Rock physics based facies classification from seismic-inversion results in unconventional reservoirs

The objective of this study is to demonstrate the power of integrating rock physics theory, measurement and simulation to improve facies prediction in an unconventional limestone and shale reservoir. Reliable facies prediction is a challenge in unconventional reservoir characterization because of complex geological heterogeneities. Both deterministic and probabilistic approaches are commonly used in facies classifications that use well and seismic data. Bayes’ theory with uninformative priors is often used for probabilistic facies classification. We provide a case study that uses Bayes’ theory with informative priors for facies classification from pre-stack simultaneous elastic inversion results in an unconventional reservoir. In the proposed methodology, we integrate rock physics based theory, measurements and simulation with Bayesian statistical techniques where the prior probability represents our knowledge about rock properties, and is consistent with our geological knowledge, rock physics theory and measured data. We evaluate four facies classification methodologies: deterministic method, probabilistic method with uninformative priors, probabilistic method with uninformative priors and training facies defined from simulation, and probabilistic method with informative priors and training facies defined from simulation. This study indicates that, in probabilistic facies classification (Method 2), if uninformative priors are used, results are sub-optimal compared to deterministic methods involving a Rock Physics Template (RPT) workflow (Method 1). Additionally, probabilistic facies classification can be further improved if we use uninformative priors and training facies defined from Monte Carlo simulation (Method 3). Probabilistic facies prediction improves if we use informative priors and training facies defined from Monte Carlo simulation (Method 4).