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基于多尺度数据融合Markov链模型的岩性随机模拟
Lithology stochastic simulation based on Markov chain models integrated with multi-scale data
【摘要】 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。
【Abstract】 The Markov chain models have played a more important role in the reservoir stochastic modeling,but the models are difficult to be integrated with the multi-scale data such as logging,core data and seismic data,which limits the application of the models.A new method and some formula were proposed for integrating the multi-scale data with the Markov chain models.The large-scale data were added into the models and taken as the conditional data,and the small-scale data were used to get exact data of well points by formula.The application of the method to simulate lithology of a section across wells in Y region of SL Basin shows that the fine lithology distribution obtained from the new method is more accurate and distinctive than that of the previous method.
【Key words】 Markov chain model; multi-scale data; integration; lithology stochastic simulation; fine lithology distribution;
- 【文献出处】 石油学报 ,Acta Petrolei Sinica , 编辑部邮箱 ,2010年01期
- 【分类号】P618.13
- 【被引频次】10
- 【下载频次】348