New proxy models for predicting oil recovery factor in waterflooded heterogeneous reservoirs

Abstract

To predict the recovery factor (RF) in waterflooded layered oil reservoirs, two empirical relationships were derived. Both correlations use four independent variables. These are reservoir heterogeneity (characterized by permeability variation coefficient), permeability anisotropy (ratio of vertical to horizontal permeability), viscosity of the injected water, and water injection rate. One of the correlations estimates RF at water breakthrough time (RFBT) and the other evaluates RF at the end of project (RFEOP). Each correlation comes in an expanded form with more parameters and a reduced form with fewer parameters. Both models are based on the global linear model. Eclipse black-oil simulation was used to determine RF for generic reservoirs with different combinations of permeability variation, permeability anisotropy, injected water viscosities, and water injection rates. A total of 192 data sets have been generated. Out of these, 144 data sets (about 75% of the generated sets) were used for model development and 48 data sets (about 25% of the generated sets) were used for model testing and validation. The expanded forms of the new developed correlations gave reliable estimates of RFBT and RFEOP with absolute average percent difference (AAPCD) of 6.9 and 1.02, respectively. The reduced forms yielded slightly higher AAPCDs of 8.30 and 1.04, respectively. When tested against 48 simulation-generated data sets, the expanded forms yielded excellent fits for RFBT and RFEOP with AAPCDs of 14 and 6.5, respectively. The reduced forms showed comparable fit with AAPCDs of 16.9 and 6.70, respectively. The highest RFEOP of 50.6% was achieved for a generic reservoir with a permeability variation in V\u2009=\u20090.1 and a permeability anisotropy of kz/kx\u2009=\u20091.0. This particular reservoir needs to be waterflooded using a water viscosity of µw\u2009=\u20091.0 cp and a water injection rate of qi = 10,000 bpd. Finally, when tested against the Guthrie–Greenberger and the API statistical study, using a single field data set, the proposed correlations gave higher absolute percent difference of 22.9 and 22.7 compared to 0.758 and 19.2 for Guthrie–Greenberger and the API statistical study, respectively.