• 页岩气开发 •

### 基于机器学习的页岩气井井间干扰评价及预测

1. 1. 中国石油集团川庆钻探工程有限公司页岩气勘探开发项目经理部,四川 成都 610051
2. 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
• 收稿日期:2021-08-20 出版日期:2022-06-26 发布日期:2022-06-24
• 作者简介:张庆（1969—）,男,高级工程师,从事地质勘探、油气合作开发技术方面的研究。地址：四川省成都市成华区猛追湾街6号,邮政编码：610056。E-mail: zhangq_ccde@cnpc.com.cn

### Well interference evaluation and prediction of shale gas wells based on machine learning

ZHANG Qing1(),HE Feng1,HE Youwei2

1. 1. Shale Gas Exploration and Development Department, CNPC Chuanqing Drilling Engineering Co., Ltd., Chengdu, Sichuan 610051, China
2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China
• Received:2021-08-20 Online:2022-06-26 Published:2022-06-24

Abstract:

Inter-well interference seriously affects the production of shale gas wells. The evaluation and prediction of well interference degree is of great significance to the efficient development of shale gas. But the existing research mainly focuses on the interference phenomenon between shale gas wells, production performance, and parameter optimization through numerical simulation. There are few studies on the quantitative evaluation and prediction of the interference degree between shale gas wells, and the selected parameters is incomplete, which makes it difficult to objectively evaluate the well interference between shale gas wells. Therefore, the machine learning method is used to comprehensively consider the geological parameters and fracturing parameters to evaluate and predict the degree of interference between wells in the shale gas reservoir. Firstly, the initial data are processed to improve the data quality. Then, based on the processed data, cluster analysis and random forest algorithm are used to evaluate and predict the interference degree of shale gas wells. The results show that the proportions of the wells with low, medium and high well interference in the shale gas reservoirs are 25.93 %, 37.03 % and 37.04 %, respectively. The fracturing factors show significant influence on the well interference degree in the shale gas reservoirs. After parameters optimization, the prediction results of well interference degree reaches 92.07 %, indicating that the developed prediction model can be applied to forecast the well interference degree in shale gas reservoirs.

• TE37