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Markov链模型在储层随机建模中发挥着越来越重要的作用,但难以融合岩心、测井、地震等多尺度数据限制了它在实际中的应用。依据前人研究的结果,提出了将多尺度数据融入到Markov链模型中的相关方法和公式,即将大尺度数据作为条件数据以贝叶斯公式表达,同时利用公式将小尺度数据转换为井点硬数据。应用此方法对SL盆地Y地区过井剖面进行的岩性模拟表明,相对于无数据融合的方法,此方法能更加直观、准确地揭示薄岩性层的分布。
Markov chain model plays more and more important role in reservoir stochastic modeling. However, it is difficult to integrate multi-scale data such as core, well logging and seismic data to restrict its application in practice. Based on the results of previous studies, the relevant methods and formulas for integrating multi-scale data into the Markov chain model are proposed, in which large-scale data are expressed as Bayesian formulas as conditional data and the small-scale data are transformed into well points Hard data. Using this method to simulate the over-well profile in Y area of SL Basin shows that this method can reveal the distribution of thin lithology more directly and accurately than the method without data fusion.