Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Seyed Kourosh Mahjour"'
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 2, Pp 686-702 (2024)
Climate change research uses an ensemble of general circulation model runs (GCMs-runs) to predict future climate under uncertainties. To reduce computational costs, this study selects representative GCM-runs (RGCM-runs) for Western North America (WNA
Externí odkaz:
https://doaj.org/article/9223a754cfd747d6a8c6aa99613134fa
Publikováno v:
Energies, Vol 17, Iss 5, p 1180 (2024)
Evaluating uncertainty in CO2 injection projections often requires numerous high-resolution geological realizations (GRs) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations
Externí odkaz:
https://doaj.org/article/2f36c4f32a7b479381c4a79c645ec0de
Autor:
Salah A. Faroughi, Ramin Soltanmohammadi, Pingki Datta, Seyed Kourosh Mahjour, Shirko Faroughi
Publikováno v:
Mathematics, Vol 12, Iss 1, p 63 (2023)
Simulating solute transport in heterogeneous porous media poses computational challenges due to the high-resolution meshing required for traditional solvers. To overcome these challenges, this study explores a mesh-free method based on deep learning
Externí odkaz:
https://doaj.org/article/71515309db774177847b220391d202dc
Autor:
Denis José Schiozer, Antonio Alberto de Souza dos Santos, Manuel Gomes Correia, Seyed Kourosh Mahjour
Publikováno v:
Journal of Petroleum Exploration and Production Technology. 11:3079-3102
The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e421884ad1122cc876e33e119496156
https://doi.org/10.1007/s13202-021-01210-5
https://doi.org/10.1007/s13202-021-01210-5
Autor:
Antonio Alberto de Souza dos Santos, Denis José Schiozer, Susana M.G. Santos, Seyed Kourosh Mahjour
Publikováno v:
Day 1 Tue, September 21, 2021.
In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8056c42f838e23407930781fba945e51
https://doi.org/10.2118/206300-ms
https://doi.org/10.2118/206300-ms
Autor:
Denis José Schiozer, Seyed Kourosh Mahjour, Antonio Alberto de Souza dos Santos, Guilherme Palermo Coelho, Luis Otávio Mendes da Silva, Luis A. A. Meira
Publikováno v:
Journal of Petroleum Science and Engineering. 209:109822
Ensembles of geological realizations (GR) are normally processed by numerical simulators to evaluate geological uncertainty during the decision-making process. Although different stochastic spatial algorithms can quickly generate hundreds to thousand
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01d54d817efa03343c5aee77f939c4e9
https://doi.org/10.1016/j.petrol.2021.109822
https://doi.org/10.1016/j.petrol.2021.109822
Autor:
Antonio Alberto de Souza dos Santos, Manuel Gomes Correia, Seyed Kourosh Mahjour, Denis José Schiozer
Publikováno v:
Journal of Energy Resources Technology. 142
Understanding the role of geological uncertainties on reservoir management decisions requires an ensemble of reservoir models that cover the uncertain space of parameters. However, in most cases, high computation time is needed for the flow simulatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc22b8bb684aa994e80c9af4c01d53d3
https://doi.org/10.1115/1.4045736
https://doi.org/10.1115/1.4045736
Publikováno v:
ECMOR XVII.
Summary Probabilistic approaches for optimization objectives need a large ensemble size to consider uncertainties, which is often computationally expensive. Our proposed method includes two scenario reduction (SR) techniques applied to geostatistical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fe244cbea9315e6ff93b2b8c0f56e40
https://doi.org/10.3997/2214-4609.202035105
https://doi.org/10.3997/2214-4609.202035105