基于GWO-SVM算法的油气井产能预测模型研究  被引量:2

Research on productivity prediction model of oil and gas well based on GWO-SVM algorithm

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作  者:杨毅 赵洪绪 袁胜斌 Yang Yi;Zhao Hongxu;Yuan Shengbin(China-France Bohai Geoservices Co.,Ltd.,Tianjin 300457,China)

机构地区:[1]中法渤海地质服务有限公司,天津300457

出  处:《能源与环保》2024年第2期178-183,共6页CHINA ENERGY AND ENVIRONMENTAL PROTECTION

基  金:国家重大专项大型油气田及煤层气开发(2016ZX05017)。

摘  要:油气井产能对于油藏完井方式选择及相关作业起着决定性作用,是油气藏开发的关键指标之一。当前基于机器学习算法的油气井产能预测过程中预测结果受样本数据影响明显。针对支持向量机方法和灰狼算法在处理小数据样本时的特征及优势,将支持向量机与灰狼算法相结合,形成了灰狼算法—支持向量机算法(GWO-SVM算法)。利用某油田数据实际井数据对优化前后的算法及当前常用的机器学习算法进行对比测试,结果表明,优化后的GWO-SVM算法在计算速度和计算精度上表现出了明显优势,能更准确地确定油气井产能。研究结果对油气井产能预测具有一定指导意义。The productivity of oil and gas wells plays a decisive role in the selection of reservoir completion methods and related operations,which is one of the key indicators for reservoir development.At present,the prediction of oil and gas well productivity based on machine learning algorithm is obviously influenced by sample data.Aiming at the characteristics and advantages of the support vector machine method and the gray wolf algorithm in dealing with small data samples,combining the support vector machine method with the gray wolf algorithm to form the gray wolf algorithm-the support vector machine SVM algorithm(GWO-SVM algorithm).The algorithm before and after optimization and the commonly used machine learning algorithm were compared and tested by using the actual well data of an oilfield.The results show that the optimized GWO-SVM algorithm exhibits significant advantages in terms of computational speed and accuracy,and can more accurately determine the production capacity of oil and gas wells.The research results have some guiding significance for the productivity prediction of oil and gas wells.

关 键 词:油气井 产能预测 支持向量机算法 灰狼算法 GWO-SVM算法 

分 类 号:TE988.2[石油与天然气工程—石油机械设备]

 

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