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发表刊物：Gas Science and Engineering
关键字：Favourable reservoir area; Coalbed Methane
摘要：确定煤层气 （CBM） 的有利储藏区对于提高煤层气项目的经济性具有重要意义。然而，准确识别油气气藏中具有强储层参数异质性的有利区域具有挑战性。在以往的研究中，应用了一些传统模型来识别煤层气储层的有利区域，然而，煤层气气藏高产区评价结果与实际分布存在较大差距。针对上述问题，采用深度学习和多分形理论确定具有强非均质性的有利储集区。在识别过程中，首先利用多重分形理论计算研究区的裂缝特征，生成加权数据层;其次，深度学习利用遗传算法和门循环单元构建模型，确定煤层气有利储集区，并与位于中国沁水煤田的范庄-郑庄区块进行验证。且上述模型识别有利区的相对误差较前一种方法减小，模型准确率达到87%，表明该方法可行，为复杂地质条件下煤层气有利储集区的识别提供了新的途径。 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