基于灰狼算法优化的支持向量机产能预测  

Productivity forecast based on support vector machine optimized by grey wolf optimizer

作　　者：宋宣毅 刘月田 马晶 王俊强 孔祥明 任兴南 SONG Xuanyi;LIU Yuetian;MA Jing;WANG Junqiang;KONG Xiangming;REN Xingnan(State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing 102249,China)

机构地区：[1]中国石油大学(北京)油气资源与探测国家重点实验室,北京102249

出　　处：《岩性油气藏》2020年第2期134-140,共7页Lithologic Reservoirs

基　　金：国家自然科学基金项目“各向异性裂缝页岩气藏渗流机理与理论研究”(编号:51374222);国家重点基础研究发展计划“陆相致密油高效开发基础研究”课题五之专题一“致密油藏产能预测方法”(编号:2015CB250905);国家重大专项“中东典型碳酸盐岩油藏改善水驱开发效果关键技术研究”(编号:2017ZX05032004-002);中国石油重大科研专项“新疆和吐哈油田勘探开发关键技术研究与应用”课题5“火山岩油藏效益开发关键技术研究与应用”(编号:2017E-0405);中国石化重点科技项目“层状砂砾岩未开发储量有效动用评价研究”(编号:P18049-1)联合资助。

摘　　要：针对常规的线性回归以及经验公式等油井初期产能预测方法应用范围有限、预测误差较大,并且难以表征初产在多因素影响下的非线性变化规律等问题,提出了基于机器学习算法的产能预测方法。以某特低渗油田为例,从地质、开发和工程3个方面,选择了影响初期产能的10种因素,采用皮尔逊相关关系分析了各因素之间的线性相关性,使用随机森林方法确定了初期产能的主控因素,首次采用灰狼算法(GWO)优化的支持向量机(SVM)建立了油井初期产能的预测模型。结果表明:特低渗油田初期产能的主控因素为:压裂加砂量,射孔段厚度,初始含水饱和度,油层有效厚度和加砂强度;与多元线性回归模型和网格寻优的支持向量机模型相比,灰狼算法优化的支持向量机初期产能预测模型精度高而且运算速度快。研究结果可为油井初期产能评估提供参考。Conventional linear regression and empirical formulas for predicting initial productivity have limited application scope and large prediction error,and are difficult to characterize the nonlinear variation of initial production under the influence of multiple factors.Therefore,machine learning algorithm was introduced to improve the accuracy of the prediction.Taking an ultra-low permeability oilfield as an example,ten factors affecting the initial productivity were selected from three aspects of geology,development,and engineering,and the linear correlation among these factors was analyzed by Pearson correlation.Random Forests was applied to identify the main controlling factors of initial productivity.Support vector machine(SVM)optimized by grey wolf optimizer(GWO)was used to establish the prediction model of initial productivity of oil well for the first time.The results show that the main controlling factors of initial productivity in ultra-low permeability oilfields are sand volume of the fracturing,perforation thickness,initial water saturation,effective oil layer thickness and sand intensity of fracturing.The GWO-SVM model has faster operation speed and higher accuracy compared with multivaluable linear regression and SVM optimized by grid searching.The research results can provide reference for the initial productivity evaluation of oil wells.

关 键 词：机器学习 随机森林 灰狼算法 支持向量机 产能预测 

分 类 号：P631.4[天文地球—地质矿产勘探]