Fully implicit two-phase reservoir simulation with the additive schwarz preconditioned inexact newton method
dc.contributor.author | Liu, Lulu | |
dc.contributor.author | Keyes, David E. | |
dc.contributor.author | Sun, Shuyu | |
dc.date.accessioned | 2015-08-04T07:10:56Z | |
dc.date.available | 2015-08-04T07:10:56Z | |
dc.date.issued | 2013-09-16 | |
dc.identifier.citation | Liu, L., Keyes, D. E., & Sun, S. (2013). Fully Implicit Two-phase Reservoir Simulation With the Additive Schwarz Preconditioned Inexact Newton Method. SPE Reservoir Characterization and Simulation Conference and Exhibition. doi:10.2118/166062-ms | |
dc.identifier.isbn | 9781629931449 | |
dc.identifier.doi | 10.2118/166062-ms | |
dc.identifier.uri | http://hdl.handle.net/10754/564650 | |
dc.description.abstract | The fully implicit approach is attractive in reservoir simulation for reasons of numerical stability and the avoidance of splitting errors when solving multiphase flow problems, but a large nonlinear system must be solved at each time step, so efficient and robust numerical methods are required to treat the nonlinearity. The Additive Schwarz Preconditioned Inexact Newton (ASPIN) framework, as an option for the outermost solver, successfully handles strong nonlinearities in computational fluid dynamics, but is barely explored for the highly nonlinear models of complex multiphase flow with capillarity, heterogeneity, and complex geometry. In this paper, the fully implicit ASPIN method is demonstrated for a finite volume discretization based on incompressible two-phase reservoir simulators in the presence of capillary forces and gravity. Numerical experiments show that the number of global nonlinear iterations is not only scalable with respect to the number of processors, but also significantly reduced compared with the standard inexact Newton method with a backtracking technique. Moreover, the ASPIN method, in contrast with the IMPES method, saves overall execution time because of the savings in timestep size. | |
dc.publisher | Society of Petroleum Engineers (SPE) | |
dc.title | Fully implicit two-phase reservoir simulation with the additive schwarz preconditioned inexact newton method | |
dc.type | Conference Paper | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computational Transport Phenomena Lab | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Earth Science and Engineering Program | |
dc.contributor.department | Environmental Science and Engineering Program | |
dc.contributor.department | Extreme Computing Research Center | |
dc.contributor.department | Physical Science and Engineering (PSE) Division | |
dc.identifier.journal | SPE Reservoir Characterization and Simulation Conference and Exhibition | |
dc.conference.date | 16 September 2013 through 18 September 2013 | |
dc.conference.name | SPE Reservoir Characterisation and Simulation Conference and Exhibition: New Approaches in Characterisation and Modelling of Complex Reservoirs, RCSC 2013 | |
dc.conference.location | Abu Dhabi | |
kaust.person | Keyes, David E. | |
kaust.person | Sun, Shuyu | |
kaust.person | Liu, Lulu | |
dc.date.published-online | 2013-09-16 | |
dc.date.published-print | 2013 |
This item appears in the following Collection(s)
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Conference Papers
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Environmental Science and Engineering Program
For more information visit: https://bese.kaust.edu.sa/study/Pages/EnSE.aspx -
Applied Mathematics and Computational Science Program
For more information visit: https://cemse.kaust.edu.sa/amcs -
Physical Science and Engineering (PSE) Division
For more information visit: http://pse.kaust.edu.sa/ -
Extreme Computing Research Center
-
Earth Science and Engineering Program
For more information visit: https://pse.kaust.edu.sa/study/academic-programs/earth-science-and-engineering/Pages/home.aspx -
Computational Transport Phenomena Lab
For more information visit: https://ctpl.kaust.edu.sa/Pages/Home.aspx -
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/