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作 者:胜亚楠 SHENG Yanan(Drilling Engineering Technology Research Institute of Sinopec Zhongyuan Petroleum Engineering Co.Ltd.,Puyang 457001,Henan China)
机构地区:[1]中石化中原石油工程有限公司钻井工程技术研究院,河南濮阳457001
出 处:《河南科学》2023年第11期1569-1575,共7页Henan Science
基 金:中国博士后科学基金特别资助(站前)项目(2021TQ0365);中石化中原石油工程有限公司项目(2021112,2023101);中石化集团公司项目(P20010)。
摘 要:川南工区是我国页岩气资源最为丰富、最具开发潜力的地区之一.该工区地层压力系数高、地质条件复杂,导致钻井复杂、故障频发,其中卡钻故障最为突出,占复杂、故障总时效的47.48%,严重制约了页岩气安全高效开发.现有卡钻故障识别技术监控信息综合利用能力差、风险预警不够及时、主观性太强等问题较为突出.针对现场亟须解决的这一问题,提出一种基于人工智能算法的页岩气水平段井下卡钻故障实时预警方法,利用录井实时数据根据构建的预警模型实现了卡钻故障的智能化、实时化定量判断.技术的攻关和应用对于提高川南工区深层页岩气钻井单井盈利水平、降低复杂和故障损失、提高工区内队伍的施工能力具有重要意义.South Sichuan work area is one of the areas with the richest shale gas resources and the most potential for development in China.The high formation pressure coefficient and complex geological conditions in this work area lead to complex drilling and frequent failures,among which sticking failure is the most prominent,accounting for 47.48%of the total complex and failure time,which seriously restricts the safe and efficient development of shale gas.The problems of the existing identification technology of sticking fault are more prominent,such as the poor comprehensive utilization ability of monitoring information,the untimely risk warning and the strong subjectivity.In order to solve this problem,this paper proposes a real-time early warning method of drilling stuck fault in shale gas horizontal section based on artificial intelligence algorithm,which can realize the intelligent,real-time and quantitative judgment of downhole sticking fault by using real-time logging data.The research and application of this technology is of great significance to improve the profit level of single well in deep shale gas drilling in South Sichuan work area,reduce the complexity and failure loss,and improve the construction ability of the team in the work area.
关 键 词:页岩气钻井 水平井段 卡钻故障 人工智能 风险预警技术
分 类 号:TE22[石油与天然气工程—油气井工程]
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