Production Performance Analysis and Fracture Volume Parameter Inversion of Deep Coalbed Methane Wells

Jianshu Wu, Xuesong Xin, Lei Zou, Guangai Wu, Jie Liu, Shicheng Zhang, Heng Wen and Cong Xiao ()
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Jianshu Wu: CNOOC Research Institute Ltd., Beijing 100028, China
Xuesong Xin: CNOOC Research Institute Ltd., Beijing 100028, China
Lei Zou: Department of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Guangai Wu: CNOOC Research Institute Ltd., Beijing 100028, China
Jie Liu: Department of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Shicheng Zhang: Department of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Heng Wen: CNOOC Research Institute Ltd., Beijing 100028, China
Cong Xiao: Department of Petroleum Engineering, China University of Petroleum, Beijing 102249, China

Energies, 2025, vol. 18, issue 18, 1-23

Abstract: Deep coalbed methane development faces technical challenges, such as high in situ stress and low permeability. The dynamic evolution of fractures after hydraulic fracturing and the flowback mechanism are crucial for optimizing productivity. This paper focuses on the inversion of post-fracturing fracture volume parameters and dynamic analysis of the flowback in deep coalbed methane wells, with 89 vertical wells in the eastern margin of the Ordos Basin as the research objects, conducting systematic studies. Firstly, through the analysis of the double-logarithmic curve of normalized pressure and material balance time, the quantitative inversion of the volume of propped fractures and unpropped secondary fractures was realized. Using Pearson correlation coefficients to screen characteristic parameters, four machine learning models (Ridge Regression, Decision Tree, Random Forest, and AdaBoost) were constructed for fracture volume inversion prediction. The results show that the Random Forest model performed the best, with a test set R 2 of 0.86 and good generalization performance, so it was selected as the final prediction model. With the help of the SHAP model to analyze the influence of each characteristic parameter, it was found that the total fluid volume into the well, proppant intensity, minimum horizontal in situ stress, and elastic modulus were the main driving factors, all of which had threshold effects and exerted non-linear influences on fracture volume. The interaction of multiple parameters was explored by the Partial Dependence Plot (PDP) method, revealing the synergistic mechanism of geological and engineering parameters. For example, a high elastic modulus can enhance the promoting effect of fluid volume into the well and proppant intensity. There is a critical threshold of 2600 m 3 in the interaction between the total fluid volume into the well and the minimum horizontal in situ stress. These findings provide a theoretical basis and technical support for optimizing fracturing operation parameters and efficient development of deep coalbed methane.

Keywords: deep coalbed methane; production performance analysis; flowback analysis; fracture volume; correlation analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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