Data acquisition strategy and history matching for chemical EOR corefloods

Réf. SB/VG/sl - R1640R - 2017/06

Stage - Géologie / Géochimie

Localisation : Hauts-de-Seine

Début : entre février et mars 2018
Durée : 5 mois
Indem. : à définir

IFP Energies nouvelles

IFP Energies nouvelles est un organisme public de recherche, d’innovation industrielle et de formation intervenant dans les domaines de l’énergie, du transport et de l’environnement. Sa mission est d'apporter aux acteurs publics et à l'industrie des technologies performantes, économiques, propres et durables pour relever les trois grands défis sociétaux du 21e siècle : changement climatique et impacts environnementaux, diversification énergétique et gestion des ressources en eau. Son expertise est internationalement reconnue.

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Data acquisition strategy and history matching for chemical EOR corefloods

Internship program:

Recovery experiments on corefloods make it possible to better characterize the efficiency of chemical EOR processes. Different experimental configurations can be considered, with varying slug size or chemical concentrations for instance. In addition, numerical models can be considered to reproduce these experiments, and help to simulate the processes at larger scale, such as pilot scale. However, these models depend on a set of input parameters that are generally poorly known.

These parameters characterize the chemical EOR processes and need to be calibrated with the coreflood experiments. In practice, laboratory experiments can be costly and time-consuming. So a limited number of them only can be performed. In addition, they may not provide enough information to fully characterize the parameters. As a result, the experiments configuration should be chosen carefully in order to reduce as much as possible the uncertainty on the model parameters.

The objective of this internship is to develop data acquisition strategies that are optimal for the calibration of the numerical model input parameters. More precisely, the aim is to identify, among all the possible experiments and measurements, the ones that will reduce the most the uncertainty on the model parameters. To solve this problem, we refer to information theory. Dependence measures between the input parameters and the model simulated outputs will be investigated. In particular, this work will focus on measures that can be estimated from a small ensemble of fluid-flow simulations.

The objective will be to identify criteria based on these dependence measures that can drive the identification of an optimal design of experiments. The data acquired following these recommendations will then be considered to calibrate the model parameters and check the relevance of the proposed approach. To that purpose, a synthetic case study will be considered, meaning that experimental results will be generated with the numerical model only.

During this internship, it would be also interesting to apply the workflow sequentially, using the previous measurements to propose additional experiments and data acquisition in order to further improve the calibration of the parameters. The uncertainty related to the real configuration of the experiments should also be taken into account to obtain robust designs of experiments.

Skills required

EOR modelling, uncertainty, history matching, programming skills

Département : Géothermohydromécanique (R164)

Encadrement : Sarah Bouquet, Véronique Gervais

Contact

IFP Energies nouvelles
Sylvie LOUIS
1&4, avenue de Bois-Preau
92852 Rueil-Malmaison cedex
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