论文详情
基于自然语言处理与大数据分析的漏失分析与诊断
石油钻探技术
2023年 51卷 第6期
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Title
Loss Analysis and Diagnosis Based on Natural Language Processing and Big Data Analysis
作者
曾义金
李大奇
陈曾伟
张杜杰
崔亚辉
张菲菲
Authors
ZENG Yijin
LI Daqi
CHEN Zengwei
ZHANG Dujie
CUI Yahui
ZHANG Feifei
单位
页岩油气富集机理与高效开发全国重点实验室,北京 102206
中石化石油工程技术研究院有限公司,北京 102206
油气钻采工程湖北省重点实验室(长江大学),湖北武汉 430100
Organization
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, 102206, China
Sinopec Research Institute of Petroleum Engineering Co., Ltd., Beijing, 102206, China
Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas(Yangtze University), Wuhan, Hubei, 430100, China
摘要
塔里木盆地西部A区块以溶蚀孔洞型、裂缝性储层为主,18条断裂带发育,断裂带附近天然裂缝分布复杂,地层承压能力低,容易发生井漏。为准确规避井漏风险,优化井漏处理技术措施,利用自然语言处理技术,提取了A区块全部完钻井的钻井资料和井漏信息,基于大数据分析汇总了易漏地层实际地层压力和实际破裂压力当量密度不确定性的分布情况,计算出了易漏地层的裂缝发育程度、裂缝宽度不确定性范围和井漏风险系数,建立了钻前井漏风险诊断方法。实例分析表明,利用所建立的钻前井漏风险诊断方法,可以在钻前诊断井漏风险,为钻完井过程中规避井漏风险和制定井漏处理技术措施提供依据。
Abstract
The Block A in the western part of the Tarim Basin are mainly karst-vuggy and fractured reservoirs. Eighteen fault zones are developed in the block. The natural fractures located near the fault zones have complex distribution and low bearing capacity of the formation, which are prone to lost circulation. In order to accurately avoid the risk of lost circulation and optimize the technical measures to deal with the lost circulation, natural language processing technology was used to extract all the drilling and completion data and lost circulation information of Block A. Based on big data analysis, the uncertainty distribution of the equivalent density of the actual formation pressure and the actual fracture pressure in the leaky formation was summarized. The uncertainty range of fracture development and fracture width, as well as the lost circulation risk coefficient of the leaky formation were calculated, and the pre-drilling lost circulation risk diagnosis method was established. The case analysis showed that the proposed method could be used to diagnose the risk of lost circulation before drilling, which can provide a basis for avoiding the risk of lost circulation and developing the technical measures for lost circulation treatment during drilling and completion.
关键词:
自然语言处理;
大数据;
漏失诊断;
当量循环密度;
裂缝发育程度;
Keywords:
natural language processing;
big data;
loss diagnosis;
equivalent circulating density;
degree of fracture development;
基金项目
国家重点研发计划项目“井筒稳定性闭环响应机制与智能调控方法”(编号:2019YFA0708303)和中国石化科技攻关项目“井筒安全风险智能诊断与调控技术研究”(编号:P21065-5)部分研究内容。
DOI
https://doi.org/10.11911/syztjs.2023108