摘要
为了深度利用钻井过程中产生的大量数据,实现对随钻风险监测与预警的智能分析和辅助决策,基于C/S三层架构和数据中台,结合物理模型、智能算法和趋势分析技术,开发了钻井智能辅助决策系统。通过分析钻井数据来源、结构和用途,结合数据传输、自然语言提取和数据融合技术,实现了多源异构数据获取、融合和管理;综合考虑钻井过程中水力学和管柱力学的耦合影响,设计了模型融合机制,建立了随钻数字井筒系统。在此基础上,结合预测参数与实测参数的偏差变化趋势,建立了风险异常监测算法,将针对井下故障和复杂情况的处理措施与预警机制相结合,实现了风险预警与辅助决策。该系统在页岩油气水平井、深井等不同类型探井中应用50余口井,预警结果与现场符合率达91.5%,验证了其可行性及实用性。钻井智能辅助决策系统能够进行钻井参数优化和钻井风险监测,为高效安全钻井提供了技术保障。
In order to deeply leverage the large amount of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for risk monitoring while drilling,a drilling intelligent decision support system has been developed.This system was developed based on a client/server(C/S)three-tier architecture and data platform,integrating with the physical models,intelligent algorithms,and trend analysis technologies.Through analyzing the source,structure,and usage of drilling data,combined with data transmission,natural language extraction,and data fusion technology,the data acquisition,fusion,and management of multi-source heterogeneous data have been achieved.Taking into account the coupling effect of hydraulics and tubular mechanics during the drilling process,the model fusion mechanism was designed,and the digital wellbore system while drilling was established.On this basis,combined with the deviation trend of predicted parameters and measured parameters,a risk anomaly monitoring algorithm was established,and thus the risk alerting and decision-making support could be realized through integrating construction measures with the alerting mechanism for downhole failure and complex situations.This system has been applied in over 50 exploration wells across various types,including shale oil and gas horizontal wells and deep wells,with an accuracy rate of 91.5%between predicted results and actual field results.Its practicality and feasibility was thereby verified.The drilling intelligent decision support system can realize drilling parameter optimization and risk monitoring,thereby it serves as a robust technical safeguard for the efficient drilling and safe operations.
作者
王建龙
王越支
邱卫红
于琛
张菲菲
王学迎
WANG Jianlong;WANG Yuezhi;QIU Weihong;YU Chen;ZHANG Feifei;WANG Xueying(School of Petroleum Engineering,Yangtze University,Wuhan,Hubei,430100,China;Research Institute of Engineering and Technology,CNPC Bohai Drilling Engineering Company Limited,Tianjin,300280,China)
出处
《石油钻探技术》
CAS
CSCD
北大核心
2024年第5期105-116,共12页
Petroleum Drilling Techniques
基金
湖北省重点研发计划项目“鄂西页岩气长水平井智能钻井数字孪生技术及装备研究”(编号:2023BCB111)
中国石油集团渤海钻探工程有限公司科技重大研发项目“钻完井智能综合分析决策系统开发”(编号:2022ZD01F-03)、“基于地面及井下监测数据的钻井智能辅助决策系统开发”(编号:2023ZD04F-02)联合资助。
关键词
大数据
融合模型
智能决策
辅助决策
风险预警
智能钻井
big data
fusion model
intelligent decision-making
decision support
risk alerting
intelligent drilling
作者简介
王建龙(1984-),山东沂水人,2010年毕业于中国石油大学胜利学院油气储运专业,2013年获中国石油大学(华东)油气井工程专业硕士学位,在读博士研究生,高级工程师,主要从事钻井软件、钻井提速工具研发与应用等方面的工作。E-mail:383462010@qq.com。
