Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Eduardo Gildin"'
Publikováno v:
Mathematics, Vol 12, Iss 20, p 3281 (2024)
A physics-informed convolutional neural network (PICNN) is proposed to simulate two-phase flow in porous media with time-varying well controls. While most PICNNs in the existing literature worked on parameter-to-state mapping, our proposed network pa
Externí odkaz:
https://doaj.org/article/5b1405d0f9434bfd9a275b7212316523
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 7 (2021)
Data-driven methods have been revolutionizing the way physicists and engineers handle complex and challenging problems even when the physics is not fully understood. However, these models very often lack interpretability. Physics-aware machine learni
Externí odkaz:
https://doaj.org/article/f5c78aaf12404099b0a6633ca08dde83
Publikováno v:
Applied Sciences, Vol 11, Iss 11, p 4874 (2021)
Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distribu
Externí odkaz:
https://doaj.org/article/97521617e791489a9c4392275e1fbb83
Autor:
Hardikkumar Zalavadia, Eduardo Gildin
Publikováno v:
Energies, Vol 14, Iss 6, p 1765 (2021)
The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optim
Externí odkaz:
https://doaj.org/article/c28469d3ed36425d9de321e7a58ae6c6
Publikováno v:
Fluids, Vol 4, Iss 3, p 138 (2019)
Learning reservoir flow dynamics is of primary importance in creating robust predictive models for reservoir management including hydraulic fracturing processes. Physics-based models are to a certain extent exact, but they entail heavy computational
Externí odkaz:
https://doaj.org/article/ffc68c2fbf3f47fe859b5c7855b14b4d
Publikováno v:
Energies, Vol 11, Iss 12, p 3368 (2018)
Capacitance resistance models (CRMs) comprise a family of material balance reservoir models that have been applied to primary, secondary and tertiary recovery processes. CRMs predict well flow rates based solely on previously observed production and
Externí odkaz:
https://doaj.org/article/4d1ffb2a05cd491d9193b73789853b55
Publikováno v:
Computation, Vol 4, Iss 2, p 22 (2016)
We propose an online adaptive local-global POD-DEIM model reduction method for flows in heterogeneous porous media. The main idea of the proposed method is to use local online indicators to decide on the global update, which is performed via reduced
Externí odkaz:
https://doaj.org/article/a72815f4ca1d41e0a008f426ace7f719
Autor:
Marcelo J. Dall'Aqua, Emilio J. R. Coutinho, Eduardo Gildin, Zhenyu Guo, Hardik Zalavadia, Sathish Sankaran
Publikováno v:
Day 1 Tue, March 28, 2023.
Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation executi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3dd519a78b74b8af134562213955a017
https://doi.org/10.2118/212204-ms
https://doi.org/10.2118/212204-ms
Autor:
Roman J. Shor, Shanti Swaroop Kandala, Eduardo Gildin, Sam F. Noynaert, Enrique Z. Losoya, Vivek Kesireddy, Narendra Vishnumolakala, Inho Kim, James Ng, Josh K. Wilson, Eric Cayeux, Rajat Dixit, Gregory S. Payette, Ty Cunningham, Paul E. Pastusek
Publikováno v:
Day 2 Wed, March 09, 2022.
As a follow-up to the challenge set forth by (Pastusek et al, 2019) to create an open-source drilling community for modelling and data, this paper presents the charter, contribution methods, workflows, and interoperability standards of the open sourc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0e6e9b41dda8510bce32d86fa8dc3c9
https://doi.org/10.2118/208794-ms
https://doi.org/10.2118/208794-ms