New methods of coalbed methane production analysis based on the generalized gamma distribution and field applications

Xian-min Zhang, Bai-yan-yue Chen, Zhuang-zhuang Zheng, Qi-hong Feng and Bin Fan

Applied Energy, 2023, vol. 350, issue C, No S0306261923010930

Abstract: Decline curve analysis is a simple and fast regression method of estimating recoverable reserves and future performance, but it is challenged when applied to unconventional reservoirs. Based on a generalized Gamma distribution in the real number field, a theoretical forecasting model describing the whole-process change of coalbed methane well production was constructed, that is, the gas production can be estimated by the early rising section data and a few declining section data, or even just the early rising section data. Furthermore, a generalized production decline analysis model was proposed, which can match all flow regimes in coalbed methane reservoirs as well as in shale gas and tight gas reservoirs. Particularly, the Arps exponential decline model, Duong model, and SEPD model are some of its special forms. By analyzing the production of typical wells in the Zhengzhuang-Fanzhuang block of China, it has demonstrated that the generalized production forecasting model performs significantly better than the Duong model in terms of data fitting accuracy and prediction accuracy, and in comparison to the Arps model, PLE model, SEPD model and Duong model, the generalized production decline model has also demonstrated greater scientific validity and field applicability for predicting coalbed methane production. The MAPE value between the predicted and actual gas production ranges from 4.46% to 8.82%, with an average of 5.75%, and the RRMSE between them ranges from 6.24% to 9.74%, with an average of 7.53%, which are both significantly lower than those provided by the Arps model, PLE model, SEPD model, and Duong model.

Keywords: Coalbed methane; Production forecasting; Recoverable reserves; Generalized gamma distribution; Generalized production decline model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2023.121729

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