The Value of Machine Learning in Reservoir Characterization
Among the many important reservoir characteristics, the ease of fluid flow within the reservoir is essential to oil and gas recovery. That characteristic of the rock, called permeability, is tricky to predict for two main reasons. First, it is highly variable; permeability can change by factors of 10,000 or more from one location to the next in a reservoir. Second, permeability is expensive to measure. Therefore, people who estimate permeability (petrophysicists) want to take a few measurements and look for relationships with other, more easily measured, rock properties.
One way to identify these relationships is with machine learning (ML). We can use ML with reservoir data to produce permeability estimates, which could save much time and expense over more traditional methods. We found, however, that ML can produce some very poor estimates if we are not careful. Using ML with reservoir data requires careful data preparation and works best under the guidance of engineers with petrophysical and geological knowledge. Carefully applied ML can provide better estimates than traditional petrophysical models.
Male, F., Jensen, J. L., and Lake, L. W., 2020, Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches: Journal of Natural Gas Science and Engineering, v. 77, 103244, doi:10.1016/j.jngse.2020.103244.
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