Pillars of Industry

Building Decision Competence: Life is the Sum of All Your Choices

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I am not the product of my circumstances. I am a product of my decisions. – Stephen R. Covey

When I joined Statoil as a reservoir engineer in the mid-1980s, I was assigned to a team of experienced reservoir engineers with the task of predicting future production from the not-yet-decided development of the Gullfaks B field.

Statoil had the latest and greatest in reservoir simulators, and we predicted 30 years of future oil production in double precision—a single production prediction for each year. We spent a lot of energy and time refining the model and used as many grid-blocks as we could, with the constraint that each simulation had to be completed overnight. Our production forecast was used as an input for the development decision, and the field was successfully developed.

I have never checked, but I’m pretty sure the field never produced anything close to what we predicted. Looking back at this work, which I really enjoyed and which seemed meaningful at the time, I struggle to see that we provided any useful input for the development decision or anything else of any value to Statoil. We provided single number forecasts that we knew, or should have known, were wrong (in the sense of not being the actual production numbers). We did not provide a range of possible production values, nor did we assign any probabilities to possible production scenarios.

In short, we did not do a very good job in supporting this very significant investment decision. Maybe I should have returned my paychecks (with interest).

I was about 10 years into my working career before I realized that the only way we can impact our lives, our families’ lives, the organizations we work for, our countries, or our world is through our decisions. This realization had a profound impact on not only my personal life but also on how I view the role of professionals and managers in the oil and gas industry.

Whether in an operating or a service company, the main role of a geoscientist, an engineer, or an economist is to support decision making, because it is only through its decisions that a company can create value. As decisions are about the future, they are always made in the face of uncertainty and it follows from this that technical work in our industry is fundamentally about uncertainty assessment for the purpose of making decisions.

This is not what I learned during my university studies nor the task that I was assigned when I started my career as an engineer. I wish my education (from grade school through university) and early work life had provided me with these insights:

It’s all about the decisions. Decisions are the only means you have to change your life or support value creation for your organization. One of the biggest obstacles in gaining decision competence, both for personal and corporate decision making, is that most of us think we are pretty good at making decisions. Yet it is easy to demonstrate that even in relatively simple decision-making situations people make choices that, when carefully reviewed, they know were unwise.

Uncertainty is an essential element of all decisions. Decisions can alter the future and, thus, always include elements (volumes, fault sealing, recovery factor, production, costs, prices, exchange rates, etc.) we are uncertain about. We must learn to become competent in dealing with uncertainty. Ignoring, or hiding from, uncertainty does not remove it.

Quantifying or reducing uncertainty creates no value in and of itself. Although uncertainty is an element of every decision, uncertainty quantification is not synonymous with decision making.

Uncertainty quantification creates value only to the extent that it holds the possibility of changing a decision that would otherwise be made differently.

Uncertainty without a decision is simply a worry. Likewise, once the decision is clear, further quantification of uncertainty is a waste of resources and only serves to obfuscate the situation.

For example, suppose your company is considering drilling a well whose value is uncertain; there is an 80% chance it could be worth $ 10 million  after drilling costs, and a 20% chance it could be worth $ 100 million. Thus, the outcome is highly uncertain, but the decision to drill is clear. Reducing this uncertainty cannot alter the best course of action.

I often hear people in the industry speak of reducing uncertainty by building a model. Modeling uncertainty does not reduce it. Rather, the model is an explicit representation of the uncertainty that is already implicit in the decision problem. Uncertainty can only be reduced or altered by our choices—not simply by our decision to recognize it formally.

Decisions versus outcomes. If we are to improve decision making, we must first be clear about what it means to make a good decision. A “good outcome” is a result that is highly valued by the decision maker. A “good decision” is one that is consistent with the decision maker’s beliefs, alternatives, and preferences at the time the decision is made. In short, a good decision is a logical decision. Unfortunately, good decisions do not always produce good outcomes. For example, drilling a particular exploration well may be a very good decision, but still results in a dry hole. Likewise, poor decisions may be followed by good outcomes.

All models are wrong; some models are useful. I learned a lot about modeling during my studies. We have a tendency to believe the results of our models, especially when we have invested a lot of time and money in generating their inputs and making sure they represent the geology/petrophysics/production/costs/economics in as realistic and detailed a manner as possible. However, companies tend to build too much detail into their models from the start and focus too much energy on specific cases or particular inputs.

A distinction that I have found useful is “cogency” versus “verisimilitude.” Cogency is having the property of being compelling. Verisimilitude means being true to life. We seek cogent decision models, rather than models exhibiting verisimilitude.

Ron Howard of Stanford University, one of the founders of decision analysis, has proposed a very helpful analogy to explain this concept. Consider model-train building. What makes a very good model train? One that is true to life. For example, an average model train might include a bar car. A good model train might include a bar car with people. A very good model train might show the people holding drinks. In an award-winning model train, you would be able to tell what kind of drinks the people were holding—for example, martinis with olives. Yet, inside a world-class model train, you would be able to see the pimento inside the olive.
Decision modeling and analysis is not about building model trains. Rather, we seek to provide insights to decision makers regarding critical decisions. Rarely does this require “pimento-level” modeling detail. Again, this level of modeling detail is really a shirking of responsibility on the part of the decision analyst who either will not or cannot build a model that includes only the most salient factors. Building in detail is easy. Building in incisiveness is hard work. As Howard has written, “...the real problem in decision analysis is not making analyses complicated enough to be comprehensive, but rather keeping them simple enough to be affordable and useful.”

Training and education. Companies should ensure a consistent definition and use of uncertainty quantification and decision-making methods. This can be facilitated through the training of management and professional staff, which will build a level of comfort and familiarity that should both increase and improve the use of the decision-analytic methods.

I believe that in addition to professional training, the industry should encourage academia to better train engineers, particularly undergraduates, in decision making. Engineering is a decision-making discipline (called design), but we simply do not train engineers in decision making. In fact, we spend more time teaching them to manipulate seldom-used mathematical formulas or to write computer programs in arcane languages than we do teaching them to make high-quality decisions. While this is true in all engineering disciplines, it appears to be particularly acute in petroleum engineering.

My time in the petroleum industry has been, and continues to be, incredibly interesting and exciting. What I have found particularly stimulating is to work on the boundary between disciplines. In our industry, we are confronting geology, physics, fluid dynamics, economics, uncertainty, and decision making every day. Our industry has historically attracted some of the best and brightest across the world, and I have been lucky to meet many of them through my students, teachers, and colleagues.


Reidar B. Bratvold is a professor of investment and decision analysis at the University of Stavanger and the Norwegian Institute of Technology. His research interests include decision analysis, project valuation, portfolio analysis, real-option valuation, and behavioral challenges in decision making. Prior to academia, Bratvold spent 15 years in the industry in various technical and management roles. He is the first author of the SPE book Making Good Decisions. Bratvold has served as an SPE Distinguished Lecturer three times and is the executive editor for the SPE Economics & Management journal. He is the 2017 recipient of the SPE Management & Information Award.  Bratvold is a Fellow of the Society of Decision Professionals and the Norwegian Academy of Technological Sciences. He holds a PhD in petroleum engineering and an MSc in mathematics, both from Stanford University, an MSc in petroleum engineering from the University of Tulsa, and has business and management science education from INSEAD and Stanford University.

 

 


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