基于XGBoost算法的vP/vS预测及其在储层检测中的应用OA北大核心CSTPCD

There are abundant oil and gas resources entrapped in the carbonate reservoirs of the Ordos Basin.However,exploration results showed that the Majiagou Formation in the Daniudi Gas Field had developed mul-tiple kinds of faults with small fault throws due to complex origins,which brings many challenges to its explora-tion and development.To address these challenges,it is crucial to optimize the sensitive elastic parameters for reservoir prediction.Therefore,the relationship between seismic attributes and the velocity ratio of compres-sional to shear waves(vP/vs)in the reservoir has been established,based on the analysis of elastic-sensitive pa-rameters in the Daniudi Gas Field.Then,a prediction method for the vP/vS based on the XGBoost algorithm and multiple seismic attributes is proposed.To further improve the performance and generalization ability of the model,the hyperparameters of the XGBoost algorithm are optimized by Bayesian algorithm.This approach aims to find the optimal combination of hyperparameters,ensuring improved performance of the model on both training and testing datasets.The XGBoost algorithm is applied to the Marmousi 2 model for predicting shear wave velocity,achieving a correlation coefficient between predicted and actual values exceeding 0.88.With root mean squared error and mean absolute percentage error below 6.55X 10 7 and 4％respectively,the accu-racy and reliability of the proposed method are demonstrated.The method applied in the Daniudi Gas Field of the Ordos Basin has successfully identified gas-bearing reservoirs,and the results are consistent with actual dril-ling data.Both theoretical model and practical data indicate that XGBoost,as a powerful machine learning algo-rithm,exhibits high accuracy,which can provide an effective approach for directly predicting vP/vS from post-stack seismic attributes.