Review of the productivity evaluation methods for shale gas wells

Autor: Yize Huang, Xizhe Li, Xiaohua Liu, Yujia Zhai, Feifei Fang, Wei Guo, Chao Qian, Lingling Han, Yue Cui, Yuze Jia
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 1, Pp 25-39 (2023)
Druh dokumentu: article
ISSN: 2190-0558
2190-0566
DOI: 10.1007/s13202-023-01698-z
Popis: Abstract The influence of geological and engineering factors results in the complex production characteristics of shale gas wells. The productivity evaluation method is effective to analyze the production decline law and estimate the ultimate recovery in the shale gas reservoir. This paper reviews the production decline method, analytical method, numerical simulation method, and machine learning method. which analyzes the applicable conditions, basic principles, characteristics, and limitations of different methods. The research found that the production decline method can mainly account for the gas well production and pressure data by fitting type curve analysis. The analytical method is able to couple multiple transport mechanisms and quantify the impact of different mechanisms on shale gas well productivity. Numerical simulation builds multiple pore media in shale gas reservoirs and performs production dynamics as well as capacity prediction visually. Machine learning methods are a nascent approach that can efficiently use available production data from shale gas wells to predict productivity. Finally, the research discusses the future directions and challenges of shale gas well productivity evaluation methods.
Databáze: Directory of Open Access Journals
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