colorado are not in isolation. The difference is there are real opportunities to develop resources to provide affordable electricity that is cleaner, allows Colorado to continue to be a net energy exporter, and provides significant, high paying jobs and revenue for the state it desperately needs. In order to realize those amazing things we, as an energy community, need to put in place a pragmatic energy policy that allows many needs to be met, while providing sorely needed affordable energy to the great state of Colorado.

TLDR

In many gas-condensate systems, as reservoir pressure is depleted, the heaviest, wax-forming components are increasingly left behind in the reservoir. The produced stream “leans out” and becomes lighter, less waxy, with fewer production headaches. This isn’t magic; it’s phase behaviour + porous-media physics. Below, we unpack what reservoir leaning is, what strengthens it, and how to operate to capture the advantages it offers.

Wax remains a big flow assurance risk in hydrocarbon production, as we have written about in many previous case studies.

While those were predominantly oil systems, wax related risks in gas-dominated systems present their own unique set of challenges:

• Sampling gaps: Pre-production sampling can fail to capture and/or quantify accurately the heavy paraffinic components

• Model limits: The tools may be insufficient to accurately predict the wax deposition in gas-dominated systems as they are largely based on single-phase, laminar flow, with 0% water oil-dominated systems

• Hydrate focus: As gas-dominated systems are usually governed by hydrate requirements (i.e. continuous MEG/MeOH injection), the passive insulation requirements are removed which eventually leaves the system to cool faster leaving more potential for wax related risks

• Chemical conflicts: Paraffin inhibitors may be incompatible with other chemicals (i.e. MEG) and/or be difficult to deploy due to high viscosity

• Changing fluids: Reservoir pressure decline can shift fluid composition over life-of-field, changing risk profile (i.e. severity, location, treatment options, etc.)

While wax content in condensates is typically lower than in oil fields - and may have a different hydrocarbon distribution - it can still reach levels high enough to create serious flow restrictions with time, especially in specific reservoirs or under certain operating conditions. We’ve seen more than a few gas-condensate fields where wax became a real operational challenge—one that had to be anticipated and mitigated well before production started (and was often not…).

The fact is - we should worry about wax issues at the design stage, because there is not an “easy” and straight-forward answer for dealing with paraffin-related issues in gas systems when they arise. Example below with an excess dP observed in early field life which is proofed to be from a wax presence, which was not anticipated prior to start-up.

The negative consequences of unexpected wax are numerous:

• Higher back-pressures, leading to deferred production and/or accelerated well phasing.

• Inability to remediate (i.e. stuck pig) should a remediation campaign be required.

• Inability to spot chemical treatment if sufficient access points are not provided in the system design

• Potential integrity management concerns (i.e. under-deposit corrosion), particularly in the event of solids production.

As awareness grows, operators can pull these concerns forward and cater for them in the design…at a cost! Designing for continuous wax-inhibitor injection or insulating a system to stay above the Wax Appearance Temperature (WAT) can drive costs so high that smaller gas-condensate developments struggle to stay economic.

And there we come into the game…. Diving deeper into the life cycle of the field and unlocking the potential.

Life of a Gas Condensate in the Reservoir

For those who think that gas condensate fluids contain little or no wax, let’s break that down:

• “Little” wax? Fair - but even small amounts can precipitate and deposit, and those deposits can build up over time. And, given the nature of gas fields - which typically are allowed to produce at colder temperatures (due to continuous hydrate chemical program), larger amounts of wax can precipitate as dictated by the wax precipitation curves (more on that below…)

• “No” wax? Extremely rare. And be careful: if you’re basing that claim on topsides samples, you may be misled.

  • Wax content should be assessed from bottomhole samples to capture the heavy ends before they drop out.

  • Topsides fluids often arrive at temperatures below the Wax Appearance Temperature (WAT), so the heavier components may have already precipitated or deposited along the wellbore, leaving you with an artificially “clean” sample.

The ultimate challenge remains: To Design or Not Design for Wax?

• Under-design can lead to lost production and costly remediation

• Over-design can lead to uneconomic project

When developing a new system, using accurate measurements is critical—but we also have to remember how a reservoir changes over time. Most gas-condensate reservoirs undergo natural depletion, meaning pressure falls steadily while temperature stays essentially constant.

The phase envelope below illustrates the point: as production continues, the reservoir pressure can drop dramatically, even while temperature remains unchanged. Eventually, the pressure will fall below the dew-point curve, and condensate will begin to drop out inside the reservoir itself, before it reaches the wellbore.

Why does that matter? Because as the reservoir depletes:

• The produced condensate-gas ratio (CGR) declines.

• More of the heavy ends (including n-paraffins) remain in the reservoir.

This phenomenon is also known as ‘condensate banking’, which is good from a wax risk perspective, but on the other side is bad from a well productivity perspective as the dP near the wellbore may increase over time.

However, every gas-condensate field must be evaluated case-by-case. Each reservoir’s phase behaviour is unique, and the “benefit” of pressure depletion, where lower CGR might reduce heavy ends reaching surface facilities, can vary widely:

• In some reservoirs, the CGR decline is modest, so any reduction in wax risk is minimal.

• In others, especially wet-gas systems (see below example) where the reservoir temperature lies to the right of the cricondentherm, which means that condensates and heavy ends will never drop out in the reservoir.

This variability underscores why wax-risk assessments for gas-condensate developments can’t rely on generic rules of thumb. Rule of thumb will work, but can make the development overdesigned and uneconomical. Detailed fluids analysis and reservoir wax leaning should be taken into consideration for a more optimum field development.

Wax “Leaving the Chat”

As always, let’s ground the discussion with a real case study.

We recently worked on a gas field with potentially waxy condensate. The fluids were fully characterized with a measured n-paraffin distribution and tuned to a measured Wax Appearance Temperature (WAT), which were deemed accurate for the collected samples. Taking everything into consideration, the following data were included for the reservoir wax leaning estimation:

• Fluid compositions, HTGC and WAT of every sample

• Reservoir pressure depletion over time

• Production forecast over time

  • Worth noting that the fluid leaning done confirmed subsurface estimates on expected condensate yield (decline) over time. It was a satisfying cross-check that confirmed our methodology. But raises the question - if forecasts are showing declining condensate (which we take credit for) due to changing composition, shouldn’t our paraffin risk also change over time?

To understand the wax risk of the system over time, some important wax parameters were evaluated. Anyone familiar with wax modelling will recognize the key parameters we focused on:

• WAT (Wax Appearance Temperature) – The temperature at which wax crystals first begin to precipitate from the fluid under a given pressure.

• Wax Content – The total mass fraction of paraffinic hydrocarbons present in the fluid that can precipitate as solid wax. Another way of illustrating this is the WPC (Wax Precipitation Curves), but we will keep it easy by only presenting the total wax that can precipitate.

Below are each of the above parameters along reservoir pressure depletion for some of the wells just to capture and illustrate the overall behaviour:

WAT (Wax Appearance Temperature)

What we see immediately (given a ~40°F seafloor temperature for this deepwater development), is the WAT reduces below ambient by Year ~3.5. Or, alternatively, when the reservoir pressure depletes below ~4000 psi, we are expected to be producing ‘wax-free fluids’.

Wax Content (n-C20+)

The plots tells a similar story: wax content is dropping over time (with declining reservoir pressure) which leads to a lower wax risk vs. time. Again, depending on actual reservoir pressure decline, by Year ~3 most wells are producing fluids with WAT below ambient temperature, effectively meaning wax risk has already “left the chat.”

Now consider the implications for modelling:

• Year-0 characterization only: If we had modelled wax deposition over the entire field life using just the initial fluid properties, the predictions would have overstated the risk.

• Wax modelling considering reservoir wax leaning: Incorporating the observed wax-leaning behaviour paints a more accurate picture of how deposition changes through time.

For this field, some wax deposition is expected during the early years of production, but the overall volume is expected to quickly dissipate. This time-dependent understanding enables significant optimization:

• Lower wax-inhibitor injection rates, adjusted downward as the fluid naturally becomes leaner in wax.

• Reduced pigging frequency, since long-term deposition is not critical.

It is really this last point - pigging assessment - that deserves attention and focus. Routine pigging for wax management, particularly for subsea tiebacks, is costly (downtime), risky (stuck pick), and logistically challenging (subsea launchers vs. dual flowlines, etc.). Building permanent pigging capabilities based on the assumption of routine pigging throughout field life may be required. However, knowing that wax risk reduces over time - and potentially a ‘wax-in-place’ strategy may be feasible - potentially makes a significant improvement to overall project economics. Adding upfront CAPEX that may not be required a few years into production is a decision that should be factored into upfront project design.

Conclusions/Takeaways

By updating the model with real-time data and wax-leaning trends, including continuous monitoring, operators can maintain flow assurance while cutting chemical and maintenance costs, avoid overdesigning the field and optimizing operation which eventually unlocks the potential of the fields that earlier were deemed economically not-feasible.

Over time, natural wax-leaning can reduce the amount of wax available to deposit and lower the WAT. By incorporating these time-dependent changes into modelling, rather than relying on a single year-0 characterization, operators can:

• Avoid over-designing mitigation measures

• Optimize wax-inhibitor injection rates

• Reduce pigging frequency

• Improve overall project economics

In short, careful characterization and continuous updating of wax-deposition models transform a perceived high-cost challenge into a manageable and economically efficient strategy for gas-condensate developments.

Subscribe now

What did you do this summer? Go anywhere fun?! Pontem made several splashes across the globe, to name a few: participating in a joint project for Methane Hydrate production (Japan), technology transfer collaborations (Mexico), contributing to the Prinos CCS project (Greece), supporting H2ALL’s efforts to bring clean water to rural communities (Uganda), chairing multiphase flow conferences (Portugal) and collaborating with various universities (Mediterranean). We also found time to get down…stream, that is. Why does this make sense for us and how do we differentiate ourselves in this space?

Loadin’ up and living right; Fast cars on a summer night

If we’re being honest, we have a history of going about our business a little unconventionally. Fundamentally, we believe the way we approach problems is translatable to multiple industries, and especially across the Energy sector. To say we solve problems does not really capture the value-add to our clients and may suggest we simply put a number on the right side of “=”. We believe our value-add is tell our clients what that answer means for them! The interesting part is when the conversation leads to “What if…” discussions and soon there is a matrix of solutions that provides options and an intelligent basis from which our clients can act.

Because we think this approach will resonate in downstream energy, we are confident we speak the same language. (Did you know: Our co-founder actually began his Energy career working FCC units across the refining sector AND our first project at Pontem was for hydrate monitoring in the downstream market) Not to put too fine a point on it, our data analytics approach is based on our collective experience as oilfield engineers and chemists and really honing in on the domain knowledge to better leverage the data. I mean, seriously, tell me if you’ve heard this one before:

• Construct a Digital Twin

• Look for plant inefficiencies and available margins

• Interact with a real-time optimizer OR go next level and utilize an APC thinly disguised as a new product

• Use the new product as a lens to report highest profitability under similar input conditions

In the shop window, it may look good, but soon you realize all these approaches are pretty similar.

The Balkan Peninsula

We are keenly aware of projects more in our wheelhouse in the Balkans, particularly in the Black Sea. OMV Petrom and Romgaz are developing Neptun Deep in the Romanian space, while the Bulgarian government has signed two exploration agreements: with Shell to explore 4,000 square meters in the Block 1-26 Khan Tervel field, and with OMV Petrom and NewMed Energy in another field off the Black Sea coast. We are still being opportunistic in the upstream arena, but we also think we can make an impact in Energy in this region in the Downstream arena.

Breeze is warm and music’s right; This endless summer

We were recently invited to a refinery in Europe to discuss their journey through digital transformation, share our capabilities, and explore our approach to data. Compared to more traditional upstream - where error bands may be inherently larger - we are now focused more on the ‘aggregated micro’ to realize those gains. Instead of +/- 10% over km of pipe, we are now +/- 10 psi in the reactor.

We are agnostic in our technology of choice, and have implemented everything from physics informed machine learning platforms to more nimble, lightweight data-driven models and target analytics, which offers multiple options in areas like mechanical availability, predictive maintenance, and smart manufacturing.

The Next Frontier For Data In Refineries

One topic that came up during our visit was process optimization. We were able to share our experiences developing models and tools that require handling multiple variable interactions and operating limits. Enhanced APC solutions are useful, despite my earlier dig, and can provide elegant solutions for these complex processes.

Optimizing Predictive Maintenance

Process upsets, equipment maintenance and failure, and efficiency losses can lead to unplanned downtimes, safety risks, and financial losses for a refinery. Developing tools to anticipate failures before they occur is one avenue to explore that can directly minimize downtime, save on costs, and improve uptime metrics. We excel in exploring these “what if…” scenarios to provide actionable and meaningful data. To the naked eye, it may not seem as if there is an obvious solution, however this is where we thrive. We are excited to continue our conversations with our new friends!

Closing Thoughts

Machine Learning is a big, generic term and a lot of companies are capable of delivering AI products and refineries have been implementing with success. While I trivialized a few concepts in jest earlier, a few real ML examples reported by refineries include significant benefits:

• Shell reported using ML digital twins to optimize steam cracking, resulting in $25MM/yr savings

• BP has implemented ML strategies in hydrocracking and have reported an increase in diesel yields by 6% when utilized

• ExxonMobil has incorporated predictive analytics to reduce some equipment downtime by 30%

We believe to make the next leap in data analytics companies must ultimately understand the environment they’re operating in and understand what the data saying and how can these products ultimately help the client? Our team has multi-decades worth of experience throughout the Energy Supply Chain based as operators, service providers, engineers, and consultancies. Because we came from the same space as our clients, we understand how to make tools deliver results that will make real impacts on their processes.

We look forward to visiting your facilities and collaborating on your data analytics products!

Subscribe now

I never really got why this is such a popular line of interview questioning. Looking back on how I got here, there wouldn’t have been a single point where I could have predicted where the next 5 years would take me.

Getting Started

What feels like an eternity ago, I graduated from the University of Bath with a Masters in Biochemical Engineering, ironically wanting to avoid oil and gas (spoiler alert – it didn’t last!). Starting off with stints in the water and pharmaceutical industries, I was building up a bit of career momentum, then ran headfirst into the 2008 financial crisis in the UK. Great.

With that delightful backdrop, the leap into oil and gas now felt like the right path after all, so I joined BJ Services in the Middle East as a Project Engineer for their Pipelines and Process Services division. The world of pre-commissioning and commissioning awaited! Engineering the jobs and then going out and actually doing them as the field engineer certainly taught me to be realistic when writing procedures from my (sort of) comfy desk, since I’d more than likely end up being the one out there doing it later.

Projects were varied, from 56” pipelines to intricate process pipework, from the searing summer heat of Qatar and Saudi to the freezing winters of Kazakhstan, from launching a single pig to campaigns of hundreds of pigs, it certainly wasn’t dull. And as anyone who’s worked in the field knows, the characters you meet along the way are equally colourful and interesting.

Back From the Field

Eventually, with a changing family life (kids on the horizon) and wanting to gain experience in different project phases, I returned to the UK and worked for JP Kenny as a commissioning engineer on Shah Deniz 2. As part of the systems engineering team, I was now influencing the subsea architecture design, making sure the required functionality was there both for pre-com, commissioning and future interventions in the operations phase. I was now interfacing with far more disciplines, learning their concerns and constraints along the way. One of these disciplines was Flow Assurance.

Making the jump to Flow Assurance with MSi Kenny (WoodGroup Kenny at the time) felt like going closer towards my Chemical Engineering roots. With the knowledge I’d picked up along the way I liked bringing the practical aspects back into the world of flow assurance, making sure that early development work always kept an eye on the realities of field operations. You could say I liked “keeping it real”. I’ll see myself out…

Taking off with AFS UK

At this point I joined AFS, helping Ian establish and grow our UK office, while being seconded into INEOS for the Clipper South project. This was not my first stint seconded on the client side of things, but what started off as a couple of weeks to review some tie-in options turned into 5 years supporting multiple projects and assets within INEOS. With a fast-paced environment, small but incredibly capable teams, a dynamic attitude and ambitious mindset, it was very much a “own it and deliver” environment. I quickly found myself back in the field, in an array of different capacities, once again seeing through engineered solutions all the way to installation, commissioning and operations.

Eventually I found myself back at Wood, this time as Project Manager and Engineering Manager. This time I was looking after some of their largest flow assurance projects for clients in the Middle East with teams of up to 15 FA engineers, often located in varying spots across the globe. This gave me the opportunity to further refine my team and project management skillset, within the framework of a now much larger organisation.

And more recently, Pontem’s draw of an exciting new challenge with respected friends and colleagues was too much to ignore. Bringing with me the knowledge and experience I’ve picked up on my 20yr-long journey, Pontem’s mission to turn data-driven insight into action and delivering solutions that work in the real world was absolutely the right fit. And I can keep on keeping it real. I’ll see myself out again….

So where do I see myself in 5 years’ time? By now I know better than to try and guess, but let’s get stuck in and find out!

The Late Life Question

Decommissioning is often viewed as a demolition job…

In reality, the real work starts years earlier in quiet data‑driven decisions that shape how that final chapter will unfold.

Right now, one question looms large:

Will wax build‑up in the pipeline become a problem before we shut down?

And, if so, what would that mean for decom, technically, economically and environmentally?

The Challenge

We have:

• A long subsea tieback.

• No pigging option.

• Declining rates that will eventually push arrival temperatures below the wax appearance threshold.

That’s a recipe for deposition. Without the ability to mechanically clean the line, we need to know, with confidence, whether wax will start to accumulate to the point of trouble before cessation of production.

A Hybrid Approach to Monitoring

We’ve paired two worlds:

• Physics‑based modelling: a thermal‑hydraulic model tuned to the asset.

• Machine learning: trained on hundreds of simulated cases and field data to recognize the “normal” behavior of the system.

By comparing predicted vs. actual temperatures and pressures, we can spot the earliest signs of wax long before they show up in raw field trends.

Think of it as a stethoscope for the pipeline: subtle changes in the heartbeat tell us when something is starting to build.

What the Data is Telling Us

So far, the model and the field data have marched in lockstep. As rates decline and arrival temperatures dip below the wax appearance temperature, we’re beginning to see the first small deviations — more pressure drop here, less cooling there.

We used these insights to run forecasted wax deposition cases, enabling an estimation of present and future wax levels.

The forecasts say:

• Wax has deposited and will continue to, but slowly…

• By the projected cessation date in a couple of years, the total volume is expected to be less than 1% of the pipeline’s capacity.

• The thickest deposits will likely be in short, removable sections near the riser.

These forecasts are built with multiple layers of conservatism, and can be continually updated as the field progresses and even more data is available. Resulting in a clear description and analysis that gives confidence that there is little operational impact to be expected.

Why That Matters for the Environment and Decommissioning Strategy

This is an environmental question as much as it is a flow assurance one.

When a pipeline reaches the end of its life, operators typically face two decom options:

Option 1: Produce Up to the Platform

• Flush the pipeline by continuing production until all fluids are pulled up to the topsides.

• Leaves the line clean, but at a cost.

• Requires extended production, more offshore time, and higher decom spend.

Option 2: Push Down into the Reservoir

• Instead of pulling everything up, fluids are pushed back down into the reservoir.

• This avoids the need for full pipeline flushing.

• Saves significant cost and offshore time, but may leave wax in the line posing environmental concerns.

That’s where our analysis comes in.

We needed to answer a critical question:

If we leave wax in the pipeline, how much will be there and will it pose an environmental or regulatory risk?

Our wax modeling has allowed us to forecast wax volumes, locations and growth rates with some clear results. These show that while wax is expected to deposit it is expected that the deposits are small (<2mm for the majority of the pipeline) and further are not expected to degrade or dissolve once production has ceased.

Under UK and OSPAR guidance, pipelines can be left in situ if residual materials pose no long-term environmental harm. Our analysis builds a strong, evidence-based case for that outcome.

We’re not on track to need an expensive, high‑risk cleaning operation. If the trend holds, the wax can be left in situ with minimal environmental impact, backed by a clear, evidence‑based case for regulators.

It also means we can focus decom planning on the things that will matter most: safe isolation, efficient removal, and minimising offshore time, instead of chasing a problem that isn’t going to materialise.

Lessons for the Future

Late‑life flow assurance isn’t just about keeping the wells flowing. It’s about understanding the system’s final years so you can make the right calls at the right time.

In our case, the hybrid monitoring approach:

• Helps avoid unnecessary intervention costs.

• Provides confidence in decom planning.

• Shows how physics and data science can work together to solve practical, end‑of‑life challenges.

Closing thought

Decommissioning isn’t just dismantling steel. It’s telling the story of an asset’s last years and making sure that story ends well. Right now, the data says the wax layer will stay small; the pipeline will be ready to rest easy when the time comes.

They offer a first impression—something that lets a company passively introduce themselves, tell their story, and share what they offer. But like anything, they can be done really well or really badly.

We were very aware of this when we started Pontem a few years ago. So much so that we purposefully chose to have as minimal a website as possible, hoping not to mess anything up on day one. We've even referred to our website as a "landing page."

Judging from the many people over the years who have asked us what exactly we do, we probably achieved that goal.

But there was real, thoughtful intent behind our early website approach. It was to give Pontem time to mature—specifically, time to properly answer the question we so often ask ourselves:

"Who do we want to be when we grow up?"

We started Pontem with the idea that building a team that is both highly experienced and knowledgeable, while also equipped with the latest technology, would be the best way to structure a consultancy aimed at truly adding value for its clients and solving difficult problems.

We now believe we're further along in knowing who we are and, more importantly, being able to explain and share it. We may not have everything figured out yet, and crafting a unified message about what your organization can do is always challenging, but the pieces have come much closer together.

For that reason, we decided it was time to polish up our website to better reflect who we are and what we offer...

Grand Reveal (v2.0)

Our new site is live now, at the same URL as before:

www.pontemanalytics.com

For a quick sneak peek without leaving our Substack, here are a few screenshots below.

What Makes Pontem Special

One thing we've always known, but that became even clearer during the discovery phase for this new site, is just how unique and special our team is. Here are a few stats that really drive this home:

• Size: We've added over 10 people in each of the nearly 3 years since we started and currently have over 30 people on the team

• Location: We have an office on every continent except South America and Antarctica (we're coming for those last two!)

• Languages: Collectively we speak over 15 different languages

To embrace our diverse backgrounds, locations, and team members, we put together the video below, letting some of our team introduce themselves in their native languages and share what they do at Pontem.

(And yes, asking a team of engineers to record themselves is one of the hardest tasks you can give them!)

In the oil and gas industry, reserves reporting is a critical process that quantifies a company's hydrocarbon assets - essentially their future production potential and economic value. These reports serve as the foundation for investor relations, regulatory compliance, and strategic decision-making, directly influencing market valuation and access to capital.

This is a strategic area where Pontem Analytics is expanding its capabilities, particularly through our new work stream focused on subsurface applications where we believe advanced data analytics can deliver significant value.

The Problem

Despite the importance of reservers reporting, most would agree that it remains stuck in the “spreadsheet era”.

Engineers and economists spend hours manually manipulating production, CAPEX, OPEX, cashflow, and NPV data across dozens of disconnected files. Traditional reserves workflows involve months of technical work, followed by the consolidation of outputs into spreadsheets and databases. These are then audited before being compiled into reports and visuals. Data is often sourced globally, arriving in inconsistent formats and frequently disorganized. These massive data cubes have to be distilled into something useful.

Turning this raw data into actionable intelligence is:

• Manual and repetitive

• Prone to version control issues

• Too slow to support strategic decisions

The value of reserves data should not be underestimated, but we recognize that the industry needs to shift from simply reporting on the data to using it to drive real outcomes: identifying additional barrels and unlocking cost savings. For that reason, we set out to build our own approach to handling this data and unlocking its value for one of our clients.

Building a Better Way

We approached this challenge by reimagining the entire reserves reporting workflow. Through our partnership with Databricks we were able to leverage modern data engineering principles and cloud-based analytics, to create a solution that automates the tedious parts while enhancing the strategic value of reserves data - transforming it from a compliance exercise into a competitive advantage.

Using Databricks and generative AI, we built a modern data pipeline and reserves intelligence dashboard that automates and streamlines the entire process.

The pipeline ingests raw production, CAPEX, OPEX, cashflow, and NPV data—cleans it, structures it, and prepares it for meaningful analysis.

The dashboard, built with Databricks BI and guided by AI prompts, visualises key trends such as:

• 📉 Declining production (BOEPD)

• 💰 CAPEX and OPEX

• ⚙️ OPEX per BOE

• 📊 Cashflow and NPV

It is fully interactive, filterable by asset class (PD, PUD, etc.), and updates automatically as new data comes in. The client has found the ability to analyze year-on-year changes particularly valuable.

Impact & Results

This solution delivered tangible benefits to our client, including several days of engineering time saved, enhanced accuracy, and clear, insightful visualizations. Beyond these immediate gains, the true value lies in transforming reserves data from a compliance requirement into a strategic asset that actively guides capital allocation, competitive benchmarking, and acquisition & divestiture decisions.

Engineers and executives can now quickly filter through reserve categories and analyze year-on-year changes to identify optimal areas for medium-term investment. The platform enables users to construct detailed asset-level views that track cashflow evolution and explore sensitivities around critical inputs - turning data into actionable intelligence.

Future Developments

We're extending this same data-driven approach to well performance monitoring, another domain currently constrained by spreadsheet limitations. Our upcoming Production Optimization Dashboard will deliver the same automation capabilities, analytical clarity, and decision-making support.

This represents more than just an aesthetic improvement to dashboarding - it's a fundamental shift in workflow: faster, cleaner, and purpose-built for decision makers who need insights, not just information.

If your responsibilities include reserves management, production forecasting, or upstream investment planning, consider the competitive advantage of having these capabilities at your fingertips.

Subscribe now

Find out more about methane hydrate gas extraction here!

JOGMEC / JMH

Our first day began with a visit to the JOGMEC offices for discussions on the optimisation of kinetic hydrate inhibitor (KHI) technology for methane hydrate extraction. Unlike conventional gas production, methane hydrate deposits present unique challenges—most notably, the absence of a free oil or condensate phase and the presence of large volumes of water (approximately 6 moles of water for every mole of methane…or ~859 bbl water for every MMscf of methane!).

To ensure KHIs perform optimally under these conditions, Pontem Analytics, Oilfield Technologies, the University of Western Australia (UWA), and JOGMEC are working together to ensure the best formulation is selected and the best application design is installed to minimise hydrate reformation risk during production and maximise uptime.

This collaboration brings together a team with distinct but complementary skill sets:

• Pontem Analytics' expertise in formulating and deploying KHI products, through Dr Pete Conrad and Stephen Hamilton.

• Oilfield Technologies' extensive experience in KHI performance testing.

• UWA’s deep knowledge of hydrate science, molecular chemistry and laboratory-scale testing.

• JOGMEC/JMH’s operational insight into the practical challenges of methane hydrate gas extraction.

The day’s productive and engaging discussions wrapped up with a well-earned dinner in Tokyo.

JMH

Our second day in Japan kicked off with a visit to JMH’s offices to discuss the sand control challenges of producing gas from methane hydrate deposits. Sand trapped within the deposit is loose and unconsolidated, presenting challenges from a subsurface (sand packing / collapsing) and production (erosion / equipment damage) perspective. Thankfully, Oilfield Technologies have the answer with a state of the art solids production test rig with the capabilities to test all forms of downhole sand control under downhole conditions. Producing real test data of the downhole sand control options, alongside Pontem’s ability to model erosion in complex pipework, allows for confidence in the final downhole sand control and well design.

Following on from these discussions, we recapped the flow assurance study performed earlier in the year and discussed the potential for more innovation and analysis going forward to ensure the next production test is successful.

Pete and I capped off the day with a well earned Snake sake. No, that is not a brand of sake, that is sake with an actual snake in it! We both felt a bit off the next morning….

INPEX

Our next day included a visit with friends at INPEX, Japan’s largest petroleum and exploration company. INPEX has plenty of experience handling traditional hydrates. Discovered in the Browse basin in 2000, INPEX developed the Ichthys with partners (including TotalEnergies and Osaka Gas), which manages hydrates using MEG. Designed for 100,000 bbl/d condensate and 9.3 MMt/y gas produced through a gas export line 890 km pipeline to an LNG facility in Darwin, Australia. (Interesting side note: Pete was part of an RFQ from INPEX in 2009 to examine whether LDHI may be a suitable option for hydrate management). First production began in 2018 and in just over a year later the 100th LNG cargo was shipped, which is quite impressive!

As part of INPEX’s commitment to providing sustainable energy to the Asia-Oceanic Region, they are partners in a number of other regional fields, including Santos-operated Coniston Van Gough and Woodside-operated Ravensworth fields. Additionally, INPEX is leading efforts to decarbonize many of their operations, including the Ichthys LNG project. With key infrastructure upgrades in the works, INPEX is planning to be CCS-ready utilizing technology to capture CO2 during liquified gas production, where they will compress, transport, and store in permanent, underground storage reservoirs.

Nippon Shokubai

Our final visit in Japan was with Nippon Shokubai, a specialist chemical company, whose products span a wide range of applications. Their company name literally translates to Japan Catalyst, and they began their impressive history perfecting the synthesis and production of phthalic anhydride, an important raw material used in coatings. Without being aware, many people use their technology today: the most famous example is the incredibly absorbent material used in diapers! Equally important, Nippon Shokubai is well-established in the Energy Sector. A few examples include:

• Electrolyte for Lithium-Ion Batteries: IONEL (LIFSI) (shameless Pontem plug for Li-extraction roll-up post we did: https://dataanddiscipline.substack.com/p/lay-the-smack-down)

• Scale Inhibitors for drilling

• Additives for drilling mud

We needed to travel from Tokyo to Osaka, about 500 km (310 miles) away. This isn’t a problem thanks to the Tokaido Shinkansen line: we made the jump in just under 2-1/2 hours! Team McLaren might laugh at my speedometer app image, but it’s the fastest I’ve travelled on land.

Nippon Shokubai took the time to show us around their laboratory and research facilities to highlight their world class capabilities in chemical development. It was a great opportunity to share experience in chemical manufacturing and performance testing, and the potential for Nippon Shokubai to expand their chemical portfolio further into the oil and gas space.

Pontem would like to thank JMH, JOGMEC, INPEX, and Nippon Shokubai for their hospitality whilst in Japan. Furthermore, a massive thank you to Katsuei-san for looking after us and making sure we only ate the best Japanese cuisine!

1 —> Latest is NOT always the greatest.

2 —> All models need to be benchmarked and validated.

Background

One of the core responsibilities in flow assurance and production engineering is the ability to accurately model field behaviour, whether it’s a brownfield or greenfield development. These models help predict and prevent issues like slugging, hydrate formation, wax deposition, and excessive pressure drops, while also supporting system design (pipelines, separators, pumps) and troubleshooting by recreating real operating conditions. In essence, we aim to replicate physical reality in a controlled environment to enable informed, proactive decisions throughout a field’s lifecycle.

Most oil and gas fields today involve multiphase flow, a mix of oil, gas, water, and occasionally solids. Accurately simulating this requires robust transient flow simulators, with OLGA (developed by Schlumberger, originally by SINTEF and IFE) widely regarded as the industry standard.

OLGA’s strength lies in its decades of development and validation, making it a trusted tool for design and operational studies. While competitors like LedaFlow and in-house simulators offer niche advantages, OLGA’s first-mover status, reliability, and wide adoption keep it at the forefront of the industry.

Regardless of how advanced or well-validated a simulator is, achieving a perfect match between model predictions and field behavior rarely happens on the first try. This mismatch can stem from a wide variety of factors, not all of which are related to the simulator itself. Some of the most common causes include:

• Uncertainties in the produced fluid

• Changes in actual field conditions

• Assumptions made during model setup

• Instrument calibration or data integrity issues

• Simplifications of system geometry or topology

• Forecast inaccuracies

• and of course human errors

These challenges highlight an important point, that most of the people are missing:

Like all software, OLGA has gone through regular updates over the years. These updates are driven by feedback from operators, academia, and lab validation studies. As we touched on briefly in our previous post on this topic (link below), staying up to date with the latest version of OLGA, and understanding the implications of each update, is crucial for maintaining modelling accuracy and reliability. In this article, we’ll discuss a little more closely some of those updates and finally how we have seen their impacts play out in real-world examples.

A decade of flow: How OLGA has evolved

As mentioned in the introduction, continuous software development is essential to improving the accuracy and reliability of flow simulations. OLGA, has consistently delivered annual updates, enhancing core functionality, adding new features, and fixing critical bugs.

From practical experience and benchmarking studies, we’ve observed that OLGA generally performs very well in single-phase and oil-dominated systems (separate post to follow focused on oil dominated systems). As an example, you can see the inlet pressure comparison between the oil field and OLGA simulations (over last 2 years) from work we did just last week…

However, its performance becomes more variable in gas condensate systems and low turndown production scenarios, some of the most complicated scenarios for multiphase fluid dynamics.

Over the past decade, specific updates have aimed to address these limitations, advancing OLGA’s ability to replicate complex field phenomena and reducing the need for excessive manual tuning. The following is a curated summary of key model-relevant updates made between OLGA 2015 and OLGA 2025.

2016–2019 and 2021-2024: “Steady progress”

During these years, OLGA saw a number of incremental fixes and technical enhancements. However, from a model benchmarking and field matching perspective, none of the changes were groundbreaking. They included general improvements in numerical stability, convergence speed, GUI usability, and bug fixes related to specific edge cases, but these had limited direct impact on core flow predictions specifically for benchmarking.

2020 OLGA Update: “Major improvements”

The 2020 release marked a significant turning point, especially for challenging applications like gas condensate flow systems and pigging simulations. Several updates stood out for their direct impact on matching field data more accurately:

1. New Apparent Liquid Film Wall Roughness Model

This update was particularly impactful for pressure drop prediction in low-liquid loading flows, a well-known challenge in gas condensate pipelines. Previously, OLGA tended to underpredict pressure drops at high gas rates in gas condensate systems. To compensate, users often manually increased pipe wall roughness, an artificial fix that lacked physical basis.

With OLGA 2020, a more physics-based approach was introduced:

• The HD stratified flow model was updated to include a new closure for the effective film roughness generated by the thin liquid film creeping up the pipe wall at high gas velocities which was validated against extensive experimental data from SINTEF and IFE laboratories, showing far better agreement for pressure drop prediction in gas-condensate systems.

  
  For the technical audience, the equations added for the effective film roughness look like follows (contact us if you want to know what each variable is, and how they work)

  

• The mechanism now considers turbulence-driven film spreading as a function of gas rate and film viscosity.

2. Enhanced Pigging Model

The pig model saw also major refinements:

• Enhanced pigging physics: Improved handling of holdup fronts, fluid segregation, flash behavior, and leakage scenarios.

• Greater numerical stability: More robust simulations under transient conditions, with better physical realism.

3. Slug Initiation and Liquid Accumulation Enhancements

• Introduction of a new slug initiation model, improving the prediction of slug formation in stratified and transition flow regimes.

• Better modelling of water accumulation at low flow rates and gravity-dominated flow, common conditions in long, horizontal flowlines with liquid dropout risk.

2025 OLGA Update: “Further refinement”

The 2025 release introduced targeted improvements that continue the work begun in 2020, further improving simulation of stratified and dispersed flows in multiphase systems:

1. Advanced Closures for HD Stratified Flow Model

• New closures were implemented for turbulence parameters, capturing the apparent eddy viscosity at the gas-liquid and liquid-liquid interfaces. OLGA keeps the actual expressions used proprietary (apologies for the curious people!)

• This helps simulate the momentum exchange across interfaces more accurately, especially in complex gas condensate environments where stratified layers and internal mixing strongly affect dP and phase separation.

2. MEAN Slug Tracking Model

• A new statistical slug tracking mode was introduced.

Over the past decade, OLGA has taken major steps forward in improving simulation accuracy for challenging systems, particularly gas condensate and low liquid loading environments. The updates in 2020 and 2025 represent key milestones in reducing the mismatch between modelled and actual field behaviour, and in minimizing the need for arbitrary tuning.

Enough of theory…. Let’s go ahead and have a look on how OLGA compares to the field throughout the years and how that compares to the required tuning/benchmarking.

Model vs. Reality: What have we seen at Pontem?

With some of the history and theory behind us, let’s look at how these updates have actually played out in terms of matching field data through a few examples.

“Field A”: Subsea-to-platform tieback in a deepwater basin

This field involves a deepwater subsea development producing a lean gas-condensate fluid, with long multiphase transport lines connecting subsea wells to a fixed platform. The system is known for low liquid loading and stratified flow regimes, particularly under turndown or early-life conditions.

Key features:

• Long subsea flowline (~120 km)

• Dry gas with small volumes of condensate and water under operating conditions, and continuous MEG injection.

This field model was originally developed in OLGA 2015, during the pre-production phase of the asset. Once the field came online, we conducted benchmarking against actual pressure drop measurements. By adjusting the wall roughness from 45 to 90 microns, we were able to achieve a great match with field data (±4%) across key operating points. Given the model’s strong performance in both steady-state and transient scenarios we chose to retain the model in its original version for ongoing analysis.

However, in recent sensitivity runs using OLGA 2021 and the default roughness of 45 microns, we observed a consistent over-prediction of pressure drop compared to both field data and the 2015-calibrated model. This trend persisted with OLGA 2025, although at higher flowrates, the predicted pressure drop began to decrease, eventually aligning more closely with older results. This behaviour is consistent with patterns noted in our previous Substack post (“Flowing Uphill in Tanzania”), where newer OLGA versions tend to predict higher pressure drops under gravity-dominated, low-liquid loading conditions, and lower pressure drops at high flowrates, compared to earlier versions for gas condensate systems.

This observation supports a hypothesis that recent updates to the effective wall roughness and liquid film modelling may be overcorrecting for stratified flow behaviour in very lean gas condensate systems, particularly those with condensate-gas ratios (CGR) below 1 stb/MMscf. It's important to emphasize that this effect appears limited to extremely low CGR systems.

“Field B”: Large-scale gas condensate development

This field is part of a large regional gas export project, with rich gas-condensate production transported over significant distances to an onshore processing terminal. Offshore-subsea pipeline, same region as the Field A, with the main differences being the different diameter and higher CGR.

Key features:

• Long (~130 km) multiphase pipeline to shore

• Low CGR gas, with condensed water under operating conditions including continuous MEG injection.

Like Field A, this model was developed during the mid-2010s when OLGA 2015 was the latest available version. Once the field entered production, the model was benchmarked against field-measured pressure drops across a range of flow conditions.

In its initial state, using the default wall roughness of 45 microns, the model under-predicted pressure drops, particularly at high flowrates (>20%). To achieve an acceptable match with observed field data, the roughness had to be increased to approximately 380 microns! While this value may initially seem unreasonably high, similar tuning has been observed in gas condensate systems, where complex flow regimes and thin film dynamics affect apparent friction far more than in oil-dominated systems.

Subsequent sensitivity analyses with OLGA 2021 and 2025, using the default 45 micron roughness, showed an improvement in pressure drop prediction at high flowrates compared to OLGA 2015. Both newer versions showed better alignment with the field at the upper end of the operating envelope, reducing the pressure drop deviation to around 10%, without any roughness tuning.

As observed in Field A, the trend between OLGA 2021 and 2025 was consistent:

• OLGA 2025 overpredicts dP at low flowrates more aggressively than OLGA 2021.

• At high flowrates, OLGA 2025 tends to predict lower dP than 2021 .

The match at low flowrates worsened in OLGA 2025, with significant over-prediction of pressure drop. However, it’s important to note that field data at these lower rates was sparse and potentially unsteady, so it's possible that the flow regime was still transitioning and additional liquid holdup was present.

Despite the improvements in OLGA's core physics, achieving a near-perfect match with field data in this case still required tuning the pipe roughness up to ~380 microns, similar to the original 2015 model. This suggests that, in very lean systems, the apparent liquid film now dominates the wall friction contribution, effectively reducing the influence of actual steel pipe roughness. As a result, even though OLGA's default predictions have improved, they still fall short of fully eliminating the need for calibration in complex gas condensate flow scenarios.

“Field C”: Long offshore tieback to onshore terminal

This mature gas-condensate field involves low-pressure wells tied back to an onshore terminal. The pipeline was designed to high flowrates, however, the field is now under very low flowrate production and is looking for either onshore compression or new tie-ins to increase the flowrates.

It operates in the lower flow envelope, making it particularly sensitive to liquid holdup, pressure drop estimation, and transient accumulation.

Key features:

• Declined pressure and production rates (<8 MMscf/d)

• The pipeline is >80% liquid filled.

This field presented a particularly challenging modelling environment, primarily due to the fact that it is operating well outside its original design envelope. While there are ongoing efforts to tie in additional production and implement onshore compression, the immediate objective was to benchmark the existing model and use it to support forward-looking assessments under current conditions.

The original model, built using OLGA 2017 by a third party, was provided to our team as a baseline. In practice, the model output aligned closely with earlier OLGA 2015 results.

In most systems, pipe wall roughness is a reliable tuning parameter for aligning model-predicted and field-measured pressure drops. However, this field was operating in a region where gravitational pressure drops dominated, rendering frictional adjustments far less effective.

The breakthrough came when we turned our attention to the pipeline bathymetry. In gas condensate systems, especially at low flowrates, liquid accumulation is highly sensitive to even small elevation changes. Upon reviewing the original elevation profile, we decided to replace it with as-built bathymetric survey, discretized using industry-standard methods. The result was an immediate improvement in pressure drop prediction, driven by a more accurate representation of liquid holdup distribution throughout the system.

With this improvement in place, we explored whether newer OLGA versions, specifically post-2020 releases, could further improve model accuracy. Upon switching to OLGA 2021, and later to OLGA 2025, we observed incremental but meaningful improvements in pressure drop prediction resulting in less than 10% deviation from field data.

This case reinforces two important conclusions:

1. The hydraulic enhancements introduced in OLGA 2025 appear to benefit gravity-dominated gas condensate systems, improving dP predictions at very low flowrates.

2. Accurate pipeline elevation data and proper spatial discretization are essential for valid model results in such regimes—yet often remain an overlooked source of error in legacy models.

“Field D”: UKCS subsea development with high CGR

This field consists of a UK North Sea gas condensate development tied back to a central processing facility. It operates with relatively high CGR (~40-60 stb/MMscf), with some wells producing formation water and continuous MEG injection.

Key features:

• Moderate-length subsea tieback

• High CGR, moderate WGR and continuous MEG injection

The examples presented in this study were intentionally selected to reflect a diverse range of operating conditions, covering different production modes, gas-liquid ratios, and flowrate regimes. This diversity allows for a more comprehensive evaluation of OLGA's predictive performance and the impact of its model improvements across various system types.

What made this Field D particularly interesting is that it featured relatively high liquid flowrates for a gas condensate system, placing it hydraulically between a conventional gas condensate and a light oil system.

The figure below compares the model-predicted pressure drops using OLGA 2015, 2021, and 2025 against actual field data across several flowrates. While the final model used for all simulation work was based on OLGA 2025, mainly due to other modeling requirements, the comparison offered valuable insights.

Interestingly, at higher flowrates, the 2015 and 2021 models appeared to match field pressure drops slightly better than the 2025 version. All three versions, however, produced results within an acceptable margin of error, indicating that the core model physics were generally sound across the board.

One key observation was that OLGA 2025 tended to slightly under-predict pressure drops at higher flowrates. While the difference was not significant enough to undermine the model's utility, it highlights how changes in turbulence effects modeling can affect pressure drop accuracy in transitional systems.

Final thoughts

Overall, OLGA continues to improve with each release, incorporating valuable enhancements, bug fixes, and feature updates that enhance its ability to replicate field conditions and support engineering decision-making. As a general principle, it is recommended to use the latest available version, especially when starting new studies, as the underlying physics and numerical stability are continually refined.

In the context of hydraulic pressure drop prediction, two major updates over the past decade have significantly influenced OLGA’s performance in low-liquid loading gas condensate systems:

• 2020 – Introduction of the apparent liquid film roughness model, aimed at improving predictions in annular and stratified flow regimes.

• 2025 – Enhancement of the HD stratified flow model through updated turbulence closures, specifically by implementing scaled apparent turbulent eddy viscosity terms on both sides of the gas-liquid and liquid-liquid interfaces.

Despite these improvements, there is still room for refinement. Our benchmarking results suggest that OLGA 2025 tends to over-predict pressure drops at low flowrates, where gravity effects dominate, and under-predict at higher flowrates, compared to earlier versions. In contrast, OLGA 2020–2024 versions generally provided more balanced predictions at higher rates, often more consistent with observed field data across a range of gas condensate systems.

Importantly, regardless of software version or theoretical improvements, all models must be rigorously benchmarked against actual field data once it becomes available. No software update can substitute for calibration using real operating conditions. A model is only as good as its assumptions, and even the most advanced simulator will diverge from reality if it’s not grounded in high-quality, field-verified inputs.

As OLGA continues to evolve, understanding how each version behaves, and validating it against real systems, remains essential to producing reliable, fit-for-purpose simulations.

Subscribe now

Trivia Question: Since 2010, what is the largest 6-month drop in WTI oil price?

You may be inclined to say the COVID drop of March 2020. Depending on your math, you could be right (there is clearly a 6-month window, ending 20-April at -$39/bbl, that would be pretty miserable). But, a relatively quick snapback mutes that argument somewhat. From a sustained downward trend perspective, this belongs to late 2014 / early 2015, where oil steadily slid from $105/bbl to $40/bbl (and further into sub-$30 territory the following year). That is where this story starts. Maybe not the ideal time to go into debt and try a startup.

Ten years ago this week (July 2015), three important events happened that impacted the career trajectory of several of us now at Pontem:

• We signed paperwork to finance our goal of building a world-class fluids testing laboratory at Assured Flow Solutions (now SPL).

• The SBA 7(a) program that we used ran out of money (SBA 7(a) Program Runs Out of Money), over the very next weekend, and most banks stopped lending to anyone in the ‘oil/gas space’.

• We broke ground on the laboratory in a then-empty grassland in Katy, TX.

From our Grand Plan to our Grand Opening, oil had lost more than $85/bbl. In that price climate, we were either stubborn, stupid, or sage. Probably all of them.

We’ll take an interesting BTS look at some of the construction and build-out. From the groundbreaking, it was the usual scramble to get the lab built (a) on-spec, (b) on-budget, and (c) during hurricane season.

Fun Fact: The plot of land we secured was effectively ‘No Man’s Land’ - an oddly shaped piece of property that nobody could really figure out what do with. Sandwiched between a greenway, it presented a problem for the developer as to what could fit. Enter our vision for a stand-alone lab and a willingness to be first. And a willingness to play the long game. It allowed us to move quickly, along with a motivated builder, to see a unique opportunity where most did not.

Its likely that many who stepped foot in the laboratory had not been witness to the origin story, both for the lab and the area generally. It used to be a much quicker/easier to drive down that corridor of Grand Parkway (99).

Driving by today - filled with business buildings and apartment complexes - hard to remember how ‘empty’ this part of Katy was when we set out to build the facility. When we built the lab, we tried to merge two theories:

• To truly understand field design, need to understand the fluids being produced.

• To truly characterize the reservoir fluids properly, need to understand how the data will be used in operations.

Building the shell was only the first step, actually sourcing the equipment (from different providers globally) and getting the interior set-up was a bigger undertaking. The lab was designed with safety as Priority #1, but with the ability for the engineers and scientists to work comfortably. Significant ‘interior decorating’ to get the air exchanges / climate control, test result visualizations, sample storage, and many other amenities were incorporated to turn the lab into a world-class facility, which can be rare for most oilfield service labs. In fact, the air exchange was so significant our smart watches would register flights of stairs climbed when we’d go in and out of the laboratory. Our Garmin profiles looked like we climbed K2 daily!

The lab rounded into shape over the 6-month construction process and by December 2015 we were running our first projects (a best-in-class asphaltene solvency qualification test and hydrate / LDHI qualification, ironically both for fields which Pontem is still engaged with today, 10 years later. Told you, long game).

During COVID, we received emergency use authorization to keep the lab (and the company) open and remain in business. We used the opportunity to make and distribute free hand sanitizer, considering we had all of the equipment available.

After emerging from COVID, there was a moment when we thought the lab burnt down (Oct ‘20). Just missed us (pictures taken from the lab across a field that no longer exists), made for a harrowing few minutes on the drive in. The lab was many things at times - enabler, differentiator, financial albatross, epicenter, frustrating, amazing, safe haven, and complicated. And almost an insurance claim (which, post COVID at times, was nearly wishful thinking!)

While the name on the building may have changed, the quality of testing carried out inside has remained first-class.

We knew when we opened our doors the initial design for our Deepwater testing facility would change. We may have been foolish enough to do all the above during the biggest downturn but we weren’t so naive to think we had installed every capability we’d need up-front. We left roughly 1/3 of the space as a warehouse knowing we’d grow into it and there were bespoke tests to develop, based on clients’ needs. And that’s what happened.

Within 5 years our bench tops were completely filled with equipment and new testing wet bench spaces and custom equipment we designed / built. The space pictured above would be filled out with additional office space, autoclaves, and a fabrication center. We were definitely building the plane as we flew it, but we held true to our vision, which we knew was serving a need for the industry. Even today, another one of the fundamental premise of the lab remains true:

• In a high commodity price environment, lab testing provides additional assurance to make sure our designs are robust to future changes and can accelerate field development.

• In a low commodity price environment, lab testing provides a way to optimize chemical spend and get a short-cycle ROI on reducing OPEX.

The same holds true for other initiatives (including the latest hot trends in data/AI): the ideas need to strike the right balance of where you can extract value (for service provider AND operator) across a range of commodity outcomes.

While the names and faces have changed over time - now currently run under the direction of former AFS staff - the lab stands as a testament to follow-through on a good (and risky) idea: that understanding fluids (and getting your hands dirty) was an enabler to good engineering and good field operation. We at Pontem continue this ethos today - frequently collaborating with the AFS/SPL staff on testing - to bridge the gap between all data (including lab data) and discipline.

Subscribe now