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作 者:Song Deng Hao-Yu Pan Hai-Ge Wang Shou-Kun Xu Xiao-Peng Yan Chao-Wei Li Ming-Guo Peng Hao-Ping Peng Lin Shi Meng Cui Fei Zhao
机构地区:[1]School of Petroleum and Natural Gas Engineering,Changzhou University,Changzhou,213164,Jiangsu,China [2]Tubular Goods Research Institute of CNPC,Beijing,102249,China [3]CNPC Engineering Technology R&D Company Limited,Beijing,102206,China
出 处:《Petroleum Science》2024年第1期535-550,共16页石油科学(英文版)
摘 要:Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.
关 键 词:Pore pressure Grey wolf optimization Multilayer perceptron Effective stress Machine learning
分 类 号:TE973[石油与天然气工程—石油机械设备] TP181[自动化与计算机技术—控制理论与控制工程]
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