Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques
In this work, we show how the a posteriori error estimation techniques proposed in [Di Pietro et al. (2014) Computers & Mathematics with Applications 68, 2331-2347] can be efficiently employed to improve the performance of a compositional reservoir simulator dedicated to Enhanced Oil Recovery (E...
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2016-09-01
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Online Access: | https://doi.org/10.2516/ogst/2016009 |
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doaj-b9085164a9374bc4bb7cf52d245e20352021-02-02T07:14:29ZengEDP SciencesOil & Gas Science and Technology1294-44751953-81892016-09-017155910.2516/ogst/2016009ogst150220Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation TechniquesGratien Jean-MarcRicois OlivierYousef SoleimanIn this work, we show how the a posteriori error estimation techniques proposed in [Di Pietro et al. (2014) Computers & Mathematics with Applications 68, 2331-2347] can be efficiently employed to improve the performance of a compositional reservoir simulator dedicated to Enhanced Oil Recovery (EOR) processes. This a posteriori error estimate allows to propose an adaptive mesh refinement algorithm leading to significant gain in terms of the number of cells in mesh compared to a fine mesh resolution, and to formulate criteria for stopping the iterative algebraic solver and the iterative linearization solver without any loss of precision. The emphasis of this paper is on the computational cost of the error estimators. We introduce an efficient computation using a practical simplified formula that can be easily implemented in a reservoir simulation code. Numerical results for a real-life reservoir engineering example in three dimensions show that we obtain a significant gain in CPU times without affecting the accuracy of the oil production forecast.https://doi.org/10.2516/ogst/2016009 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gratien Jean-Marc Ricois Olivier Yousef Soleiman |
spellingShingle |
Gratien Jean-Marc Ricois Olivier Yousef Soleiman Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques Oil & Gas Science and Technology |
author_facet |
Gratien Jean-Marc Ricois Olivier Yousef Soleiman |
author_sort |
Gratien Jean-Marc |
title |
Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques |
title_short |
Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques |
title_full |
Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques |
title_fullStr |
Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques |
title_full_unstemmed |
Reservoir Simulator Runtime Enhancement Based on a Posteriori Error Estimation Techniques |
title_sort |
reservoir simulator runtime enhancement based on a posteriori error estimation techniques |
publisher |
EDP Sciences |
series |
Oil & Gas Science and Technology |
issn |
1294-4475 1953-8189 |
publishDate |
2016-09-01 |
description |
In this work, we show how the a posteriori error estimation techniques proposed in [Di Pietro et al. (2014) Computers & Mathematics with Applications 68, 2331-2347] can be efficiently employed to improve the performance of a compositional reservoir simulator dedicated to Enhanced Oil Recovery (EOR) processes. This a posteriori error estimate allows to propose an adaptive mesh refinement algorithm leading to significant gain in terms of the number of cells in mesh compared to a fine mesh resolution, and to formulate criteria for stopping the iterative algebraic solver and the iterative linearization solver without any loss of precision. The emphasis of this paper is on the computational cost of the error estimators. We introduce an efficient computation using a practical simplified formula that can be easily implemented in a reservoir simulation code. Numerical results for a real-life reservoir engineering example in three dimensions show that we obtain a significant gain in CPU times without affecting the accuracy of the oil production forecast. |
url |
https://doi.org/10.2516/ogst/2016009 |
work_keys_str_mv |
AT gratienjeanmarc reservoirsimulatorruntimeenhancementbasedonaposteriorierrorestimationtechniques AT ricoisolivier reservoirsimulatorruntimeenhancementbasedonaposteriorierrorestimationtechniques AT yousefsoleiman reservoirsimulatorruntimeenhancementbasedonaposteriorierrorestimationtechniques |
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1724299798361669632 |