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发表刊物：Gas Science and Engineering
关键字：Favourable reservoir area; Coalbed Methane
摘要：Identificating favourable reservoir areas of Coalbed Methane(CBM) is significant for improving economics of CBM project. However, accurately identification of favourable areas from CBM reservoir with strong heterogeneity of reservoir parameters is challenging. In previous researches, some traditional models were applied to identify favourable areas of CBM reservoir, however there is a widely gap between the evaluation result and reality distribution of high-yield areas of CBM reservoir. For solving above problem, identifying favourable reservoir area with strong heterogeneity is conducted with deep learning and multifractal theory. In identification process, firstly the fracture characteristics of the research area is calculated with multifractal theory to generate weighted data layer. Secondly a deep learning model is constructed with the genetic algorithm and door circulation unit to identify favourable reservoir areas of CBM and is verifyed with the block of Fanzhuang-Zhengzhuang located in the Qinshui coalfield of China. And the relative error of identification of favourable areas by the above model is reduced compared with the previous method, and the accuracy of the model reaches 87%, which indicates that the method is feasible and provides a new way for identificating favourable reservoir areas of CBM under complex geological conditions.
论文类型：期刊论文
论文编号：205176
是否译文：否
发表时间：2023-11-23
收录刊物：SCI
发布期刊链接：https://doi.org/10.1016/j.jgsce.2023.205176