期刊
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
卷 94, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jngse.2021.104045
关键词
Shale gas production; Machine learning; Geological factors; Operational factors; Fracturing parameters optimization
资金
- Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, China [PLC2020044]
- Canada First Research Excellence Fund (CFREF)
- Alliance Grant from Natural Sciences and Engineering Research Council of Canada (NSERC) [ALLRP \5485762019]
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2020-05215]
A comprehensive machine learning approach was developed to forecast shale gas production by integrating geological and operational factors, with factors such as fluid injection, proppant mass, and well characteristics found to have significant impact on production. The Extra Trees approach demonstrated the highest coefficient of determination R2 at 0.81, showing potential for effective prediction of shale gas production.
Hundreds of horizontal wells have been performed fracturing operations to exploit the unconventional shale gas resources in the Duvernay Formation of Fox Creek, Alberta. Despite achieving the practical analysis of shale gas production via the data-mining approach, previous studies failed to incorporate comprehensive site-specific geological and operational factors. In this study, a comprehensive machine-learning approach is developed to forecast the shale gas production via the integration of geological and operational factors. Thirteen geological and operational parameters deriving from the well logging, core experiment and treatment data are included as the input variables, whereas the 12-month shale gas production is regarded as the target variable. Results show that factors that mostly contributed to the shale gas production are found to be total fluid injection, total proppant mass, well TVD, permeability, y coordinate, porosity, gas saturation, number of stages, x coordinate, formation pressure, horizontal length, distance to fault and Duvernay thickness. Four machine learning methods are evaluated, where the Extra Trees approach has led to the highest coefficient of determination R2 of 0.81. Case study for Well 2 have shown that the shale gas production can be doubled if increase the total pumped volume and proppant placed mass by approximately 73% and 38%.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据