Determining the most influential reservoir parameters on the GAGD process is an essential step to understanding the EOR process efficiency. In this paper, we introduce Bayesian Model Averaging (BMA) as a stochastic linear modelling approach to select the most influential parameters affecting the Gas Assisted Gravity Drainage (GAGD) Process performance in a multilayer heterogeneous sandstone oil reservoir. Lithofacies and petrophysical property model was reconstructed considering multiple-point geostatistics for 3D property distribution. CO2 is injected through vertical injectors at the top two layers. The 2nd three layers were left as a transition to allow a vertical depth interval for gas gravity drainage. Horizontal producers were set up through the sixth, seventh, and eighth layers where the oil saturation has the highest levels. The last four layers were left with no injection/production activity. The studies reservoir factors are horizontal permeability, anisotropy ratio (Kv/Kh), and porosity. Latin Hypercube Design created many simulation jobs and the elimination was conducted by the BMA stochastic approach, which adopts posterior probability to choose the best model among a set of candidate models. Moreover, the accurate determining of influential factors through BMA has led to better understanding of the effect of heterogeneity and anisotropy on the GAGD process. @en

The purpose of this research is to determine an actual optimal solution through cyclic optimization of CO2-Gas Assisted Gravity Drainage (GAGD) process in a heterogeneous sandstone reservoir under geological uncertainties. We propose an integrated approach to optimize durations of gas injection, soaking, and oil production under geological uncertainties. Therefore, 100 stochastic reservoir realizations of the 3D permeability and porosity distributions were created honouring geological constraints. Ranking was applied through quantifying of reservoir oil response to select P10, P50, and, P90 that represent the overall reservoir uncertainty. More than 400 training simulation runs were created including the durations and geological uncertainty parameters through Latin Hypercube Design to build the second-order proxy model along with approximately 200 extra verification runs. The verification runs led to keep the solutions in global optima and obtain satisfactory proxy model through an iterative validation procedure. The cyclic optimization has shown its feasibility to increase oil recovery through the GAGD process from 71.5% to 75.5% with incremental cumulative oil production of 225 million barrels. The presented robust optimization workflow under geological uncertainties led to higher recovery factor than nominal realization optimization with providing degrees of freedom for the decision-maker to significantly reduce the project risk. @en