Adaptive weight combination forecast of rock mechanical parameters in the Fengcheng Formation of Mahu Sag
TANG Junfang1, XIONG Jian1,2, LIU Xiangjun1, GAN Renzhong3, LUO Dejiang4, LIANG Lixi1
1. National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China; 2. National Center for International Research on Deep Earth Drilling and Resource Development, Ministry of Science and Technology China University of Geosciences (Wuhan), Wuhan, Hubei 430074, China; 3. PetroChina Xinjiang Oilfield Company, Karamay, Xinjiang 834000, China; 4. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, Sichuan 610059, China
Abstract：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 machine 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 parameters， 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 accurate forecasts of multiple rock mechanical parameters in complex lithological formations.
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