The lithology of the Fengcheng Formation in Mahu Sag,Junggar Basin is complex.To accurately predict its rock mechanical parameters,this paper proposes an adaptive weight combination forecast method.Firstly,the paper analyzes and compares the predictive performance of traditional methods and different ma-chine learning algorithms(BP neural network,XGBoost,support vector machine(SVM),random forest(RF),convolutional neural network(CNN),Classifation and regression tree(CART),long-short term memory neural(LSTM)network,etc.).Traditional methods are difficult to achieve accurate forecasts of rock mechanical pa-rameters,while different machine learning algorithms have different predictive effects.The optimal machine learning algorithm model for predicting compressive strength,tensile strength,and brittleness index is SVM.The optimal models for predicting elastic modulus,Poisson's ratio,and cohesion are BP,RF,and XGBoost,respectively.The optimal model for predicting internal friction angle and fracture toughness is LSTM network.A single machine learning algorithm is difficult to achieve synchronous and accurate forecasts of multiple rock mechanical parameters.On this basis,adaptive weight combination forecast is carried out by selecting different forecast base models for different rock mechanical parameters,assigning weights based on the forecast effect of the base models,and combining them.The results show that this method can effectively improve the forecast accuracy and generalization performance of machine learning algorithms and can achieve synchronous and ac-curate forecasts of multiple rock mechanical parameters in complex lithological formations.
关键词
岩石力学参数/复杂岩性地层/机器学习/自适应组合预测
Key words
rock mechanical parameters/complex lithologic formations/machine learning/adaptive combination forecast
维普
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