Spatial hidden Markov chain models for estimation of petroleum reservoir categorical variables

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• 作者：Xiang Huang ; Jie Li ; Yuru Liang…
• 关键词：Hidden Markov model ; Posterior probability ; Reservoir simulation ; Sonic impedance ; Viterbi algorithm
• 刊名：Journal of Petroleum Exploration and Production Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：7
• 期：1
• 页码：11-22
• 全文大小：6293KB
• 刊物主题：Geology; Industrial and Production Engineering; Energy Technology; Offshore Engineering; Industrial Chemistry/Chemical Engineering; Monitoring/Environmental Analysis;
• 出版者：Springer Berlin Heidelberg
• ISSN：2190-0566
• 卷排序：7

文摘

Indicator variograms and transition probabilities are used to measure spatial continuity of petroleum reservoir categorical variables. Variogram-based Kriging variants are symmetric geostatistical methods, which cannot completely capture the complex reservoir spatial heterogeneity structure. The asymmetric spatial Markov chain (SMC) approaches employ transition probabilities to incorporate proportion, length and juxtaposition relation information in subsurface reservoir structures. Secondary data in petroleum geology, however, cannot be reasonably aggregated. We propose a spatial hidden Markov chain (SHMC) model to tackle these issues. This method integrates well data and seismic data by using Viterbi algorithm for reservoir forecasting. The classified sonic impedance is used as auxiliary data, directly in some kind of Bayesian updating process via a hidden Markov model. The SMC embedded in SHMC has been redefined according to first-order neighborhood with different lag in three-dimensional space. Compared with traditional SMC in Markov chain random field theory, the SHMC method performs better in prediction accuracy and reflecting the geological sedimentation process by integrating auxiliary information.