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Integration of rock physics template to improve Bayes' facies classification

Reliable facies prediction is a key problem in reservoir characterization. Facies classification using an arbitrary selected zone is the simplest method. However, the problem is that the interpretation result strongly depends on the size of the selected zone. Using an RPT (rock physics template), we can define an accurate zone instead of defining an arbitrarily sharp cutoff for the zone. The next level of sophistication is using a statistical technique, whereby we can calculate not only the best zone, but also the probability of occurrence of that zone. Baye’s theory is normally used for probabilistic facies classification. However, the prior belief is a fundamental part of Bayesian statistics. The posterior probabilities are heavily influenced by the prior probabilities, so any error caused by the interpretation of the prior probability will be amplified in the posterior probability. The objective of this study is to improve the prior probability predictions using rock physics analysis for quantitative facies classification. We use an RPT as a guidance to define these prior probabilities. For seismic reservoir characterization, well data along with rock physics theory via RPT are used to define the prior probability. We found that Baye’s prediction increases as we define the prior probabilities from the RPT.