基于机器学习的储层预测方法  

Reservoir prediction method based on machine learning

作　　者：史长林 魏莉 张剑 杨丽娜 SHI Changlin;WEI Li;ZHANG Jian;YANG Lina(CNOOC EnerTech-Drilling&Production Co.,Ltd.,Tianjin City,300450,China)

机构地区：[1]中海油能源发展股份有限公司工程技术分公司,天津300450

出　　处：《油气地质与采收率》2022年第1期90-97,共8页Petroleum Geology and Recovery Efficiency

基　　金：中国海洋石油集团有限公司科研项目“基于深度机器学习的油气储层预测技术”(NFGJ2019-06);“双重介质碳酸盐岩油藏调驱/堵控水技术研究与应用”(NFGJ2019-05)。

摘　　要：机器学习和数据挖掘具有出色的预测、分析、决策和计算能力,在油气勘探开发领域的应用已取得良好的效果。在总结储层预测方法的基础上,分析了不同储层预测方法的适用性及优缺点,应用机器学习方法,挖掘测井和地震数据,预测了储层的岩石类型、空间展布、孔隙度、渗透率和含油饱和度。将该方法与地震反演储层预测对比,结果表明该方法具有明显优势。一是挖掘地震数据蕴含的大量信息并进行多重属性融合,使预测精度提高;二是数据驱动代替经验驱动,使工作流程简化。Machine learning and data mining possess excellent abilities of prediction,analysis,decision-making,and calculation and have achieved good results in the field of oil and gas exploration and development.On the basis of summarizing the reservoir prediction methods,this paper analyzes the applicability,advantages and disadvantages of different reservoir prediction methods.It utilizes the machine learning algorithm to predict the rock type,spatial distribution,porosity,permeability,and oil saturation of the reservoir by mining logging and seismic data.This method reveals evident advantages compared with seismic inversion reservoir prediction:first,mining a large amount of information contained in seismic data and multi-attribute fusion can improve the prediction accuracy;second,data-driven instead of experience-driven can simplify the workflow.

关 键 词：机器学习 数据挖掘 岩性预测 物性预测 含油气性预测 

分 类 号：TE319[石油与天然气工程—油气田开发工程]