Reservoir lithology stochastic simulation based on Markov random fields
来源期刊：中南大学学报(英文版)2014年第9期
论文作者：LIANG Yu-ru(梁玉汝) WANG Zhi-zhong(王志忠) 郭建华
文章页码：3610 - 3616
Key words：stochastic modeling; Markov random fields; training image; Monte Carlo simulation
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.
LIANG Yu-ru(梁玉汝)1, WANG Zhi-zhong(王志忠)1, GUO Jian-hua(郭建华)2
(1. School of Mathematics and Statistics, Central South University, Changsha 410083, China;
2. School of Earth Sciences and Physical Information, Central South University, Changsha 410083, China)
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.
Key words:stochastic modeling; Markov random fields; training image; Monte Carlo simulation
