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The Role of the Engineer in Exploration: Quantify Everything

I’m Doug Weaver, and I’m a partner with Rose and Associates residing in Houston, Texas. I joined Rose a little over three years ago after retiring from a 39-year career with Chevron. I’ve spent well over half of my career in exploration as a petroleum engineer.

I’m often asked, “Why would an engineer be so interested in exploration”? There are many reasons, but let me pose one of my usual responses – “if you think it’s difficult to generate resource estimates with all the data you’d want – try doing it with none”.

I hope to continue this blog well into the future and get into some of the services engineers provide for exploration teams. But in this first session, let me convey an observation on a topic that will be pervasive in the future notes – Engineers and Geoscientists approach problems differently.

As I was scheduling my final semester of undergrad, I met with my advisor to get his feedback on one last technical course. Though my major was geotechnical engineering, I was a bit surprised when he suggested an advanced course in geology. Being that my advisor was one of the top geotechnical engineers in the world, I took his advice and enrolled in Geomorphology. The class consisted of about twenty geologists – and me. A good background for a future engineer in exploration!

All my engineering, math and science classes had followed a very familiar cadence. Three hourly exams and a final. No reading, no reports, just understanding equations and concepts and solving problems with that knowledge on a test. Solve problems with math.

In the geomorphology class, we were posed with the problem of figuring out where a glacier had stopped and created a moraine. We collected data in the field. We then went back to the lab, plotting and interpreting this data. To my surprise, I was able to plot the exact location where the glacier had stopped. No formulas, just data collection and interpretation.

I’m fairly sure that Professor Hendron not only intended for me to learn about geomorphology, but also to give me the experience of this alternate approach to solving a problem.

From what I’ve observed, this typifies the way most engineers and geologists solve problems (of course, I’m typecasting us all). Engineers start with a systematic workflow leading to a precise answer, while geoscientists use a more fluid, interpretive approach. Which leads us to the best answer? Both methods – when used together. The issues we face in exploration will certainly not allow the precise answer an engineer would normally want. In exploration, engineers need to embrace the uncertainty present in every aspect of their calculations. But, at some point we need to quantify our analysis. We can’t make effective decisions if we can’t quantify and rank the investment options for our companies. And that becomes the primary role of the engineer in exploration – to quantify opportunities.

Back to our glacial moraine. Suppose I’m a Midwestern gravel company looking for mining opportunities. It’s great that I’ve identified my moraine and a potential quarry, but what does that imply from an investment perspective? How does this deposit compare to others I might exploit? What’s the quality of the sand and gravel within the deposit? Are others more accessible?

Switching hats from geologist to engineer, my task is now to answer these questions. I now understand that I will never know the exact size of the deposit, as it is uncertain. I’ll have to rely on samples collected to build a representation of the nature of the deposit, realizing the samples reflect a tiny portion of the total moraine. This data will inform me about the range of possible sizes of this deposit. I’ll want to investigate other deposits in the area to support the analysis of the samples I’ve collected in my own deposit, and investigate how they were developed to get some idea of how to best evaluate the costs and timing of the extraction process. Finally, I somehow have to transform my moraine map and all these answers into a range of economic metrics, primarily Net Present Value, or if risk is present, Expected Value.

That’s where we’ll pick up next time, interrogating the Expected Value equation. Thanks for reading!

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