Oil Simulation, Climate Modeling and the Scientific Method

Subtitle: Garbage In, Consensus Out, Part II

Part I concerned a WSJ essay by Robert J. Caprara, “Confessions of a Computer Modeler”. In Part II I will share what I know about computer modeling in oil and gas applications, and raise questions about climate modeling.

This being a long post, for your reading enjoyment this holiday weekend. I’ll start with the conclusions so we can see where we’re going…


As an oil company engineer, I’ve got a life-sized picture what would have happened if I had:

  • Consumed significant resources studying and modeling a reservoir system;
  • Spent years convincing management of the model’s validity and the dire consequences of ignoring its warnings;
  • Proposed massive investment based on the model’s conclusions.

Then, when observations deviate significantly from the model’s forecasts:

  • Failed to update the model to match observations;
  • Fabricated novel and unprovable explanations of why it was wrong;
  • Told my bosses that I didn’t understand why everyone put so much stock in these models — after all, we understand the “basic physics” –

– I would have been out of a job, that’s what.

As we shall see, there are significant parallels between the type of models used in the petroleum industry and in climate science. A big difference is the money involved: while we’re talking millions to hundreds of millions of dollars in private funds in oil and gas, tens to hundreds of billions of public funds may be required to enact climate “solutions”.


Image from International Reservoir Technologies, Inc.

Modeling Oil and Gas Reservoirs

Disclaimer: No one would hire me to design a model: it’s not my area of expertise. But as a technical manager for an oil company, models have been prepared by others under my direction and supervision. My role requires enough understanding of the process to know its limitations and to ask intelligent questions of the experts, and to make business judgments based on the results.

The goal of modeling is accurate forecasts of future behavior. Reservoir simulations may be built to understand how many wells may be required to efficiently drain a reservoir, or how to enhance recovery with oil with water injection. Without a means of modeling different scenarios, the engineer must resort to guesswork; with sometimes hundreds of millions of dollars at stake, guesswork is “sub-optimal”.

Here’s a concise description of the process [We'll discover the source of this description when we change the subject to climate modeling, in due time. - Ed.]:

Reservoir models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To “run” a model, scientists divide the reservoir into a 3-dimensional grid, apply the basic equations, and evaluate the results. Fluid flow models calculate pressure,  fluid movement,  hydrocarbon phase behavior, fluid saturations, and mass balance within each grid and evaluate interactions with neighboring points.

(In the interest of readability, I’m going to move a discussion of what goes on in a reservoir simulation to the *Appendix below…)

History match. After the model is built based on everything known about the physical system, the geologic, rock and fluid properties will be tweaked and tuned to achieve a “History Match”: at that point the model’s output — predominantly its oil, gas and water production and pressure — matches as closely as possible to the actual observed history. The model is deemed to be an acceptable description of the observable reservoir system. At that point, the model can be switched to predict future production/pressure performance: “Forecast Mode”.

That’s when all Hell breaks loose.

Let’s say our original model included production data and pressures up to the end of 2013. We’ve just spent the first six months of 2014 building a beautiful computer representation, and tweaked it until every single bobble and wobble in the data was honored. But according to the prediction, we should have produced 200,000 barrels of oil in 2014 but only 150,000 have been sold down the pipeline! That’s a $5 million bust miscalculation, so far, not to mention how far our projections may be off in 2015, 2016 and beyond.

At this point the budding oil and gas reservoir modeler learns the first general truth about modeling:

There is no such thing as a unique history match.

History matches can be deceptive. An estimate that’s too high on one parameter may be offset by a guess that’s too low on another, so that the errors offset each other. A good history match is the classic case of confirmation bias. The modeler has worked so hard and made so many tweaks and everything fits just so! How could it have lied to us?!

Which leads us to Modeling Lesson #2:

Mother Nature is a bitch.

But none of this means the model is necessarily a bad one. There is an important clue in the new data, the “actual” data that conflicts with the model’s projections. In our example, there are six months of new “history” that now needs a new history match, and to do it we’re going to have to tweak parameters again. Then make a new projection.

And wait another period of time and do the whole process over again. Cycle, rinse, repeat. With each update, the model should be converging on better and better depictions of reality, and providing more accurate forecasts. That should mean the model is improving in quality, and along with it, a better understanding of the physical system.

Dammit, Jim, I’m an engineer, not a climate scientist

That being the case, let’s look at what Wikipedia has to say about Climate Modeling:


Credit: Wikipedia Commons.

Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To “run” a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points.

Sound familiar? I lifted this passage from Wikipedia’s Climate Modeling page for the description of petroleum reservoir simulation that I used above. By swapping the bolded climate terms for their petroleum analogs, the description works perfectly for reservoir modeling.

A geologic problem should be easier to model than the global climate system for a couple of reasons. For one, the physics of fluid flow in a reservoir is easily understood and there are relatively few variables. Second, at time zero, a hydrocarbon reservoir is static and at equilibrium. We can describe original conditions, and they were unchanged until production changed the equilibrium. With global climate, what is time zero? What is “normal” average temperature?

In June of 2013, Dr. Roy Spencer compiled temperature projections from 73 climate models, and compared those forecast values to actual observations (circles and squares in the graph below; large scale version here):

Dr. Spencer wrote:

In my opinion, the day of reckoning has arrived. … The discrepancy between models and observations is not a new issue…just one that is becoming more glaring over time.

It will be interesting to see how all of this plays out in the coming years. I frankly don’t see how the IPCC can keep claiming that the models are “not inconsistent with” the observations. Any sane person can see otherwise. …

Hundreds of millions of dollars that have gone into the expensive climate modelling enterprise has all but destroyed governmental funding of research into natural sources of climate change. For years the modelers have maintained that there is no such thing as natural climate change…yet they now, ironically, have to invoke natural climate forces to explain why surface warming has essentially stopped in the last 15 years!

You’d think that a committed climate modeler would want to improve both his model and his understanding of the physical system by updating the model, tweaking its parameters and various sensitivity factors, to more closely fit observations.

You’d think so, but you would be wrong. Because that would lead to a much less dire prediction of global temps in 2050 and beyond. And without those dire predictions, there may be no need to abandon fossil fuels in the developed world, no need for massive government control of the energy sector, no need for Progressives to save us from ourselves and take over the nasty job of running our daily lives.

Instead, climate scientists find new mechanisms to blame for all that missing heat that the CO2 must have trapped (example: “hidden heat” in deep oceans postulated by Kenneth Trenberth) while proclaiming “… the underlying science has not changed”.

Another strategy recently came to light: that of denying that Global Climate Models (GCM’s) are all that important … these same climate models that they have been clubbing the public and politicians with for a generation to try to shape policy. This, from Richard Betts, a climate modeler and one of the lead authors of the Intergovernmental Panel on Climate Change’s 5th Assessment Report:

…  I am slightly bemused over why you think GCMs are so central to climate policy.

Everyone* agrees that the greenhouse effect is real, and that CO2 is a greenhouse gas. Everyone* agrees that CO2 rise is anthropogenic. Everyone** agrees that we can’t predict the long-term response of the climate to ongoing CO2 rise with great accuracy. It could be large, it could be small. We don’t know. The old-style energy balance models got us this far. We can’t be certain of large changes in future, but can’t rule them out either. So climate mitigation policy is a political judgement based on what policymakers think carries the greater risk in the future – decarbonising or not decarbonising. [* and ** - see link. - Ed.]

*Appendix – A more long-winded detailed discussion of what goes on in a reservoir simulation

What exactly is being modeled? Simply, what comes out of the reservoir as production of oil, gas and/or water; what goes into the reservoir, typically water, either from natural influx or injected in wells; and the pressure inside the reservoir. As wells produce, the pressure will decline, meaning the system is losing the energy required to drive continued production. We are keenly interested in how fast it will decline with continued production, and how fast production will decline in the future. These are the key questions.

How is the model built? In a computer, naturally, but it can be represented graphically (see above). The first step in modeling a reservoir is to represent its geometry (geologic structure and boundaries). Think of a set of Legos™. Each one can have unique dimensions (height x width x length), as well as other properties (such as porosity, fluid transmissibility, fluid saturations, pressure, etc.) Each Lego is connected to its neighbors and can be affected by changes within them. The properties of the rock and the fluid systems are also described in terms of their behavior with changing pressure and temperature.

Certain cells in the model represent wells, where fluid is removed as “production” from the model. The model is constructed with as much detail as is available about the oil, gas and water withdrawal history of the reservoir, and the corresponding pressure measurements over time.

How does the model run? A software “clock” ticks forward in time increments, or steps. Beginning with time zero at equilibrium, production is “withdrawn” from the model’s well cells in each time step corresponding to actual production records. That’s when the software starts to do its magic. On the fly, the software honors physical constraints (gravity, fluid flow laws, conservation of mass, etc.) to calculate the interaction of every cell in the model, how fluids move within the model, and to what extent pressure decreases. It calculates all those interactions for every cell in the model; when finished, it clicks forward another time step. More production is removed, and the process is repeated. And repeated. And repeated, until time = the end of “history”, a/k/a the present.

There has never been a reservoir simulation of any scope that got it all right the first time. There are simply too many important factors in the system which cannot be known with sufficient precision to build an accurate model on the first attempt. To achieve the best possible match between the model’s behavior and that which has been observed, the modeler may have to change the geologic model, increasing or decreasing its volume, including the size of the water-bearing aquifer which provides energy to help “push” the oil out; changing connectivity of cells within the model (maybe geologic barriers, like faults); changing assumptions of rock and fluid compressibility, which can also be important drive mechanisms; changing fluid properties like viscosity and gas/oil interactions, etc., etc.

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I miss Breaking Bad.


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Numerically-challenged press release from Dept of Interior #rsrh

The headline is right, but the first paragraph is wrong: “$109,951,644 million” would be an amount 6 times the national debt, or approximately what the projected debt will be at the end of the Obama Administration. Further proof that those “-illion” words and number concepts cause journalists and bureaucrats problems. N.B. This press release has been live on their website since Wednesday.

$110 million is a pretty anemic lease sale. Single tracts have gone for more in the past.

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A new twist on an old grade school science project

Steve Maley:

This why we don’t have to worry about Peak Oil: at some point, price and necessity drive innovation*. It has been that way since the days of Thomas Malthus.

*Government cannot and will not mandate innovation. They may be helpful in funding basic science, but as currently executed government subsides and maintains the status quo. That works counter to the creative destruction necessary for transformational change.

Originally posted on Watts Up With That?:

From Stanford University something familiar to most anyone who has taken science – electrolysis of water into hydrogen and oxygen.

Stanford scientists develop a water splitter that runs on an ordinary AAA battery

Stanford scientists have developed a low-cost device that uses an ordinary AAA battery to split water into oxygen and hydrogen gas. Gas bubbles are produced from electrodes made of inexpensive nickel and iron. Credit: Mark Shwartz/Stanford Precourt Institut for Energy

In 2015, American consumers will finally be able to purchase fuel cell cars from Toyota and other manufacturers. Although touted as zero-emissions vehicles, most of the cars will run on hydrogen made from natural gas, a fossil fuel that contributes to global warming.

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Top 10 reasons the new WordPress Beep Boop Boob editor is a stunning failure.

Steve Maley:

I associate myself with the gentleman’s remarks.

Originally posted on Watts Up With That?:

(WUWT readers, please excuse this distraction while I holler at WUWT’s hosting provider, wordpress.com. As Willis would say, “my blood is mightily angrified”.)

I have generally been supportive of most wordpress.com upgrades, for example the recent upgrade to allow the top editor bar to float with scrolling is a HUGE time saver.

Unfortunately, the new Beep Beep Boop “upgrade” is a crash-and-burn moment in user interface design.

Top 10 reasons the new WordPress Beep Boop Boob editor is a stunning failure.

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Garbage In, Consensus Out: Part I

In the July 8 Wall Street Journal, Robert J. Caprara describes the process of computer modeling, and the motivations of the modeler. He was a consultant charged with building a detailed computer model of the nation’s fresh water sources, including drinking water intakes and sewage discharges. He tuned and tweaked the model, and was happy with his preliminary conclusion: the EPA program he had been asked to study had reached a point of diminishing returns and should be wound down.

Confessions of a Computer Modeler (Paywall)

Any model, including those predicting climate doom, can be tweaked to yield a desired result. I should know.

When I presented the results to the EPA official in charge, he said that I should go back and “sharpen my pencil.” I did. I reviewed assumptions, tweaked coefficients and recalibrated data. But when I reran everything the numbers didn’t change much. At our next meeting he told me to run the numbers again.

After three iterations I finally blurted out, “What number are you looking for?” He didn’t miss a beat: He told me that he needed to show $2 billion of benefits to get the program renewed. I finally turned enough knobs to get the answer he wanted, and everyone was happy. …

I realized that my work for the EPA wasn’t that of a scientist, at least in the popular imagination of what a scientist does. It was more like that of a lawyer. My job, as a modeler, was to build the best case for my client’s position. The opposition will build its best case for the counter argument and ultimately the truth should prevail. …

Surely the scientific community wouldn’t succumb to these pressures like us money-grabbing consultants. Aren’t they laboring for knowledge instead of profit? If you believe that, boy do I have a computer model to sell you.

A terrific op-ed; you should read it all if possible.


Image from International Reservoir Technologies, Inc.

Modeling Oil and Gas Reservoirs

Reservoir modeling per se is not my area of expertise. In my capacity as a technical manager for an oil company, models have been prepared by others under my supervision. Basically, I didn’t run the software, but I needed enough of a working knowledge of the process to be able to understand it and ask intelligent questions. 
The goal of modeling is to create a numerical representation of a reservoir that honors what is known about the system to be able to make accurate forecasts about its future behavior.
The first step in modeling a reservoir is to represent its geometry, as shown above. Each cell in the representation is represented by descriptive parameters, such as volume, pressure, porosity, fluid transmissibility and fluid saturations. As production is “withdrawn” from the model in each time step, the software honors the physical constraints (gravity, fluid flow laws, conservation of mass, etc.) to calculate the interaction of every cell in the model, how fluids move within the model, and to what extent pressure decreases.
The next step in the modeling process is achieving an acceptable “history match”. Through the production phase, withdrawal of fluids (oil, gas and water) have been carefully recorded over time, along with reservoir pressure. What follows is a tedious process wherein various parameters are tweaked and tuned so that the model’s performance is the same as actual observation. Only when close agreement has been achieved between field performance and the model can the modeler proceed to the next step: prediction.
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Bill Nye @TheScienceGuy and Al Gore, ‘not even wrong’ on CO2 ‘Climate 101′ experiment according to paper published in AIP Journal

Steve Maley:

Another instance of “fake but accurate”: a case study in confirmation bias. Bill Nye and AlGore foist a “proof” of Global Warming’s key mechanism on the rubes, but the underlying physics of the experimental setup is shown to be flawed.

Originally posted on Watts Up With That?:

From the department of  “I told you so and I have an experiment that precedes this to prove it” comes a paper that proves Bill Nye’s faked ‘greenhouse effect’ experiment is also based on the wrong ‘basic physics’. Remember when I ripped Bill and Al a new one, exposing not only their video fakery, but the fact that experiment fails and could never work? Well, somebody wrote a paper on it and took these two clowns to task.

The Hockey Schtick writes:

Oh dear, the incompetent & faked attempt by Bill Nye to demonstrate the greenhouse effect for Al Gore’s Climate “Reality” Project has also been shown by a peer-reviewed paper to be based upon the wrong “basic physics” as well. According to the authors, Nye’s experiment and other similar classroom demonstrations allegedly of the greenhouse effect:

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Ugly: MSNBC host wants ‘re-education’ for Republican ‘climate deniers’

Steve Maley:

I’ll be happy to compare my Earth Science education and C.V. with Ed Schultz. What a dillweed.

Originally posted on Watts Up With That?:

Wow. just wow. No wonder MSNBC is tanking in ratings. Watch the video:

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Mexico Opens Its Energy Sector to Outside Investment

Even Joe Biden would agree: this is a pretty big flipping deal.

To recognize how big, you need to understand that Mexico commemorates the anniversary of the day it nationalized its oil fields and threw the Norte Americanos out as a national holiday. The national oil company, Petroleos Mexicanos (PEMEX), is a great source of national pride and a cash cow that funds the country’s social programs. Allowing private companies back in, not merely as service providers but as equity owners of production required amendment of the national constitution.

MEXICO CITY (AP) — Mexico has passed laws to open its oil, gas and electric industries to private and foreign investors after 76 years of state control. Now comes the hard part.

Experts say Mexico’s hopes for tens of billions of dollars in outside investment, and possibly a shale gas boom like the one occurring across the border in Texas, hinge on being able to design the kind of tenders, contracts and concessions that would actually prove attractive to companies that already have their hands full drilling in deep sea waters and hydro-fracking elsewhere. …

Mexico’s oil and gas production peaked in 2004 at 3.4 million barrels a day. It has fallen steadily since to the current 2.5 million barrels. With the reform, the government hopes to increase that to 3 million barrels by 2018 and 3.5 million by 2025, by attracting private companies with the expertise and technology to exploit the country’s vast shale and deep-water reserves.

And that’s just it. As an arm of the state, PEMEX’s operations have historically been hamstrung by labor unions and plundered for personal gain. Politicians’ thirst for petrodollars meant that PEMEX could not be run like a capitalist business. Consequently, projects that consume lots of capital and depend on the latest technology (read: deepwater and shale plays) have been bypassed in favor of the large shallow water, conventional offshore fields that have historically been PEMEX’s bread and butter.

Geology knows no political boundaries. Half of the Gulf of Mexico deepwater lies in Mexico’s Exclusive Economic Zone and is relatively unexplored. Onshore, it’s easy to project the Eagle Ford trend of South Texas across the Rio Grande, but PEMEX has only drilled a handful of wells there. Needless to say, the deepwater and shale plays are the prize that has the attention of the major international companies who hope to make Mexico’s relatively unexploited resources their playground.

Since 2007, the Eagle Ford and Permian Basin booms have propelled Texas from a declining 1 million barrel per day producer to 3 million barrels per day (N.B.: greater than all of Mexico). Opening Mexico to capitalist competition for the first time in 75 years is great news for the industry, for North American oil supply and for the Mexican economy.

More on the story here and opinion from the Houston Chronicle here.

The energy opening has been termed “Mexico’s second revolution.” While some may view the statement as an exaggeration, few would dispute that the reform will be transformational for the country. Given Mexico’s immense existing and potential resource wealth, and its other favorable attributes (stable democracy, solid macroeconomic fundamentals, global economic integration, geographic proximity to the US, to name a few), the energy reform should attract international interest appropriate to the unique and unusual opportunity it presents. For those in Texas involved in a booming energy sector, the extension of North America’s energy renaissance is a good thing.

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You can take the course that Vladimir Putin mastered

From this morning’s email comes an offer for an extremely valuable course:

Leadership IQ Webinar:
Managing Narcissists, Blamers, Drama Queens and more

Do you ever have to deal with giant egos, or blamers, or people who find drama in every little thing? Do you have to work with anyone who always sees the negative in any situation? Or someone who is hyper-sensitive and always gets their feelings hurt?

Sadly, not every person in our organization is nice, pleasant and easy-going. So you’ve got to know how to manage and understand difficult personalities. Fortunately, we’ve identified the Big Five difficult personalities that drive the most conflict in organizations, and we’ve developed specific scripts for dealing with each one.

In this 60-minute webinar called Managing Narcissists, Blamers, Drama Queens and more, you’ll learn specific scripts for managing Narcissists (Giant Egos), Blamers and Finger-Pointers, Drama Queens and Kings, Negative and Overly Sensitive people.

Yes, Putin certainly has the skills required to manage the narcissists, blamers and drama queens in his life. Pretty consistently gets the upper hand.

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