基于灰狼算法优化的支持向量机产能预测

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。

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

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.