基于CBFS-CV算法的煤层气井压裂效果主控因素识别  

Identification of main controlling factors of fracturing performance in coalbed methane wells based on CBFS-CV algorithm

作　　者：闵超[1,2] 张馨慧 杨兆中 李小刚[3] 代博仁[1,2] MIN Chao;ZHANG Xinhui;YANG Zhaozhong;LI Xiaogang;DAI Boren(School of Science,Southwest Petroleum University,Chengdu City,Sichuan Province,610500,China;Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu City,Sichuan Province,610500,China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu City,Sichuan Province,610500,China)

机构地区：[1]西南石油大学理学院,四川成都610500 [2]西南石油大学人工智能研究院,四川成都610500 [3]西南石油大学油气藏地质及开发工程国家重点实验室,四川成都610500

出　　处：《油气地质与采收率》2022年第1期168-174,共7页Petroleum Geology and Recovery Efficiency

基　　金：国家科技重大专项“多层复杂煤体结构区煤储层直井压裂技术研究”(2016ZX05044-004-002);四川省科技计划项目“四川页岩气产业发展质量综合监测和评价技术研究与应用示范”(2020YFG0145)。

摘　　要：准确识别煤层气井压裂效果的主控因素,进而有效指导重复压裂方案优化,是煤层气井提升重复压裂产能的关键。依托研究区块的地质及工程大数据,利用基于Copula互信息的特征选择和交叉验证算法(CBFS-CV)识别影响压裂效果的主控因素,并结合梯度提升回归模型进行产能预测检验,形成了一种改进的煤层气井压裂效果主控因素识别算法。该算法可有效减少冗余性特征且增大相关性,并确定最佳特征数目。结果表明:煤体结构、储层参数(含气量、含气饱和度和临储比)和施工排量参数(最大施工排量)是影响研究区块压裂效果的3个主控因素,通过梯度提升回归模型验证CBFS-CV算法所识别出的主控因素的预测符合率达88%,证明了该算法的有效性。利用结果对该区块典型井进行主控因素分析,采用氮气泡沫解堵方案解决煤体结构差、煤粉堵塞等问题,现场施工后日产气量由288 m^(3)/d增至805 m^(3)/d,压裂效果明显改善。Accurately identifying the main controlling factors of the fracturing performance in coalbed methane(CBM)wells and then effectively guiding the optimization of repeated fracturing schemes are the keys to improving the repeated fracturing productivity in CBM wells.Relying on the geological and engineering data in the research block,the feature selection based on Copula mutual information and cross-validation(CBFS-CV)algorithm was adopted to identify the main controlling factors that affect the fracturing performance.In combination with the gradient boosting regression model for productivity prediction and inspection,an improved identification algorithm was formed for CBM wells.This algorithm can effectively reduce the redundant features and increase correlation,and thus determine the optimal number of features.The results show that the coal structure,reservoir parameters(gas content,gas saturation,critical reservoir ratio),and operation displacement parameters(maximum operation displacement)are the three main controlling factors that affect the fracturing performance in the research block.The gradient boosting regression model verifies that the prediction coincidence rate of the main controlling factors identified by the CBFS-CV algorithm reaches 88%,which proves the effectiveness of the algorithm.Moreover,the main controlling factors of the typical well in this block were analyzed based on the above results,and the plugging removal solution with nitrogen foam was applied to the problems of poor coal structure and coal powder plugging.After field operation,the daily gas production increased from 288 m^(3)/d to 805 m^(3)/d,and the fracturing performance was significantly improved.

关 键 词：煤层气 压裂效果 主控因素 Copula互信息 回归模型 大数据 

分 类 号：TE319[石油与天然气工程—油气田开发工程]