基于大数据驱动的低阻油层精准识别方法  

Accurate identification method of low-resistance oil layers driven by big data

作　　者：刘昕[1] 张如玉 孙琦[2] 孙玉强 牛庆威 徐思远 LIU Xin;ZHANG Ruyu;SUN Qi;SUN Yuqiang;NIU Qingwei;XU Siyuan(College of Computer Science and Technology,China University of Petroleum(East China),Qingdao City,Shandong Province,266580,China;No.1 Oil Production Plant,PetroChina Dagang Oilfield Company,Tianjin City,300280,China)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,山东青岛266580 [2]中国石油大港油田分公司第一采油厂,天津300280

出　　处：《油气地质与采收率》2022年第1期30-36,共7页Petroleum Geology and Recovery Efficiency

基　　金：中国石油重大科技项目“塔里木盆地深层油气高效勘探开发理论及关键技术研究”(ZD2019-183-001);中央高校基本科研业务费专项“基于人工智能的储层多尺度评价方法研究”(20CX05018A);上海工业控制系统安全创新功能型平台开放课题项目“数据驱动的工业设备故障诊断与预测模型研究”(TICPSH202003015-ZC)。

摘　　要：目前中国大部分油田开发进入后期,常规油气逐渐开采殆尽,低阻油层等非常规油气藏成为重要探测目标。在复杂断块油藏中,受沉积微相、构造以及层间干扰等多因素所致,单纯依靠专家经验人工识别准确率不高且效率较低。应用大数据挖掘技术,以小层数据为切入点,融合测井与研究成果资料筛选并核实低阻层;应用并行关联规则算法挖掘小层的含油性相关参数关系;基于聚类分析算法进行小层分类,对包含已核实低阻油层类小层进行相似度计算,实现低阻油层识别。通过对东部地区某油田大量数据分析表明,大数据驱动的低阻油层精准识别方法可以有效地实现低阻油层的挖潜,识别准确率达90%,并将优选的潜力层在油田生产实施,获得了良好增油效果。该方法在油田的应用节省了大量人力,降低了开发成本,提高了采收率。Most oilfields in China have entered the late stage of development,and the conventional oil and gas reserves are gradually exhausted.Therefore,unconventional oil and gas reservoirs such as low-resistance oil layers have become important targets of exploration.In complicated fault-block reservoirs,affected by multiple factors such as sedimentary microfacies,structure,and interlayer interference,manual identification is inaccurate and inefficient,simply relying on expert experience.In this regard,big data mining technology was adopted.Firstly,the low-resistance oil layers were screened and verified with sub-layer data as a pointcut through the combination of logging data and research results;then the relationships between oil-bearing-related parameters of sub-layers were analyzed by the parallel association rule algorithm;finally,all sub-layers were classified by clustering analysis algorithm,and the similarity on the sub-layers containing verified low-resistance oil layers were calculated.As a consequence,the low-resistance oil layers were identified.The analysis of substantial data from an oil field in the eastern region shows that the accurate identification method of low-resistance oil layers driven by big data can tap the potential of low-resistance oil layers,with an accuracy rate of 90%.The potential reservoirs selected in the oilfield were put into production,with a great oil increment.This method saved massive manpower,reduced development costs,and enhanced oil recovery.

关 键 词：油层识别 快速聚类 相似度计算 并行关联规则 大数据 

分 类 号：TE319[石油与天然气工程—油气田开发工程]