Rapid productivity prediction method for frac hits affected wells based on gas reservoir numerical simulation and probability method
Abstract: As an important unconventional resource, shale gas can alleviate energy shortage, and its efficient development ensures the long-term growth of oil and gas. The prediction of production levels and estimated ultimate recovery with high accuracy is necessary for shale gas development. Conventional methods are widely applied in the oil and gas industry owing to their simplicity and effectiveness; however, none of them can accurately predict the results for frac hits affected wells. In this work, a probability method based on the numerical model of shale gas reservoir has been formed. In view of the impact of frac hits on the productivity of production wells during the development of shale gas reservoirs, an embedded discrete fractured numerical simulation method for gas reservoirs is proposed to simulate the geological engineering parameter range of wells before frac. And aiming at the established numerical model of shale gas reservoir, this method adopts the ensemble smoother with multiple data assimilation automatic history matching technology to carry out the history matching process of the model. Based on the probability theory and numerical simulation results, this study analyses the influence of different distribution functions of parameters on the calculation results of reserves, and obtains the expected curve of reserves through combination calculation. Besides, the effectiveness of this method was verified by comparing with other traditional predicted method.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Rapid productivity prediction method for frac hits affected wells based on gas reservoir numerical simulation and probability method ; volume:21 ; number:1 ; year:2023 ; extent:12
Open physics ; 21, Heft 1 (2023) (gesamt 12)
- Creator
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Nie, Jie
Wang, Hao
Hao, Yuexiang
- DOI
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10.1515/phys-2022-0233
- URN
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urn:nbn:de:101:1-2023030414281658864067
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:03 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Nie, Jie
- Wang, Hao
- Hao, Yuexiang
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