More than a blueprint
Evan Bianco
"This company used to function just fine without any modeling."
My brother, an architect, paraphrased his supervisor this way one day; perhaps you have heard something similar. "But the construction industry is shifting," he noted. "Now, my boss needs to see things in 3D in order to understand. Which is why we have so many last minute changes in our projects. 'I had no idea that ceiling was so low, that high, that color, had so many lights,' and so on."
The geological modeling process is often an investment with the same goal. I am convinced that many are seduced by the appeal of an elegantly crafted digital design, the wow factor of 3D visualization. Seeing is believing, but in the case of the subsurface, seeing can be misleading.
Not your child's sandbox! Photo: R Weller.Building a geological model is fundamentally different than building a blueprint, or at least it should be. First of all, a geomodel will never be as accurate as a blueprint, even after the last well has been drilled. The geomodel is more akin to the apparatus of an experiment; literally the sandbox and the sand. The real lure of a geomodel is to explore and evaluate uncertainty. I am ambivalent about compelling visualizations that drop out of geomodels, they partially stand in the way of this high potential. Perhaps they are too convincing.
I reckon most managers, drillers, completions folks, and many geoscientists are really only interested in a better blueprint. If that is the case, they are essentially behaving only as designers. That mindset drives a conflict any time the geomodel fails to predict future observations. A blueprint does not have space for uncertainty, it's not defined that way. A model, however, should have uncertainty and simplifying assumptions built right in.
Why are the narrow geological assumptions of the designer so widely accepted and in particular, so enthusiastically embraced by the industry? The neglect of science keeping up with technology is one factor. Our preference for simple and quickly understood explanations is another. Geology, in its wondrous complexity, does not conform to such easy reductions.
Despite popular belief, this is not a blueprint.We gravitate towards a single solution precisely because we are scared of the unknown. Treating uncertainty is more difficult that omitting it, and a range of solutions is somehow less marketable than precision (accuracy and precision are not the same thing). It is easier because if you have a blueprint, rigid, with tight constraints, you have relieved yourself from asking what if?
- What if the fault throw was 20 m instead of 10 m?
- What if the reservoir was oil instead of water?
- What if the pore pressure increases downdip?
The geomodelling process should be undertaken for the promise of invoking questions. Subsurface geoscience is riddled with inherent uncertainties, uncertainties that we aren't even aware of. Maybe our software should have a steel-blue background turned on as default, instead of the traditional black, white, or gray. It might be a subconscious reminder that unless you are capturing uncertainty and iterating, you are only designing a blueprint.
If you have been involved with building a geologic model, was it a one-time rigid design, or an experimental sandbox of iteration?
The photograph of the extensional sandbox experiment is used with permission from Roger Weller of Cochise College. Image of geocellular model from the MATLAB Reservoir Simulation Toolbox (MRST) from SINTEF applied mathematics, which has been recently release under the terms of the GNU General public license!
Reader Comments (3)
@Evan:
One thing that kind of surprised me early on when I started working on a construction site is that blue prints are not as accurate as we'd like to believe. They are diagramatic, and are not be be taken literally.
In construction we have shop drawings which are prepared by a contractor showing various small details to extreme degrees of accuracy (to the millimeter or 1/32" anyway). Architectural drawings are never to this level of detail, but instead they show relationships between objects, such as the length of a kitchen island and distance between it and the nearest walls.
Shop drawings will show the exact dimensions of the island itself, including thicknesses of materials and how it's to be built (is it 3/4" construction or 5/8"? does the paint add 1/16" or 1/32" to each surface? how big are the drawers? the gables? the reveals? et cetera). All of this information would be impossible to effectively communicate on an architectural plan, but it is absolutely vital to understanding the kitchen. You need the architectural plan to position the island, and the shop drawing to build it.
I wonder if geoscience could take a page out of the builder play book (in fairness, we've been doing it thousands of years longer than you). It is useful to have an 'big picture' plan that shows the overall idea and the relationships, but it's just as crucial to have the details as well. Neither is enough information on it's own to understand the entire project, but each tells a crucial part of the story.
In the case of your geomodels, the most important thing to make clear up front is whether you are looking at the plan or the shop drawing - the relationships or the details.
If your audience is regularly getting confused by your model for example, maybe it's because the model looks like it has more information, or more accurate information, then you ever intended to show. It really helps readability if you keep a clear separation between the information shown on your plans and in your details.
Great posting, excellent observation by Reid. Best part of all, a non-geoscientist following Agile and lending us the benefit of his experience outside of our discipline. Thanks for all of it.
I think it would help us communicate the level of uncertainty in our models (maps, charts, cross sections...) if we could become more conversant with the concept of probability density functions, maybe using color, line transparency or thickness, whatever, to convey high and low confidence areas. Faint parts of the model mean fuzziness in our understanding. Sharp, bold lines - the data supports more certainty. Just a thought.
@Brian,
When I use the abbreviation PDF, people usually expect that I am sending them and Adobe document! I agree with you. We need to take a bit more effort to communicate this way, and I will be the first to admit that I have a ways to learn. A PDF does exist for every point in space, so the challenge is to capture that information somehow, and also to display it. I have used opacity (semi-transparency) in the past to show qualitative measures of probability. It is certainly another attribute for displaying additional information. This concept of fuzziness is going to be something we hear a lot more about, I predict.