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.

参考文献

[1] 孙金声, 白英睿, 程荣超, 等. 裂缝性恶性井漏地层堵漏技术研究进展与展望[J].石油勘探与开发, 2021, 48(3):630-638.
SUN Jinsheng, BAI Yingrui, CHENG Rongchao, et al.Research progress and prospect of plugging technologies for fractured formation with severe lost circulation[J].Petroleum Exploration and Development, 2021, 48(3):630-638.
[2] ARSHAD U, JAIN B, RAMZAN M, et al.Engineered solution to reduce the impact of lost circulation during drilling and cementing in Rumaila Field, Iraq[R].IPTC 18245, 2015.
[3] 李伟, 白英睿, 李雨桐, 等.钻井液堵漏材料研究及应用现状与堵漏技术对策[J].科学技术与工程, 2021, 21(12):4733-4743.
LI Wei, BAI Yingrui, LI Yutong, et al.Research and application progress of drilling fluid lost circulation materials and technical countermeasures for lost circulation control[J].Science Technology and Engineering, 2021, 21(12):4733-4743.
[4] 暴丹, 邱正松, 叶链, 等.热致形状记忆""智能""型堵漏剂的制备与特性实验[J].石油学报, 2020, 41(1):106-115.
BAO Dan, QIU Zhengsong, YE Lian, et al.Preparation and characteristic experiments of intelligent lost circulation materials based on thermally shape memory polymer[J].Acta Petrolei Sinica, 2020, 41(1):106-115.
[5] ALKINANI H H, AL-HAMEEDI A T, DUNN-NORMAN S, et al.Applications of artificial neural networks in the petroleum industry:a review[R].SPE 195072, 2019.
[6] 匡立春, 刘合, 任义丽, 等.人工智能在石油勘探开发领域的应用现状与发展趋势[J].石油勘探与开发, 2021, 48(1):1-11.
KUANG Lichun, LIU He, REN Yili, et al.Application and development trend of artificial intelligence in petroleum exploration and development[J].Petroleum Exploration and Development, 2021, 48(1):1-11.
[7] 杨平, 詹仕凡, 李明, 等.基于梦想云的人工智能地震解释模式研究与实践[J].中国石油勘探, 2020, 25(5):89-96.
YANG Ping, ZHAN Shifan, LI Ming, et al.Research and practice on artificial intelligence seismic interpretation mode based on E & P Dream Cloud[J].China Petroleum Exploration, 2020, 25(5):89-96.
[8] 赵邦六, 雍学善, 高建虎, 等.中国石油智能地震处理解释技术进展与发展方向思考[J].中国石油勘探, 2021, 26(5):12-23.
ZHAO Bangliu, YONG Xueshan, GAO Jianhu, et al.Progress and development direction of PetroChina intelligent seismic processing and interpretation technology[J].China Petroleum Exploration, 2021, 26(5):12-23.
[9] 李宁, 徐彬森, 武宏亮, 等.人工智能在测井地层评价中的应用现状及前景[J].石油学报, 2021, 42(4):508-522.
LI Ning, XU Binsen, WU Hongliang, et al.Application status and prospects of artificial intelligence in well logging and formation evaluation[J].Acta Petrolei Sinica, 2021, 42(4):508-522.
[10] 赵丽莎, 史永彬, 金玮, 等.基于梦想云的测井智能化解释应用研究[J].中国石油勘探, 2020, 25(5):97-103.
ZHAO Lisha, SHI Yongbin, JIN Wei, et al.Application research on intelligent logging interpretation based on E & P Dream Cloud[J].China Petroleum Exploration, 2020, 25(5):97-103.
[11] 杨传书, 李昌盛, 孙旭东, 等.人工智能钻井技术研究方法及其实践[J].石油钻探技术, 2021, 49(5):7-13.
YANG Chuanshu, LI Changsheng, SUN Xudong, et al.Research method and practice of artificial intelligence drilling technology[J].Petroleum Drilling Techniques, 2021, 49(5):7-13.
[12] 王敏生, 光新军.智能钻井技术现状与发展方向[J].石油学报, 2020, 41(4):505-512.
WANG Minsheng, GUANG Xinjun.Status and development trends of intelligent drilling technology[J].Acta Petrolei Sinica, 2020, 41(4):505-512.
[13] 李道伦, 刘旭亮, 查文舒, 等.基于卷积神经网络的径向复合油藏自动试井解释方法[J].石油勘探与开发, 2020, 47(3):583-591.
LI Daolun, LIU Xuliang, ZHA Wenshu, et al.Automatic well test interpretation based on convolutional neural network for a radial composite reservoir[J].Petroleum Exploration and Development, 2020, 47(3):583-591.
[14] 李阳, 廉培庆, 薛兆杰, 等.大数据及人工智能在油气田开发中的应用现状及展望[J].中国石油大学学报:自然科学版, 2020, 44(4):1-11.
LI Yang, LIAN Peiqing, XUE Zhaojie, et al.Application status and prospect of big data and artificial intelligence in oil and gas field development[J].Journal of China University of Petroleum:Edition of Natural Science, 2020, 44(4):1-11.
[15] MOHAN R, HUSSEIN A, MAWLOD A, et al.Data driven and ai methods to enhance collaborative well planning and drilling risk prediction[R].SPE 203073, 2020.
[16] LIND Y B, KABIROVA A R.Artificial neural networks in drilling troubles prediction[R].SPE 171274, 2014.
[17] KRISHNA S, RIDHA S, VASANT P, et al.Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations:a comprehensive review[J].Journal of Petroleum Science and Engineering, 2020, 195:107818.
[18] 徐同台, 刘玉杰.钻井工程防漏堵漏技术[M].北京:石油工业出版社, 1997.
XU Tongtai, LIU Yujie.Leakage prevention and plugging technology in drilling engineering[M].Beijing:Petroleum Industry Press, 1997.
[19] 张磊, 王晓鹏, 谢涛, 等.天然裂缝性地层漏失压力预测新模型[J].钻采工艺, 2018, 41(5):19-22.
ZHANG Lei, WANG Xiaopeng, XIE Tao, et al.A New Leakage pressure prediction model for natural fractured formation[J].Drilling & Production Technology, 2018, 41(5):19-22.
[20] MORITA N, BLACK A D, GUH G F.Theory of lost circulation pressure[R].SPE 20409, 1990.
[21] 苏皓, 雷征东, 李俊超, 等.储集层多尺度裂缝高效数值模拟模型[J].石油学报, 2019, 40(5):587-593.
SU Hao, LEI Zhengdong, LI Junchao, et al.An effective numerical simulation model of multi-scale fractures in reservoir[J].Acta Petrolei Sinica, 2019, 40(5):587-593.
[22] 蒋裕强, 刘雄伟, 付永红, 等.渝西地区海相页岩储层孔隙有效性评价[J].石油学报, 2019, 40(10):1233-1243.
JIANG Yuqiang, LIU Xiongwei, FU Yonghong, et al.Evaluation of effective porosity in marine shale reservoir, western Chongqing[J].Acta Petrolei Sinica, 2019, 40(10):1233-1243.
[23] 杨午阳, 杨佳润, 陈双全, 等.基于U-Net深度学习网络的地震数据断层检测[J].石油地球物理勘探, 2021, 56(4):688-697.
YANG Wuyang, YANG Jiarun, CHEN Shuangquan, et al.Seismic data fault detection based on U-Net deep learning network[J].Oil Geophysical Prospecting, 2021, 56(4):688-697.
[24] 牟丹, 张丽春, 徐长玲.3种经典机器学习算法在火山岩测井岩性识别中的对比[J].吉林大学学报:地球科学版, 2021, 51(3):951-956.
MOU Dan, ZHANG Lichun, XU Changling.Comparison of three classical machine learning algorithms for lithology identification of volcanic rocks using well logging data[J].Journal of Jilin University:Earth Science Edition, 2021, 51(3):951-956.
[25] 谷宇峰, 张道勇, 鲍志东, 等.利用梯度提升决策树(GBDT) 预测渗透率——以姬塬油田西部长4+5段致密砂岩储层为例[J].地球物理学进展, 2021, 36(2):585-594.
GU Yufeng, ZHANG Daoyong, BAO Zhidong, et al.Permeability prediction using Gradient Boosting Decision Tree (GBDT):a case study of tight sandstone reservoirs of member of Chang 4+5 in western Jiyuan oilfield[J].Progress in Geophysics, 2021, 36(2):585-594.
[26] 陈桂, 刘洋.基于人工智能的断层自动识别研究进展[J].地球物理学进展, 2021, 36(1):119-131.
CHEN Gui, LIU Yang.Research progress of automatic fault recognition based on artificial intelligence[J].Progress in Geophysics, 2021, 36(1):119-131.
[27] 邹文波.人工智能研究现状及其在测井领域的应用[J].测井技术, 2020, 44(4):323-328.
ZOU Wenbo.Artificial intelligence research status and applications in well logging[J].Well Logging Technology, 2020, 44(4):323-328.
[28] GENG Zhi, WANG Hanqing, FAN Meng, et al.Predicting seismic-based risk of lost circulation using machine learning[J].Journal of Petroleum Science and Engineering, 2019, 176:679-688.
[29] 王建君, 李井亮, 李林, 等.基于叠后地震数据的裂缝预测与建模——以太阳-大寨地区浅层页岩气储层为例[J].岩性油气藏, 2020, 32(5):122-132.
WANG Jianjun, LI Jingliang, LI Lin, et al.Fracture prediction and modeling based on poststack 3D seismic data:a case study of shallow shale gas reservoir in Taiyang-Dazhai area[J].Lithologic Reservoirs, 2020, 32(5):122-132.
[30] 卢志远, 张晓黎, 张万龙, 等.一种地震裂缝预测与岩石力学相结合评估井漏风险的新方法——以准噶尔盆地玛湖油田A井区为例[J].天然气地球科学, 2020, 31(10):1453-1465.
LU Zhiyuan, ZHANG Xiaoli, ZHANG Wanlong, et al.A new method of leakage risk evaluation based on seismic fracture prediction and rock mechanics:case study in a wellblock of Mahu oilfield in Junggar Basin[J].Natural Gas Geoscience, 2020, 31(10):1453-1465.
[31] 张吉昌, 邢玉忠, 郑丽辉.利用人工智能技术进行裂缝识别研究[J].测井技术, 2005, 29(1):52-54.
ZHANG Jichang, XING Yuzhong, ZHENG Lihui.Using artificial intelligent technique to identify fractures[J].Well Logging Technology, 2005, 29(1):52-54.
[32] 申辉林, 高松洋.基于BP神经网络进行裂缝识别研究[J].新疆石油天然气, 2006, 2(4):39-42.
SHEN Huilin, GAO Songyang.Fractures identification based on BP neural network[J].Xinjiang Oil & Gas, 2006, 2(4):39-42.
[33] JIANG Jiajun, XU Rui, JAMES S C, et al.Deep-learning-based vuggy facies identification from borehole images[J].SPE Reservoir Evaluation & Engineering, 2021, 24(1):250-261.
[34] TRAN N L, GUPTA I, DEVEGOWDA D, et al.Application of interpretable machine-learning workflows to identify brittle, fracturable, and producible rock in horizontal wells using surface drilling data[J].SPE Reservoir Evaluation & Engineering, 2020, 23(4):1328-1342.
[35] LI Zejun, CHEN Mian, JIN Yan, et al.Study on intelligent prediction for risk level of lost circulation while drilling based on machine learning[R].ARMA 2018-105, 2018.
[36] 何涛, 谢显涛, 王君, 等.利用优化BP神经网络建立裂缝宽度预测模型[J].钻井液与完井液, 2021, 38(2):201-206.
HE Tao, XIE Xiantao, WANG Jun, et al.Crack width prediction model based on optimized BP neural network[J].Drilling Fluid & Completion Fluid, 2021, 38(2):201-206.
[37] FENG Yongcun, JONES J F, GRAY K E.A review on fracture-initiation and -propagation pressures for lost circulation and wellbore strengthening[J].SPE Drilling & Completion, 2016, 31(2):134-144.
[38] YIN Qishuai, YANG Jin, LIU Shujie, et al.Intelligent method of identifying drilling risk in complex formations based on drilled wells data[R].SPE 187472, 2017.
[39] AHMED S A, MAHMOUD A A, ELKATATNY S, et al.Prediction of pore and fracture pressures using support vector machine[R].IPTC 19523, 2019.
[40] YU Hao, CHEN Guoxiong, GU Hanming.A machine learning methodology for multivariate pore-pressure prediction[J].Computers & Geosciences, 2020, 143:104548.
[41] BOONCHAROEN P, RINSIRI T, PAIBOON P, et al.Pore pressure estimation by using machine learning model[R].IPTC 21490, 2021.
[42] ALKINANI H H, AL-HAMEEDI A T, DUNN-NORMAN S, et al.Data-driven neural network model to predict equivalent circulation density ECD[R].SPE 198612, 2019.
[43] HAN Chao, GUAN Zhichuan, LI Jingjiao, et al.Equivalent circulating density prediction using a hybrid arima and BP neural network model[R].SPE 197495, 2019.
[44] ALKINANI H H, AL-HAMEEDI A T, DUNN-NORMAN S, et al.Prediction of lost circulation prior to drilling for induced fractures formations using artificial neural networks[R].SPE 195197, 2019.
[45] ALKINANI H H, AL-HAMEEDI A T, DUNN-NORMAN S, et al.Minimizing lost circulation non-productive time for naturally fractured formations[R].ARMA 2019-0513, 2019.
[46] JAHANBAKHSHI R, KESHAVARZI R, JALILI S.Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs:a case study[J].European Journal of Environmental and Civil Engineering, 2014, 18(3):320-335.
[47] AL-HAMEEDI A T, ALKINANI H H, DUNN-NORMAN S, et al.Using machine learning to predict lost circulation in the Rumaila field, Iraq[R].SPE 191933, 2018.
[48] HOU Xinxin, YANG Jin, YIN Qishuai, et al.Lost circulation prediction in south china sea using machine learning and big data technology[R].OTC 30653 2020.
[49] 翟晓鹏, 鞠鹏飞, 谢志涛, 等.页岩诱导性裂缝漏失压力动力学模型[J].天然气工业, 2018, 38(3):81-86.
ZHAI Xiaopeng, JU Pengfei, XIE Zhitao, et al.A dynamic model for the leakage pressure of induced fractures in shale reservoirs[J].Natural Gas Industry, 2018, 38(3):81-86.
[50] 邸士莹, 程时清, 白文鹏, 等.致密油藏动态裂缝扩展机理及应用[J].力学学报, 2021, 53(8):2141-2155.
DI Shiying, CHENG Shiqing, BAI Wenpeng, et al.Dynamic fracture propagation mechanism and application in tight oil reservoir[J].Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8):2141-2155.
[51] 李大奇, 康毅力, 刘修善, 等.基于漏失机理的碳酸盐岩地层漏失压力模型[J].石油学报, 2011, 32(5):900-904.
LI Daqi, KANG Yili, LIU Xiushan, et al.The lost circulation pressure of carbonate formations on the basis of leakage mechanisms[J].Acta Petrolei Sinica, 2011, 32(5):900-904.
[52] 涂曦予, 于露, 耿子辰, 等.基于大规模时间序列的井漏事故预警方法[J].信息技术, 2018, 42(12):1-4.
TU Xiyu, YU Lu, GENG Zichen, et al.Research on intelligent warning of loss circulation based on large-scale time series data[J].Information Technology, 2018, 42(12):1-4.
[53] 谢平, 蒋丽雯, 赵尧, 等.基于神经网络的井涌井漏实时预测方法研究[J].现代计算机, 2018(11):23-28.
XIE Ping, JIANG Liwen, ZHAO Yao, et al.Research on real-time prediction and analysis based on neural network for well kick and lost circulation[J].Modern Computer, 2018(11):23-28.
[54] 史肖燕, 周英操, 赵莉萍, 等.基于随机森林的溢漏实时判断方法研究[J].钻采工艺, 2020, 43(1):9-12.
SHI Xiaoyan, ZHOU Yingcao, ZHAO Liping, et al.Losses in real time on basis of random forest method[J].Drilling & Production Technology, 2020, 43(1):9-12.
[55] UNRAU S, TORRIONE P.Adaptive real-time machine learning-based alarm system for influx and loss detection[R].SPE 187155, 2017.
[56] ANDIA P, SANT R V, WHITELEY N.A comprehensive real-time data analysis tool for fluid gains and losses[R].SPE 189576, 2018.
[57] 马涛, 张仲宏, 王铁成, 等.勘探开发梦想云平台架构设计与实现[J].中国石油勘探, 2020, 25(5):71-81.
MA Tao, ZHANG Zhonghong, WANG Tiecheng, et al.Architecture design and implementation of E & P Dream Cloud platform[J].China Petroleum Exploration, 2020, 25(5):71-81.
[58] 李剑峰.智慧石化建设:从信息化到智能化[J].石油科技论坛, 2020, 39(1):34-42.
LI Jianfeng.Construction of intelligent petrochemical industry:from information to intelligence[J].Petroleum Science and Technology Forum, 2020, 39(1):34-42.
[59] 张志磊, 胡百中, 卞维坤, 等.昭通页岩气示范区井漏防治技术与实践[J].钻井液与完井液, 2020, 37(1):38-45.
ZHANG Zhilei, HU Baizhong, BIAN Weikun, et al.Mud loss control techniques and practices in Zhaotong demonstration zone of shale gas drilling[J].Drilling Fluid & Completion Fluid, 2020, 37(1):38-45.
[60] 李宁, 张震, 程荣超, 等.库车山前堵漏材料关键性能参数及评价方法研究[J].内蒙古石油化工, 2020, 46(8):6-10.
LI Ning, ZHANG Zhen, CHENG Rongchao, et al.Research on key performance parameters and evaluation methods of lost circulation materials in Kuqa piedmont[J].Inner Mongolia Petrochemical Industry, 2020, 46(8):6-10.
[61] 王涛, 刘锋报, 罗威, 等.塔里木油田防漏堵漏技术进展与发展建议[J].石油钻探技术, 2021, 49(1):28-33.
WANG Tao, LIU Fengbao, LUO Wei, et al.The technical advance and development suggestions for leakage prevention and plugging technologies in the Tarim oilfield[J].Petroleum Drilling Techniques, 2021, 49(1):28-33.
[62] 张学洪, 李黔.基于案例推理的井漏风险预警方法[J].断块油气田, 2017, 24(2):255-258.
ZHANG Xuehong, LI Qian.Risk pre-warning method of well leakage based on case reasoning[J].Fault-Block Oil and Gas Field, 2017, 24(2):255-258.
[63] 尹虎, 王海彪.基于CBR的井漏复杂事故的智能预警方法研究[J].科技通报, 2018, 34(4):195-199.
YIN Hu, WANG Haibiao.Intelligent research of complex loss circulation' warning based on CBR[J].Bulletin of Science and Technology, 2018, 34(4):195-199.
[64] REEDY K, POPA A S, CASSIDY S D.Remediation solutions for lost circulation using case based reasoning[R].SPE 190066, 2018.
[65] ALKINANI H H, AL-HAMEEDI A T T, DUNN-NORMAN S.Minimizing lost circulation non-productive time using expected monetary value and decision tree analysis[R].SPE 200844, 2021.
[66] ABBAS A K, HAMED H M, AL-BAZZAZ W, et al.Predicting the amount of lost circulation while drilling using artificial neural networks:an example of southern Iraq oil fields[R].SPE 198617, 2019.
[67] ABBAS A K, BASHIKH A A, ABBAS H, et al.Intelligent decisions to stop or mitigate lost circulation based on machine learning[J].Energy, 2019, 183:1104-1113.
[68] IZYUROV V, KHARITONOV A, SEMENIKHIN I, et al.Selecting bridging agents' particle size distribution for optimum plugging while drilling in permeable zones[R].SPE 197009, 2019.
[69] 蒋官澄, 毛蕴才, 周宝义, 等.暂堵型保护油气层钻井液技术研究进展与发展趋势[J].钻井液与完井液, 2018, 35(2):1-16.
JIANG Guancheng, MAO Yuncai, ZHOU Baoyi, et al.Progress made and trend of development in studying on temporarily type plugging reservoir protection drilling fluids[J].Drilling Fluid & Completion Fluid, 2018, 35(2):1-16.
[70] 张世锋, 王相, 崔新颖, 等.基于改进理想充填理论的堵漏颗粒粒度分布设计方法[J].常州大学学报:自然科学版, 2021, 33(3):54-59.
ZHANG Shifeng, WANG Xiang, CUI Xinying, et al.Modified ideal packing theory to optimize size distribution of plugging particle for fracture lost circulation control[J].Journal of Changzhou University:Natural Science Edition, 2021, 33(3):54-59.
[71] 康毅力, 王凯成, 许成元, 等.深井超深井钻井堵漏材料高温老化性能评价[J].石油学报, 2019, 40(2):215-223.
KANG Yili, WANG Kaicheng, XU Chengyuan, et al.High-temperature aging property evaluation of lost circulation materials in deep and ultra-deep well drilling[J].Acta Petrolei Sinica, 2019, 40(2):215-223.
[72] 暴丹, 邱正松, 邱维清, 等.高温地层钻井堵漏材料特性实验[J].石油学报, 2019, 40(7):846-857.
BAO Dan, QIU Zhengsong, QIU Weiqing, et al.Experiment on properties of lost circulation materials in high temperature formation[J].Acta Petrolei Sinica, 2019, 40(7):846-857.
[73] 熊战, 李立昌, 王学龙, 等.理想充填堵漏工艺在温储6井的应用[J].钻井液与完井液, 2020, 37(4):465-468.
XIONG Zhan, LI Lichang, WANG Xuelong, et al.Lost circulation control with idea packing on well Wenchu-6[J].Drilling Fluid & Completion Fluid, 2020, 37(4):465-468.
[74] 贾承造.中国石油工业上游发展面临的挑战与未来科技攻关方向[J].石油学报, 2020, 41(12):1445-1464.
JIA Chengzao.Development challenges and future scientific and technological researches in China's petroleum industry upstream[J].Acta Petrolei Sinica, 2020, 41(12):1445-1464.

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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