井漏风险层位钻前智能识别方法研究  

Research on the intelligent pre-drilling identification method of thief zone with lost circulation risk

作　　者：卢运虎[1,2,3] 金衍[1,2,3] 王汉青 耿智[3] LU Yunhu;JIN Yan;WANG Hanqing;GENG Zhi(College of Artificial Intelligence,China University of Petroleum-Beijing,Beijing 102249,China;State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum-Beijing,Beijing 100049,China;College of Petroleum Engineering,China University of Petroleum-Beijing,Beijing 100049,China;Petroleum Production Research Department,SINOPEC Petroleum Exploration and Production Research Institute,Beijing 102206,China)

机构地区：[1]中国石油大学(北京)人工智能学院,北京102249 [2]中国石油大学(北京)油气资源与工程全国重点实验室,北京102249 [3]中国石油大学(北京)石油工程学院,北京102249 [4]中国石化石油勘探开发研究院油田开发研究所,北京102206

出　　处：《石油科学通报》2024年第4期574-585,共12页Petroleum Science Bulletin

基　　金：国家自然科学基金面上项目(52074314);国家重点研发计划(2019YFA0708303)联合资助。

摘　　要：井漏是复杂地层钻井工程常遇到的工程难题,呈现出频发性、随机性与持续性等特征,钻前准确预测井漏风险层位对于安全钻井显得尤为重要。传统井漏层位分析偏重于随钻诊断和钻后总结,主要采用工程数据与现场经验相结合的手段,导致分析结果存在滞后性,无法在钻前有效指导钻井工程设计。本文以地震属性体数据和漏失工程数据为基础,在具有典型漏失特征单井选取的基础上,提取过井地震属性体数据,通过时深关系将漏失与地震属性相匹配,并采用随机森林方法甄别优选出与井漏预测相关性强的地震属性体,然后运用机器学习方法中的软投票算法建立集成学习模型,该模型融合了逻辑回归、随机森林和支持向量机3个子模型,实现了多元地震属性体与漏失工程数据之间的非线性映射关系及其对应权重的表征,同时获得基于地震与工程数据融合驱动的漏失风险层位分布概率,实现钻前井漏风险层位三维空间分布预测。研究结果表明,方差、时频衰减、甜点和均方根振幅与井漏的相关性最高,综合上述多种属性体可以实现更为精确的井漏风险预测,而过多增加地震属性数据并不能显著提升预测效果精度,相反还会增加计算成本。与单一机器学习模型相比,集成学习模型由于融合了多个子模型的优点,能够取得更好的预测效果。实际应用效果表明,采用地震属性体进行漏失风险预测,其精度取决于地震数据的采样率,井漏风险层位区域横向预测分辨率约为25 m,纵向预测分辨率约为6m(2ms),预测结果表明横向相比于纵向更为可靠。但由于时深关系的影响,可能导致纵向预测精度的偏移。本研究能够较好的进行钻前漏失预测,为钻前漏失预测提供了一种新的思路,对于指导井位部署、井眼轨道优化以及安全钻井具有重要意义。Lost circulation is a common problem encountered in complex formation drilling engineering,which is characterized by frequent occurrence,randomness and persistence.Accurate prediction of potential thief zone before drilling is particularly important for safe drilling.The traditional analysis of lost circulation is focused on diagnosis while drilling and summary after drilling,mainly using the means of combining engineering data and field experience,which leads to the lag of analysis results and cannot effectively guide drilling engineering design before drilling.Based on seismic attributes and lost circulation engineering data,this paper extracted the seismic attributes of drilled wells on the basis of the selection of single wells with typical lost circulation characteristics,and selected the seismic attributes with strong correlation with lost circulation prediction by time-depth relationship and adopted random forest method to identify and select the seismic attributes with strong correlation with lost circulation prediction.Then,an ensemble learning model was established by using soft voting algorithm in machine learning method.The model integrates three sub-models named logistic regression,random forest and support vector machine,and realizes the nonlinear mapping relationship between multiple seismic attributes and lost circulation engineering data and the corresponding weight characterization.At the same time,the probability of lost circulation risk distribution driven by the fusion of seismic and engineering data is obtained,and the 3D spatial distribution prediction of pre-drilling lost circulation risk layer is realized.The results show that variance,time-frequency attenuation,sweet spot and root mean square amplitude have the highest correlation with lost circulation.Combining the above attributes can achieve more accurate lost circulation risk prediction.How-ever,excessive addition of seismic attributes cannot significantly improve the prediction accuracy,on the contrary,it will increase the calculati

关 键 词：井漏风险 地震属性体 机器学习 钻前预测 复杂地层 

分 类 号：TE2[石油与天然气工程—油气井工程] TP181[自动化与计算机技术—控制理论与控制工程]