An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity

Gang Hui, Zhangxin Chen, Youjing Wang, Dongmei Zhang and Fei Gu

Energy, 2023, vol. 266, issue C

Abstract: The controlling factors of unconventional shale productivity by comprehensive analysis of mineralogy, petrophysics, geochemistry, and geomechanics have not been well understood. The comprehensive datasets from 1182 core samples of key wells from the Duvernay shale at Crooked Lake, Alberta, are gathered to evaluate the fundamental parameters controlling unconventional shale gas production. By integrating reservoir parameters and shale productivity, a machine learning-based approach is used to identify the fundamental elements that affect shale productivity. Four machine learning approaches are evaluated, where Extra Trees has led to the highest coefficient of determination R2 of 0.817. Factors that mostly contribute to shale productivity are found to be the production index, formation pressure, effective porosity, total organic carbon, gas saturation, and shale thickness. Case studies demonstrate that the average accordance rate between the predicted and actual production of three new wells reaches 92.3%, thereby shedding light on the site selection of hydraulic fracturing wells for the efficient development of unconventional resources.

Keywords: Unconventional resources; Shale productivity; Machinefig learning; Controlling factors; Extra tree (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033989

DOI: 10.1016/j.energy.2022.126512

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