Sensitivity Analysis and Optimization of Surfactant-Polymer Flooding under Uncertainties

Frédéric Douarche, Sébastirn da Veiga, Mathieu Feraille, Guillaume Enchéry (), Samir Touzani and R. Barsalou
Additional contact information
Frédéric Douarche: IFPEN - IFP Energies nouvelles
Sébastirn da Veiga: IFPEN - IFP Energies nouvelles
Mathieu Feraille: IFPEN - IFP Energies nouvelles
Guillaume Enchéry: IFPEN - IFP Energies nouvelles
Samir Touzani: IFPEN - IFP Energies nouvelles
R. Barsalou: Beicip Franlab

Post-Print from HAL

Abstract: Chemical flooding is currently one of the most promising solution to increase the recovery of mature reservoirs. In Surfactant-Polymer (SP) processes, several parameters should be taken into account to estimate the return on investments: concentrations of the injected chemical species, slug sizes, initiation times, residual oil saturation, adsorption rates of the chemical species on the rock, etc. Some parameters are design parameters whereas other ones are uncertain. For operators, defining the optimal values of the first ones while considering the uncertainties related to the second ones, is not an easy task in practice. This work proposes a methodology to help handle this problem. Starting from a synthetic reservoir test case where an SP process is set up, we select design and uncertain parameters which may impact the production. In the reservoir simulator, for the sake of flexibility, some of them are tabulated functions, which enables the user to input any data coming from any system. However, point-wise modifications of these curves would soar the number of parameters. Therefore, a particular parameterization is introduced. We then propose a methodology based on Response-SurfaceModeling (RSM) to first approximate the oil production computed by a reservoir simulator for different values of our parameters and identify the most influential ones. This RSM is based on a Karhunen-Loe've decomposition of the time response of the reservoir simulator and on an approximation of the components of this decomposition by a Gaussian process. This technique allows us to obtain substantial savings of computation times when building the response surfaces. Once a good predictability is achieved, the surfaces are used to optimize the design of the SP process, taking economic parameters and uncertainties on the data into account without additional reservoir simulations.

Keywords: EOR; enhanced oil recovery; surfactant polymère; récupération assistée; injection polymères; injection surfactants; incertitude économique (search for similar items in EconPapers)
Date: 2014-08-25
Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-01068221
References: View complete reference list from CitEc
Citations:

Published in Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2014, 69 (4), pp. 603-617. ⟨10.2516/ogst/2013166⟩

Downloads: (external link)
https://ifp.hal.science/hal-01068221/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01068221

DOI: 10.2516/ogst/2013166

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().