Production Prediction Based on Principal Component Analysis and Neural Network—A Case Study in a Coalbed Methane Field

Author:

Yang Yong,Bie Ai-fang,Bie Han- yu,Li Yong-gen,Duan Li-jiang,Zhu Xiao- lin

Publisher

Springer Nature Singapore

Reference16 articles.

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2. Zhang, X., Zhang, S., Sun, Q., et al.: Evaluating the influence of geological structure to CBM productivity based on AHP and fuzzy mathematics. J. China Coal Soc. 42(9), 2385–2392 (2017)

3. Fang, X., Ding, Z., Shu, X.: Hydrogen yield prediction model of hydrogen production from low rank coal based on support vector machine optimized by genetic algorithm. J. China Coal Soc. 35(sup.), 205–209 (2010)

4. Zheng, Q., Zhang, Y., Zhu, Y.: Prediction model of petroleum yield based on nearest neighbor-radial basis function networks. Technol. Develop. Chem. Indus. 32(2), 38–40 (2003)

5. Yand, Z.W., Jiang, Z.X., Liang, Z.K., et al.: Evaluation of shale TOC content based on two machine learning methods: a case study of Wufeng-Longmaxi formation in Southern Sichuan Basin. Lithol. Reserv. 34(1), 130–138 (2022)

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