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automatic well test interpretation based on convolutional neural network for a radial composite reservoir

Petroleum Exploration and Development 2020年第3期47卷 623-631页

作者： LI Daolun LIU Xuliang ZHA Wenshu YANG Jinghai LU Detang Hefei University of Technology Hefei 230009China Daqing Well Logging Technology Service Company Daqing 163453China University of Science and Technology of China Hefei 230026China

An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network(CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed by logarithm function and the loss function of mean square error(MSE), the optimal CNN is obtained by reducing the loss function to optimize the network with "dropout" method to avoid over fitting. The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir, that is, the log-log plot of the given measured pressure variation and its derivative data are input into the network, the outputs are corresponding reservoir parameters(mobility ratio, storativity ratio, dimensionless composite radius, and dimensionless group characterizing well storage and skin effects), which realizes the automatic initial fitting of well test interpretation parameters. The method is verified with field measured data of Daqing Oilfield. The research shows that the method has high interpretation accuracy, and it is superior to the analytical method and the least square method.

关键词： radial composite reservoir well testing interpretation convolutional neural network automatic interpretation artificial intelligence

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A deep reinforcement learning(DRL)based approach for well-testing interpretation to evaluate reservoir parameters

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Petroleum Science 2022年第1期19卷 264-278页

作者： Peng Dong Zhi-Ming Chen Xin-Wei Liao Wei Yu State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum Beijing(CUP)Beijing102249China University of Texas at Austin Austin78731TexasUSA

Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.

关键词： Well testing Deep reinforcement learning automatic interpretation Parameter evaluation

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A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data

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Petroleum Research 2022年第2期7卷 204-219页

作者： Rakesh Kumar Pandey Anil Kumar Ajay Mandal Department of Petroleum and Energy Studies School of Engineering and TechnologyDIT UniversityDehradun248009India Data Science Research Group School of ComputingDIT UniversityDehradun248009India Department of Petroleum Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India

A robust deep learning model consisting of long short-term memory and fully connected neural net-works has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite,no flow,and constant pressure outer boundary conditions.The pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and,further,regression to estimate output parameter.Gaussian noise was added to analytical models while generating the synthetic training data.The hyperparameters were regulated to perform model optimization,resulting in a batch size of 64,Adam optimization algorithm,learning rate of 0.01,and 80:10:10 data split ratio as the best choices of hyperparameters.The perfor-mance accuracy also increased with an increase in the number of samples during training.Suitable classification and regression metrics have been used to evaluate the performance of the models.The paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test cases.The proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308,respectively,in estimating output for the simulated test cases and relative error of 0.0319 for the real test case.

关键词： Well test Reservoir characterization automatic interpretation Prediction model Hyperparameter tuning Performance indicator

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