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Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the deep neural network with physical constraints

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Petroleum Science 2023年第2期20卷 1129-1141页

作者： Hong-Yan Qu Jian-Long Zhang Fu-Jian Zhou Yan Peng Zhe-Jun Pan Xin-Yao Wu State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China School of Artificial Intelligence China University of PetroleumBeijing102249China Unconventional Oil and Gas Institute China University of PetroleumBeijing102249China School of Petroleum Engineering China University of PetroleumBeijing102249China Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development Ministry of EducationNortheast Petroleum UniversityDaqingHeilongjiang163318China Engineering Technology Research Institute PetroChina Xinjiang Oilfield CompanyKaramayXinjiang834000China

Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.

关键词： Evaluation of fracturing effects Tight reservoirs Physical constraints Deep neural network Horizontal wells combined neural network

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Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT

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Global Energy Interconnection 2023年第5期6卷 530-541页

作者： Wei Liu Feifei Xue Yansong Gao Wumaier Tuerxun Jing Sun Yi Hu Hongliang Yuan Northwest Engineering Corporation Limited Xi’an 710000P.R.China College of Energy and Electrical Engineering Hohai UniversityNanjing 210098P.R.China

Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.

关键词： Wind-speed forecasting DBN Elman HHT combined neural network

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