Bayesian Experimental Design for the Influence Identification of Uncertain Geological Parameters on the CO2-GAGD Process

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.