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The shale gas productivity model based on shale gas nonlinear seepage mechanism is an effective way to reasonably predict productivity. The incomplete gas nonlinear effects considered in the current production prediction models can lead to inaccurate production prediction. Based on the conventional five-zone compound flow model, comprehensive gas nonlinearities were considered in the improved compound linear flow model proposed in the paper and a semianalytical solution for productivity was obtained. The reliability of the productivity model was verified by the field data, and then, the 20-year production performance analysis of the gas well was studied. Ultimately, the key influencing factors of the fracture control stage and matrix control stage have been analyzed. Research indicated the following: (1) the EUR predicted by the productivity model is higher than the EUR that the comprehensive nonlinear effects are not considered, which demonstrated that the various nonlinear effects cannot be neglected during the production prediction to ensure the greater calculation accuracy; (2) during the early production stage of shale reservoir, the adsorbed gas is basically not recovered, and the cumulative adsorption contribution rate does not exceed 10%. The final adsorption gas contribution rate is 23.28%, and the annual adsorption rate can exceed 50% in the 20th year, showing that free gas and adsorbed gas are, respectively, important sources of the early stage of production and long-term stable production; (3) the widely ranged three-dimensional fracturing reformation of shale reservoirs and reasonable bottom hole pressure in the later matrix development process should be implemented to increase the effective early production of the reservoir and ensure the earlier gas production process of the matrix development. The findings of this study can help for better ensuring the prediction accuracy of the estimated ultimate recovery and understanding the main influencing factors of the dynamic performance of gas wells so as to provide a theoretical reference for production optimization and development plan formulation of the shale gas reservoirs. [ABSTRACT FROM AUTHOR]

The shale gas productivity model based on shale gas nonlinear seepage mechanism is an effective way to reasonably predict productivity. The incomplete gas nonlinear effects considered in the current production prediction models can lead to inaccurate production prediction. Based on the conventional five-zone compound flow model, comprehensive gas nonlinearities were considered in the improved compound linear flow model proposed in the paper and a semianalytical solution for productivity was obtained. The reliability of the productivity model was verified by the field data, and then, the 20-year production performance analysis of the gas well was studied. Ultimately, the key influencing factors of the fracture control stage and matrix control stage have been analyzed. Research indicated the following: (1) the EUR predicted by the productivity model is higher than the EUR that the comprehensive nonlinear effects are not considered, which demonstrated that the various nonlinear effects cannot be neglected during the production prediction to ensure the greater calculation accuracy; (2) during the early production stage of shale reservoir, the adsorbed gas is basically not recovered, and the cumulative adsorption contribution rate does not exceed 10%. The final adsorption gas contribution rate is 23.28%, and the annual adsorption rate can exceed 50% in the 20th year, showing that free gas and adsorbed gas are, respectively, important sources of the early stage of production and long-term stable production; (3) the widely ranged three-dimensional fracturing reformation of shale reservoirs and reasonable bottom hole pressure in the later matrix development process should be implemented to increase the effective early production of the reservoir and ensure the earlier gas production process of the matrix development. The findings of this study can help for better ensuring the prediction accuracy of the estimated ultimate recovery and understanding the main influencing factors of the dynamic performance of gas wells so as to provide a theoretical reference for production optimization and development plan formulation of the shale gas reservoirs. [ABSTRACT FROM AUTHOR]

Predicting the production behaviors of shale gas wells is of great importance for further developing future unconventional hydrocarbon strategies. An accurate prediction production, as well as reliable shale gas production models, are required to fully understand the shale gas exploitation budget. However, a major problem with classical analytic methods is the insufficient accuracy of the existing models, the time-consuming collection of historical production data, and the costly computational expense. To minimize this problem, a combination of the exponential smoothing method, autoregressive integrated moving average (ARIMA) model, and long short-term memory (LSTM) model was proposed to provide robust support for the production behaviors of shale gas. In this paper, we employed shale gas well production data to establish a database for model training and optimized the predicted model. Hereby, we sought to evaluate the production data predicted by conventional analytical methods, the exponential smoothing method, the ARIMA model, and the LSTM model. Shortly afterward, we objectively compared the predicted results obtained by the novel LSTM model and traditional analytical methods, such as Arps, stretched exponential decline (SEPD), and the Duong model. Herein, we compared the computational cost between the LSTM model and traditional numerical simulation. The combined interpretation of the proposed model demonstrates that the LSTM model achieved scientific accuracy and outstanding results in both short-term and long-term predictions, and realized production prediction of the adjacent well, with excellent agreement with the real shale gas production and a low error, making it an effective tool in forecasting shale gas production. This assay could be used as a potential approach for evaluating deep learning in the petroleum industry and for predicting the future production of unconventional hydrocarbons. [Display omitted] • A framework for shale gas production potential estimation was developed. • A deep-learning-based time series analysis model was built to predict the short-term and longterm shale gas production, based on thousands of in-situ production data samples from three shale gas wells. • Computational cost of the shale gas production prediction was significantly reduced with proposed long short-term memory model. • Objectively compare the advantage and drawback forecasting production among conventional analytical methods, exponential smoothing algorithm, ARIMA, and LSTM model. • The study shed lights on the few-shot learning applications in shale gas production prediction. [ABSTRACT FROM AUTHOR]