Evaluation of Mineralogy per Geological Layers by Approximate Bayesian Computation
- Vianney Bruned (Schlumberger Petroleum Services) | Alice Cleynen (University of Montpellier) | André Mas (University of Montpellier) | Sylvain Wlodarczyk (Schlumberger Petroleum Services)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- May 2020
- Document Type
- Journal Paper
- 2020.Society of Petroleum Engineers
- wellbore log, mineralogical inversion, approximate Bayesian computation
- 11 in the last 30 days
- 20 since 2007
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We propose a new three-step methodology to perform an automated mineralogical inversion from wellbore logs. The approach is derived from a Bayesian linear-regression model with no prior knowledge of the mineral composition of the rock. The first step makes use of approximate Bayesian computation (ABC) for each depth sample to evaluate all the possible mineral proportions that are consistent with the measured log responses. The second step gathers these candidates for a given stratum and computes through a density-based clustering algorithm the most probable mineralogical compositions. Finally, for each stratum and for the most probable combinations, a mineralogical inversion is performed with an associated confidence estimate. The advantage of this approach is to explore all possible mineralogy hypotheses that match the wellbore data. This pipeline is tested on both synthetic and real data sets.
|File Size||12 MB||Number of Pages||15|
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