基于XGBoost的低渗油田储层粒度预测

Prediction of Reservoir Grain Size in Low Permeability Oilfield Based on XGBoost

针对低渗油田储层粒度预测问题,本文提出利用机器学习中的极致剃度提升树(extreme gradient boosting,XGBoost)来对低渗油田储层粒度进行预测的方案.首先,根据问题构建合适的XGBoost模型,然后根据已有的岩心储层粒度特征值与其余测井信息的关系,选取适用于粒度预测的测井曲线建立样本库,最后利用样本库数据对建立的XGBoost模型进行训练,训练后的模型即可预测研究区域未知的储层粒度特征.结果表明,本文所设计的XGBoost模型对低渗油田的储层粒度预测方案在计算效率、预测准确率等方面均优于BP神经网络.

To address the prediction problems of reservoir grain sizes in low permeability oilfields,this study proposes a scheme for predicting reservoir grain sizes in low permeability oilfields with the extreme gradient boosting(XGBoost)in machine learning.First,a proper XGBoost model is built in consideration of the problems.Then,well logging curves suitable for grain size prediction are selected to create a sample database according to the established relationships of the characteristic values of the core reservoir grain size with other logging information.Finally,sample database data are employed to train the newly built XGBoost model.The trained model can predict unknown reservoir grain size characteristics in a study area.The results show that the XGBoost model designed in this study is superior to the back propagation(BP)neural network in calculation efficiency and prediction accuracy of reservoir grain sizes in low permeability oilfields.