Dataset Information

Estimation of Local Equilibrium Model Parameters for Simulation of the Laboratory Foam-Enhanced Oil Recovery Process Using a Commercial Reservoir Simulator.

ABSTRACT: An accurate determination of the foam simulation parameters is crucial in modeling foam flow in porous media. In this paper, we present an integrated workflow to obtain the parameters in the local equilibrium foam model by history matching a series of laboratory experiments performed at reservoir conditions (131 F and 1500 psi) on Estaillades limestone using a commercial reservoir simulator. The gas-water and water-oil relative permeability curves were first validated after history matching with the unsteady-state flooding experiments. The modeling parameters for foam generation and foam dry-out effect were obtained by history matching with the gas/surfactant coinjection experiments at varying foam quality and injection rates. Moreover, the modeling parameters for the destabilizing effect of oil on foam and foam shear thinning effect were derived after history matching with the foam-enhanced oil recovery process and oil fractional flow experiments in the laboratory. In practice, the calculated results reproduce the experimental outputs reasonably well. Furthermore, sensitivity analysis of foam modeling parameters is investigated to determine the most dominating parameters for accurate simulation of foam-enhanced oil recovery process in porous media. In this work, an efficient parameter estimation approach is developed from reliable foam flooding experimental data, which may be further applied to field-scale simulation. Moreover, the simulation approach can also be utilized to facilitate our interpretation of complex lab foam flooding results.

SUBMITTER: Ding L

PROVIDER: S-EPMC7496038 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Estimation of Local Equilibrium Model Parameters for Simulation of the Laboratory Foam-Enhanced Oil Recovery Process Using a Commercial Reservoir Simulator.

Ding Lei L Cui Leyu L Jouenne Stephane S Gharbi Oussama O Pal Mayur M Bertin Henri H Rahman Mohammad Azizur MA Romero Carolina C Guérillot Dominique D

ACS omega 20200831 36

An accurate determination of the foam simulation parameters is crucial in modeling foam flow in porous media. In this paper, we present an integrated workflow to obtain the parameters in the local equilibrium foam model by history matching a series of laboratory experiments performed at reservoir conditions (131 F and 1500 psi) on Estaillades limestone using a commercial reservoir simulator. The gas-water and water-oil relative permeability curves were first validated after history matching with ...[more]

PMID: 32954197

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Project description:Since the oil formation volume factor (Bo) is crucial for various calculations in petroleum engineering, such as estimating original oil in place, fluid flow in the porous reservoir medium, and production from wells, this parameter is predicted using conventional methods including experimental tests, correlations, Equations of State, and artificial intelligence models. As a substitute to conventional black oil methods, the compositional oil method has been recently used for accurately predicting the oil formation volume factor. Although oil composition is essential for estimating this parameter, it is time-consuming and cost-intensive to obtain through laboratory analysis. Therefore, the input parameter of dissolved gas in oil has been used as a representative of the amount of light components in oil, which is an effective factor in determining oil volume changes, along with other parameters, including pressure, API gravity, and reservoir temperature. This study created machine learning models utilizing Gradient Boosting Decision Tree (GBDT) techniques, which also incorporated Extreme Gradient Boosting (XGBoost), GradientBoosting, and CatBoost. A comparison of the results with recent correlations and machine learning methods adopting a compositional approach by implementing tree-based bagging methods: Extra Trees (ETs), Random Forest (RF), and Decision Trees (DTs), is then performed. Statistical and graphical indicators demonstrate that the XGBoost model outperforms the other models in estimating the Bo parameter across the reservoir pressure region (above and below bubble point pressure); the new method has significantly improved the accuracy of the compositional method, as the average absolute relative deviation is now only 0.2598%, which is four times lower than the previous (compositional approach) error rate. The findings of this study can be used for precise prediction of the volumetric properties of hydrocarbon reservoir fluids without the need for conducting routine laboratory analyses by only employing wellhead data.

Dataset Information

Estimation of Local Equilibrium Model Parameters for Simulation of the Laboratory Foam-Enhanced Oil Recovery Process Using a Commercial Reservoir Simulator.

Publications

Estimation of Local Equilibrium Model Parameters for Simulation of the Laboratory Foam-Enhanced Oil Recovery Process Using a Commercial Reservoir Simulator.

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