摘要
针对目前油气钻井工程智能化的发展趋势,阐述了机器学习技术在油气钻井领域的研究进展与应用情况,探讨并展望了未来机器学习技术在油气钻井领域的发展方向和研究重点。研究结果表明:1)相较于传统分析方法,机器学习技术在模型构建、多目标决策优化、复杂非线性关系分析以及适应拓展能力等方面具有很大优势;2)机器学习算法种类丰富,功能涵盖数据分析、文本分析以及图像分析等多个方面,可以处理多种类型的工程问题;3)目前机器学习技术在钻井工程中的优化设计、井下钻柱状态控制、风险监测评估以及辅助决策等方面都得到了初步应用,具有良好发展前景;4)未来机器学习在油气钻井领域的研究重点为现场数据的规范整合、模型与问题的契合度的提高以及集成模型的构建。该研究成果可以为油气行业升级转型、钻井智能化发展提供技术支撑。
In view of the current development trend of intelligent oil and gas drilling engineering,this paper expounds the research progress and application of machine learning technology in the field of oil and gas drilling,and discusses and prospects the development direction and research focus of machine learning technology in the field of oil and gas drilling in the future.The results show that:1)Compared with traditional analysis methods,machine learning technology has great advantages in model construction,multi-objective decision optimization,complex nonlinear relationship analysis and adaptive expansion ability;2)There are many kinds of machine learning algorithms,whose functions include data analysis,text analysis,image analysis and so on;3)At present,machine learning technology has been preliminarily applied in drilling engineering optimization design,downhole drill string state control,drilling risk monitoring and evaluation and auxiliary decision-making,and has a good development prospect;4)The future research of machine learning in the field of oil and gas drilling will focus on the standardized integration of field data,the improvement of the fit between model and problem,and the construction of integrated model.Machine learning research can provide technical support for the upgrading and transformation of oil and gas industry and the development of intelligent drilling.
作者
徐楷
苏堪华
李猛
万立夫
简旭
XU Kai;SU Kanhua;LI Meng;WAN Lifu;JIAN Xu(School of Petroleum Engineering,Chongqing University of Science and Technology,Chongqing 401331,China;Chuandong Drilling Company,Chuanqing Drilling Engineering Company of CNPC,Chongqing 400021,China)
出处
《非常规油气》
2023年第5期8-17,共10页
Unconventional Oil & Gas
基金
国家自然科学基金面上项目“耦合动力土反力作用的深水井口多轴疲劳理论和时变可靠度研究”(51974052)
重庆市基础研究与前沿探索项目“连续管钻井(塞)管柱底部激振波及规律和振扭耦合多轴疲劳研究”(cstc2019jcyj-msxmX0199)
重庆科技学院研究生科技创新项目“近钻头钻柱振动预测机器学习算法优选研究”(YKJCX2020113)。
关键词
人工智能
油气井工程
机器学习
钻井设计
风险监测
辅助决策
artificial intelligence
oil and gas well engineering
machine learning
drilling design
risk detection
decision support
