Reservoir facies design and modeling using probabilistic rock-physics templates

Abstract

We have developed a technique to design and optimize reservoir lithofluid facies based on probabilistic rock-physics templates. Subjectivity is promoted to design possible facies scenarios with different pore-fluid conditions, and quantitative simulations and evaluations are conducted in facies model selection. This method aims to provide guidelines for reservoir-facies modeling in an exploration setting in which limited data exist. The work includes two parts: facies-model simulations and uncertainty evaluations. We have first derived scenarios with all possible fluid types using Gassmann fluid substitution. We designed models with different numbers of facies and pore-fluid conditions using site-specific rock-physics templates. Detailed facies simulations were conducted in the petroelastic, elastic, and seismic domains in a step-by-step framework to preserve the geologic interpretability. The use of probabilistic rock-physics templates allowed for multiple realizations of each facies model to account for different types and magnitudes of errors and to infer facies probability and uncertainty. For each realization, we used Bayesian classification to assign facies labels. Comparisons between the predicted and true labels provided the success rates and entropy indices to quantify the prediction errors and confidence degrees, respectively. This workflow was tested with well-log data from a clastic reservoir in the Gulf of Mexico. We simulated models with five to seven facies with different pore-fluid parameters. From the petroelastic, elastic, and seismic domains, the uncertainty of facies models significantly increased due to well-log measurement errors, data-model mismatch, and resolution differences. The facies model consisting of oil sand, gas sand, and shale was the optimal set based on the high success rates and low entropy indices. Facies profiles estimated from this optimal model presented significant consistency with well-log interpretations. The techniques and results demonstrated here could be applied to different types of clastic reservoirs, and they provide useful constraints for reservoir facies modeling during early oilfield exploration stages.