Petroleum Reservoir Evaluation and Development >
2022 , Vol. 12 >Issue 3: 487 - 495
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2022.03.011
Well interference evaluation and prediction of shale gas wells based on machine learning
Received date: 2021-08-20
Online published: 2022-06-24
Inter-well interference seriously affects the production of shale gas wells. The evaluation and prediction of well interference degree is of great significance to the efficient development of shale gas. But the existing research mainly focuses on the interference phenomenon between shale gas wells, production performance, and parameter optimization through numerical simulation. There are few studies on the quantitative evaluation and prediction of the interference degree between shale gas wells, and the selected parameters is incomplete, which makes it difficult to objectively evaluate the well interference between shale gas wells. Therefore, the machine learning method is used to comprehensively consider the geological parameters and fracturing parameters to evaluate and predict the degree of interference between wells in the shale gas reservoir. Firstly, the initial data are processed to improve the data quality. Then, based on the processed data, cluster analysis and random forest algorithm are used to evaluate and predict the interference degree of shale gas wells. The results show that the proportions of the wells with low, medium and high well interference in the shale gas reservoirs are 25.93 %, 37.03 % and 37.04 %, respectively. The fracturing factors show significant influence on the well interference degree in the shale gas reservoirs. After parameters optimization, the prediction results of well interference degree reaches 92.07 %, indicating that the developed prediction model can be applied to forecast the well interference degree in shale gas reservoirs.
Qing ZHANG , Feng HE , Youwei HE . Well interference evaluation and prediction of shale gas wells based on machine learning[J]. Petroleum Reservoir Evaluation and Development, 2022 , 12(3) : 487 -495 . DOI: 10.13809/j.cnki.cn32-1825/te.2022.03.011
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