RESEARCH ARTICLE


Production Forecasting of Coalbed Methane Wells Based on Type-2 Fuzzy Logic System



Lei Xu1, Kai Zhu2, Xiaoli Yang*, 3
1 Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, Sichuan, China
2 Research Institute of Yanchang Petroleum (Group) CO. Ltd, Xi'an 710052, China
3 College of Life and Science, Neijiang Normal University, Neijiang 641100, Sichuan, China


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© Xu et al.; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the College of Life and Science, Neijiang Normal University, Neijiang 641100, Sichuan, China; Tel: +86-18080221926; E-mail: 1986_xulei@163.com


Abstract

Coal bed methane (CBM) reservoir production is controlled by a large set of parameters: geology, tectonics, reservoir, completion and operation. Its simulation process is complicated, relative information is difficult to be obtained, so it is necessary to analyze accurately coal bed gas potential production capacity by adopting other mathematics methods in case of incomplete information. Regarding this problem, a new type-2 fuzzy logic system (T2FLS) method to predict CBM production capacity is proposed in this paper. Methods analyze and assess input parameters of T2FLS by integrating qualitative analysis method and quantitative assessment method (Fuzzy cluster analysis and grey correlation degree analysis). Output parameters include cumulative average gas production, peak gas rate and time to achieve a peak rate. T2FLS production forecast method is applied to CBM wells of Hancheng mine and verification results show that such prediction results are highly consistent with the variation of the CBM well production. The proposed method required less data. The comparison of this method with the existed method (ANN, T1FLS) shows that the proposed method has notable advantage in generalization, stability and consistency.

Keywords: Coal bed methane, Fuzzy cluster analysis, Grey correlation degree analysis, Production prediction, Type-2 fuzzy logic system.