Shale gas plays an important role in supplementing energy demand and reducing carbon footprint. A precise and effective prediction of shale gas production is important for optimizing completion parameters. This paper established a gated recurrent unit and multilayer perceptron combined neural network (GRU-MLP model) to forecast multistage fractured horizontal shale gas well production. A nondominated sorting genetic algorithm II (NSGA II) was introduced into the model to enable its automatic architectural optimization. In addition, embedded discrete fracture models (EDFM) and a reservoir simulator were used to generate training datasets. Meanwhile, a sensitivity analysis was carried out to find the variable’s importance and support the history matching. The results illustrated that the GRU-MLP model can precisely and efficiently predict the productivity of multistage fractured horizontal shale gas in a rapid and effective manner. Additionally, the model fits better at peak values of shale gas production. The GRU-MLP hybrid model has a higher accuracy within an acceptable computational time range compared to recurrent neural networks (RNN), long short-term memory (LSTM), and GRU models. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) for shale gas production generated by GRU-MLP model were 3.90% and 3.93%, respectively, values 84.87% and 84.88% smaller than those of the GRU model. Consequently, compared with a purely data-driven method, the physics-constrained data-driven method behaved better. The main results of the study will hopefully contribute to the intelligent development of shale gas production prediction.
Coalbed methane (CBM) has emerged as one of the clean unconventional resources to supplement the rising demand of conventional hydrocarbons. Analyzing and predicting CBM production performance is critical in choosing the optimal completion methods and parameters. However, the conventional numerical simulation has challenges of complicated gridding issues and expensive computational costs. The huge amount of available production data that has been collected in the field site opens up a new opportunity to develop data-driven approaches in predicting the production rate. Here, we proposed a novel physics-constrained data-driven workflow to effectively forecast the CBM productivity based on a Gated Recurrent Unit (GRU) and Multi-Layer Perceptron (MLP) combined neural network (GRU-MLP model). The model architecture is optimized by the multiobjective algorithm: nondominated sorting genetic algorithm Ⅱ (NSGA Ⅱ). The proposed framework was used to predict synthetic cases with various fracture-network-complexities and two multistage-fractured wells in field sites located at Qinshui basin and Ordos basin, China. The results indicated that the proposed GRU-MLP combined neural network was able to accurately and stably predict the production performance of multi-fractured horizontal CBM wells in a fast manner. Compared with Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the proposed GRU-MLP had the highest accuracy and stability especially for gas production in late-time. Consequently, a physics-constrained data-driven approach performed better than a pure data-driven method. Moreover, the optimum GRU-MLP model architecture was a group of optimized solutions, rather than a single solution. Engineers can evaluate the tradeoffs within this set according to the field-site requirements. This study provides a novel machine learning approach based on a GRU-MLP combined neural network model to estimate production performances in CBM wells. The method is simple and gridless, but is capable of predicting the productivity in a computational cost-effective way. The key findings of this work are expected to provide a theoretical guidance for the intelligent development in oil and gas industry.
Summary Coalbed methane (CBM) has emerged as one of the clean unconventional resources to supplement the rising demand of oil and gas. Analyzing and predicting CBM production performance are critical in choosing the optimal completion methods and parameters. However, the conventional numerical simulation has challenges of complicated gridding issues and expensive computational costs. The huge amount of available production data that has been collected in the field site opens up a new opportunity to develop data-driven approaches in predicting the production rate. Here, we proposed a novel physics-constrained data-driven workflow to effectively forecast the CBM productivity based on a gated recurrent unit (GRU) and multilayer perceptron (MLP) combined neural network (GRU-MLP model). The model architecture is optimized automatically by the multiobjective algorithm: nondominated sorting genetic algorithm Ⅱ (NSGA Ⅱ). The proposed framework was used to predict gas and water production in synthetic cases with various fracture-network-complexity/connectivity and two multistage fractured horizontal wells in field sites located at Ordos Basin and Qinshui Basin, China. The results indicated that the proposed GRU-MLP combined neural network was able to accurately and stably predict the production performance of CBM fractured wells in a fast manner. Compared with recurrent neural network (RNN), GRU, and long short-term memory (LSTM), the proposed GRU-MLP had the highest accuracy, stability, and generalization, especially in the peak or trough and late-time production periods, because it could capture the production-variation trends precisely under the static and dynamic physical constraints. Consequently, a physics-constrained data-driven approach performed better than a pure data-driven method. Moreover, the contributions of constraints affecting the model prediction performance were clarified, which could provide insights for the practicing engineers to choose which categorical constraints are needed to focus on and preferentially treated if there are uncertainties and unknowns in a realistic reservoir. In addition, the optimum GRU-MLP model architecture was a group of optimized solutions, rather than a single solution. Engineers can evaluate the tradeoffs within this optimal set according to the field-site requirements. This study provides a novel machine learning approach based on a GRU-MLP combined neural network to estimate production performances in naturally fractured reservoir. The method is gridless and simple, but is capable of predicting the productivity in a computational cost-effective way. The key findings of this work are expected to provide a theoretical guidance for the intelligent development in oil and gas industry.
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