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

Conclusions

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

• Put a significant dent in the corporateô budget studying and modeling a reservoir system;
• Spent years convincing management of the model’s validity and the dire consequences of ignoring its warnings;
• Proposed millions of dollars of new drilling and facilities upgrades based on the model’s conclusions.

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

• 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, 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 reservoirinto 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”.

Most times, 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 *****.

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. Atmosphericmodels 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):

http://www.redstate.com/2014/08/30/oil-simulation-climate-modeling-scientific-method/