基于随机森林算法的油气层敏感性损害预测

Prediction of Four Kinds of Sensibility Damages to Hydrocarbon Reservoirs Based on Random Forest Algorithm

储层损害贯穿在油气田勘探开发的各个时期,其种类繁多、损害机理十分复杂。传统岩心流动实验评价储层敏感性的结果可靠,但岩心获取成本高、投入时间和成本大。调研和实践表明,利用神经网络、随机森林等算法基于小规模样本建立的模型可以实现对样本的预测,节约时间和经济成本。基于X区块敏感性室内评价小规模样本资料,选择训练集及测试集,深入对比了BP神经网络算法、径向基函数神经网络算法、随机森林算法,优选出随机森林算法作为储层敏感性损害定量诊断的主要方法,采用网格搜索等算法进行了超参数优化、根据因素权重对数据进行降维,以此提高预测精度,搭建了完整的模型。4种损害模型的R2平均值为0.852,预测精度在90.00%~95.68%。

Many kinds of hydrocarbon reservoir damages with complex mechanisms have been encountered in every phase of oil and gas field exploration and development.Conventional core flow test used in evaluating the sensibility damage of a reservoir can give reliable test results,however,this test is both expensive(coring,for instance)and time consuming.Researches have shown that a model established with neural network and random forest algorithm on small-scale samples can be used to save time and money in predicting the properties of samples.In this study,the data of a set of small-scale samples tested in laboratory is obtained from the block X.The training-sets and testing-sets are then selected on the samples.By extensively comparing the results of three algorithms,which are the BP neural network algorithm,the radial basis function neural network algorithm and the random forest algorithm,the random forest algorithm is finally selected as the main method of quantitatively diagnosing the sensitivity damage of hydrocarbon reservoirs.To improve the prediction accuracy,algorithms such as grid search are used in hyperparameter optimization,and data dimensionality reduction is performed based on factor weight.A complete model is finally established based on the studies conducted.The average R2 value of the four kinds of reservoir damage model is 0.852,with a prediction accuracy between 90.00%and 95.68%.