油页岩
岩石物理学
石油工程
非常规油
生产力
水力压裂
地质学
地质力学
采矿工程
多孔性
岩土工程
古生物学
宏观经济学
经济
作者
Gang Hui,Zhangxin Chen,Youjing Wang,Dongmei Zhang,Fan Gu
出处
期刊:Energy
[Elsevier]
日期:2023-03-01
卷期号:266: 126512-126512
被引量:24
标识
DOI:10.1016/j.energy.2022.126512
摘要
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.
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