机器学习在防漏堵漏中研究进展与展望

doi:10.7623/syxb202201008

石油学报 ›› 2022, Vol. 43 ›› Issue (1): 91-100.DOI: 10.7623/syxb202201008

机器学习在防漏堵漏中研究进展与展望

孙金声^1,2, 刘凡¹, 程荣超¹, 冯杰¹, 郝惠军¹, 王韧¹, 白英睿², 刘钦政³

1. 中国石油集团工程技术研究院有限公司北京 102206;
2. 中国石油大学(华东)石油工程学院山东青岛 266580;
3. 中国石油大学(北京)地球科学学院北京 102249

收稿日期:2021-07-29 修回日期:2021-11-04 出版日期:2022-01-25 发布日期:2022-02-10
通讯作者: 孙金声,男,1965年1月生,2006年获西南石油大学博士学位,现为中国工程院院士、中国石油集团工程技术研究院有限公司总工程师、中国石油大学(华东)博士生导师,主要从事钻井液、储层保护、天然气水合物开采理论与技术等研究工作。
作者简介:孙金声,男,1965年1月生,2006年获西南石油大学博士学位,现为中国工程院院士、中国石油集团工程技术研究院有限公司总工程师、中国石油大学(华东)博士生导师,主要从事钻井液、储层保护、天然气水合物开采理论与技术等研究工作。Email:sunjsdri@cnpc.com.cn
基金资助:
中国石油天然气集团有限公司重大工程现场试验项目"恶性井漏防治技术与高性能水基钻井液现场试验"（2020F-45）、中国博士后科学基金项目"裂缝性地层树脂类固化堵漏材料及固化机理研究"（2020M670585）和国家自然科学基金面上项目"深层裂缝性地层剪切响应型凝胶体系构筑与空间自适应堵漏机理"（No.52074327）资助。

Research progress and prospects of machine learning in lost circulation control

Sun Jinsheng^1,2, Liu Fan¹, Cheng Rongchao¹, Feng Jie¹, Hao Huijun¹, Wang Ren¹, Bai Yingrui², Liu Qinzheng³

1. CNPC Engineering Technology R&D Company Limited, Beijing 102206, China;
2. School of Petroleum Engineering, China University of Petroleum, Shandong Qingdao 266580, China;
3. College of Geoscience, China University of Petroleum, Beijing 102249, China

Received:2021-07-29 Revised:2021-11-04 Online:2022-01-25 Published:2022-02-10

摘要/Abstract

摘要： 随着大数据和人工智能技术在油气勘探开发领域应用不断拓展，数字化、智能化防漏堵漏技术已成为必然发展趋势，基于机器学习的算法模型及配套软件是核心内容。通过系统归纳分析了人工神经网络、支持向量机、随机森林、案例推理等机器算法在井漏特征预测、井漏实时监测和应用决策模型的应用现状，对比了各类机器学习算法的输入参数、输出参数、测试准确率及应用效果。机器学习算法在漏失层位预测、井漏监测预警及防漏堵漏措施推荐等方面体现了良好的应用前景，相比人工统计分析，其时效性、准确性和规模化应用优势明显，但还无法科学预测计算漏失压力、孔缝尺寸等井漏特征关键参数以及优化施工工艺。国外油气公司数字化钻完井技术布局早，现已整合多种机器学习算法开发了防漏堵漏相关软件，并在现场取得了一定应用成效。中国井漏相关数据治理、机器学习算法开发及配套软件攻关研究起步较晚，尚未建立成熟可靠的防漏堵漏数字化平台和智能化专家系统。为加快中国防漏堵漏技术数字化、智能化转型发展，需重点开展3方面研究：①推进井漏相关的多维度数据整合，搭建包括地震、测井、录井、钻井、防漏堵漏室内评价、防漏堵漏现场施工等方面的数据湖，补齐数据短板；②加强机器学习算法模型的解释性研究，结合井漏相关机理，提升算法模型的科学性和准确性；③集成井漏数据湖和算法模块，分区域建立井漏智能预测预警及防漏堵漏辅助决策专家系统，制定精细的防漏堵漏作业标准，全面提高一次防漏堵漏成功率。

关键词: 防漏堵漏, 机器学习, 井漏, 人工智能, 油气钻井

Abstract: With the expanding of big data and artificial intelligence (AI) technology in oil and gas exploration and exploitation, the development of digital and intelligent lost circulation control technology has become an inevitable trend, and the core lies in the machine learning-based algorithm model and the bundled software. This paper systematically summarizes and analyzes the application status of artificial neural network (ANN), support vector machine (SVM), random forest, case-based reasoning (CBR) and other machine algorithms inlost circulation feature prediction, real-time monitoring of lost circulation and application decision-making model. Additionally, a comparison is made on the input parameters, output parameters, test accuracy, and application effects of various machine learning algorithms. On this basis, it has been found that machine learning algorithms tend to have good application prospects in thief zone location prediction, lost circulation monitoring and early warning, and recommendation of measures for preventing and plugging lost circulation. Compared with artificial statistical analysis, the machine learning algorithms have obvious advantages in timeliness, accuracy and large-scale application. However, it is still unable for us to conduct scientific prediction and calculation of the loss pressure, pore/fracture size and other key parameters of lost circulation features and to optimize the field construction using these algorithms. As we know, foreign oil and gas companies made an early start for the overall arrangement of digital drilling and completion technology. At present, they have developed the software for lost circulation prevention and plugging by integrating a variety of machine learning algorithms, and have achieved certain application results in the field. However, China started late in the management of data related to lost circulation prevention and plugging, the development of machine learning algorithms and the research of bundled software, and it has not yet established any mature, reliable digital platform and intelligent expert system for lost circulation prevention and plugging. In order to accelerate the digital and intelligent transformation development of lost circulation technology in China, the research should focus onthe following three aspects:(1) to promote the integration of multi-dimensional data related to lost circulation, build a data lake involving earthquake, logging, drilling, laboratory evaluation and field construction of lost circulation, and make up and improvethe shortcomings of data; (2) to strengthen the explanatory study on machine learning algorithm model, and improve the scientificity and accuracy of the algorithm model based on the mechanisms of lost circulation; (3) to integrate the lost circulation data lake and algorithm modules, establish an intelligent expert system for the intelligent prediction and early warning as well as assistant decision-making of lost circulation in different regions, and formulate fine operation standards for lost circulation prevention and plugging, so as to comprehensively improve the one-time success rate of fd lost circulation control.

Key words: lost circulation prevention and plugging, machine learning, lost circulation, artificial intelligence (AI), oil and gas drilling

中图分类号:

TE254

孙金声, 刘凡, 程荣超, 冯杰, 郝惠军, 王韧, 白英睿, 刘钦政. 机器学习在防漏堵漏中研究进展与展望[J]. 石油学报, 2022, 43(1): 91-100.

Sun Jinsheng, Liu Fan, Cheng Rongchao, Feng Jie, Hao Huijun, Wang Ren, Bai Yingrui, Liu Qinzheng. Research progress and prospects of machine learning in lost circulation control[J]. Acta Petrolei Sinica, 2022, 43(1): 91-100.

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机器学习在防漏堵漏中研究进展与展望

Research progress and prospects of machine learning in lost circulation control

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