Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jincong He"'
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 7 (2021)
Oil and gas field development optimization, which involves the determination of the optimal number of wells, their drilling sequence and locations while satisfying operational and economic constraints, represents a challenging computational problem.
Externí odkaz:
https://doaj.org/article/2e5aa06b50294cf681bf06be45371e76
Publikováno v:
SPE Journal. 28:371-380
Summary Pressure measurement from permanent downhole gauges (PDHGs) during extended shut ins (SIs) is a key piece of information that is often used for model calibration and reserve estimation in deepwater gas reservoirs. A key challenge in practical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9afb0c01d0b1e927337b1885fe19157e
https://doi.org/10.2118/212265-pa
https://doi.org/10.2118/212265-pa
Publikováno v:
Day 1 Tue, March 28, 2023.
Traditionally, subsurface models are created based on reservoir characterization, then simulated and calibrated via history matching (HM) to honor data, generate forecasts, and quantify uncertainties. However, this approach is time consuming for unco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a3e24420da7ae1bc42e8dd7f17bf262
https://doi.org/10.2118/212230-ms
https://doi.org/10.2118/212230-ms
Publikováno v:
SPE Journal. 27:133-152
SummaryDrilling sequence optimization is a common challenge faced in the oil and gas industry, and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drilling queue is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9857e946c5ce633591ec572cd7024899
https://doi.org/10.2118/203986-pa
https://doi.org/10.2118/203986-pa
Publikováno v:
Machine Learning Applications in Subsurface Energy Resource Management ISBN: 9781003207009
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cc83bd8f762bba62309110c22f2a893
https://doi.org/10.1201/9781003207009-20
https://doi.org/10.1201/9781003207009-20
Publikováno v:
SPE Journal. 27:226-245
Summary The optimization of field development plans (FDPs), which includes optimizing well counts, well locations, and the drilling sequence is crucial in reservoir management because it has a strong impact on the economics of the project. Traditiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4100b4e3dfa06d98bd373f958f373a9e
https://doi.org/10.2118/203951-pa
https://doi.org/10.2118/203951-pa
Publikováno v:
Day 4 Thu, May 05, 2022.
Pressure measurement from permanent downhole gauges (PDHGs) during extended shut-ins is a key piece of information that is often used for model calibration and reserve estimate in deep-water gas reservoirs. A key challenge in practical operation has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0a86abd9973b64dc2382412a37e928e
https://doi.org/10.4043/31758-ms
https://doi.org/10.4043/31758-ms
Publikováno v:
Day 1 Tue, October 26, 2021.
Drill sequence optimization is a common challenge faced in the oil and gas industry and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drill queue is currently des
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::497407e87ad3579bcdd30b3330514b8a
https://doi.org/10.2118/203986-ms
https://doi.org/10.2118/203986-ms
Autor:
Song Du, Tsubasa Onishi, Jincong He, Changdong Yang, Xiaoyue Guan, Jianping Chen, Zhenzhen Wang, Xian-Huan Wen
Publikováno v:
Day 1 Tue, October 26, 2021.
Summary Full-physics subsurface simulation models coupled with the surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run time of the coupled model to enab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ea93b7fc080784a803841176e2393d6
https://doi.org/10.2118/204004-ms
https://doi.org/10.2118/204004-ms
Publikováno v:
SPE Journal. 25:139-161
Summary A common pitfall in probabilistic history matching is omitting the local variation of spatial uncertainties and falsely generalizing the learning from local data to the entire field. This can lead to radical overestimation of uncertainty redu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d9119f3e6e0d22de0953835c3167630
https://doi.org/10.2118/191480-pa
https://doi.org/10.2118/191480-pa