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• W2991240765 abstract "The uncertainty and complexity of the geological parameters of coalbed methane (CBM) reservoirs may result in the assignment of atypical exact values to a whole block. Therefore, the influence of different resources and productivity characterization indices on the development of CBM blocks was analyzed systematically in this research. Furthermore, this research reported an interval-number index evaluation system and a gray relational analysis optimization model for CBM development. The weights of the evaluation indices were determined by combining the entropy method and the best worst method (BWM) with the objective deviation law of the data and the subjective experience of experts. Based on the gray system theory, a multi-index gray relational analysis optimization model was established to evaluate the interval values and optimize the development potential of CBM blocks with complex geological conditions. The results show that the order of importance of the eight resources and reservoir evaluation indices, from highest to lowest, is permeability, reservoir pressure gradient, gas content, reservoir thickness, reservoir depth, critical reservoir ratio, Young’s modulus, and reservoir temperature. The order of the target blocks, from most to least optimal, is the Sanjiao block, Fanzhuang-Zhengzhuang block, Daning-Jixian block, Gujiao block, and Fengrun block. The evaluation results are consistent with actual conditions. Thus, the multi-index gray relation analysis optimization model, on account of the gray system theory, is feasible for the development evaluation of CBM blocks under complex conditions. This model is suitable for the CBM blocks in the range of 547 km2~675 km2. When using this model, the index values must be representative and accurate." @default.
• W2991240765 created "2019-12-05" @default.
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• W2991240765 date "2019-11-21" @default.
• W2991240765 modified "2023-10-16" @default.
• W2991240765 title "Gray Relational Analysis Optimization for Coalbed Methane Blocks in Complex Conditions Based on a Best Worst and Entropy Method" @default.
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• W2991240765 doi "https://doi.org/10.3390/app9235033" @default.
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