Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Xue, Liang; Wang, Jiabao; Han, Jiangxia; Yang, Minjing; Mwasmwasa, Mpoki Sam; Nanguka, Felix

doi:10.46690/ager.2023.06.03

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2024

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Cited by 7 publications

References 24 publications

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Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian

Tang,

He,

et al. 2024

Petroleum Science

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Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian

Tang,

He,

et al. 2024

Petroleum Science

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Predicting the productivity of fractured horizontal wells using few-shot learning

Wang,

Ge,

Zhang

et al. 2024

Petroleum Science

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A physical constraint-based machine learning model for shale oil production prediction

Wang,

Lei,

Zhou

et al. 2024

Physics of Fluids

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Shale oil has become a crucial unconventional resource, bolstering energy supply security, and it is important to accurately predict shale oil production dynamics. However, traditional prediction methods are faced with the challenges of large data fluctuations and numerous interference factors, which make accurate prediction difficult. This paper introduces a deep learning approach, specifically a physical constraint-based convolutional neural network combined with long short-term memory and attention mechanism (CNN-LSTM-AM), to predict shale oil production dynamics. Initially, the correlation analysis method is used to analyze the correlation strengths of features with the prediction target, and the data that are most strongly correlated with the daily oil production are screened out and preprocessed. Key features are then extracted; that is, the CNN layer's output is fed into the LSTM layer, the output of which informs a fully connected layer for time-series production predictions. The model integrates an AM to concentrate on vital data aspects. In the “five-linear flow” formula, the imbibition is hard-coded into the shale oil production prediction model. Physical constraints are introduced into the model. Data driven and empirical formulas are used to introduce a loss function as a constraint condition in the training process of a machine learning model to improve the interpretability and predictive ability of the model. During the tuning phase, Bayesian optimization is used to fine-tune the model's hyperparameters. Shapley additive explanation and accumulated local effects analyses are used to further assess and quantify the significance of the essential parameters, thus optimizing the prediction effectiveness.

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Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Abstract: Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data.

Cited by 7 publications

References 24 publications

Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian

Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian

Predicting the productivity of fractured horizontal wells using few-shot learning

A physical constraint-based machine learning model for shale oil production prediction

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