Reservoir lithology stochastic simulation based on Markov random fields

Abstract

Markov random fields (MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables (for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D (three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC (Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body.

Keywords

stochastic modeling / Markov random fields / training image / Monte Carlo simulation

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