A shale gas production prediction model based on masked convolutional neural network

Wei Zhou, Xiangchengzhen Li, ZhongLi Qi, HaiHang Zhao and Jun Yi

Applied Energy, 2024, vol. 353, issue PA, No S0306261923014563

Abstract: Shale gas production prediction is of great significance for shale gas exploration and development, as it can optimize exploration strategies and guide adjustments to production parameters for both new and existing wells. However, the dynamic production characteristics of shale gas wells under the influence of multiple factors such as reservoirs, engineering, and production, exhibit complex nonlinear and non-stationary features, leading to low accuracy in predicting shale gas production. To address this issue, a novel masked convolutional neural network (M-CNN) based on masked autoencoders (MAE) is proposed for shale gas production prediction. First, high-dimensional shale gas production data are transformed into images with unknown information using an encoding structure, thereby converting the regression task into images generation task. Then, convolutional neural network is used for image restoration prediction, and the corresponding numerical values at the image positions are extracted as shale gas production prediction results. Specifically, dilated convolution and multi-scale residual structure (MSRS) are developed to improve the feature representation capability of the network. Meanwhile, convolutional block attention module (CBAM) is adopted to enhance the feature extraction ability of the M-CNN. The performance of our method is validated experimentally on shale gas production data of Changning (CN) block in China. The average RMSE, MRE, and R2 on the test sets are 0.211 (104m3/d), 10.9%, and 0.906, respectively, which is much lower than the traditional time series models. Experimental results demonstrate the effectiveness and superiority of the proposed M-CNN method for shale gas production prediction.

Keywords: Shale gas production prediction; CNN; Mask mechanism; Data analysis (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2023.122092

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