Fig. 9. Oil saturation profiles for different stages of flooding at the end of 0, 30, 60, 208, 294, 444 min in case of:(a) 14-6-14 GS; (b) 14-6-14 GS + PHPA; and(c) 14-6-14 GS + PHPA + SiO2.
Relative permeability curve analysis
In the presence of multiple fluid systems such as oil and water, relative permeability describes the alteration in flow behavior with saturation change. This is commonly observed during secondary and tertiary flooding studies involving chemical induced displacement of oil/water in porous media applications. Wettability alteration, pore morphology, fluid distribution and saturation data are primary parameters that influence relative permeability measurements [66,67]. Figs. 10(a), 10(b) and 10(c) show the water/oil relative permeability plots obtained during different flooding simulations. Solid lines represent experimental data curves, wherein adjusted curves obtained after CMOST assisted history matching are represented with help of dotted lines. Initially, the core reservoir is in intermediate-wet state, in which rock pore surfaces are wetted with oil and water exists within the central regions between the pores. It is evident that relative permeabilities for oil and water phases vary significantly with increasing water saturation, which is brought about during brine/chemical injection [67]. In summary, simulation studies prove that the wetting nature of rock is altered to strongly water-wet state.
Fig. 10. Relative permeability curves of different case scenarios involving: (a) 14-6-14 GS; (b) 14-6-14 GS + PHPA; and (c) 14-6-14 GS + PHPA + SiO2flooding.
Cumulative oil production during secondary and tertiary flooding
The oil recovery performance of surfactant, surfactant-polymer and surfactant-polymer-nanoparticle based aqueous chemical fluids were corroborated by history matching of experimental data. In previous section, the laboratory results of flooding studies were discussed for different chemical formulations (see Fig. 6). However, it is important to study the validity of these results with compositional fluid flow simulations such as CMG [17,18]. Fig. 11 shows a good match between experimental and simulated outcomes of different flooding scenarios. Experimental analyses revealed that water-flooding processes extracted respective volumes of 5.73 cm3, 5.74 cm3 and 5.93 cm3 of initial oil content in case scenarios I, II and II respectively. Simulation studies showed water-flood recoveries of 44.86%, 44.31% and 44.99% respectively at the end of secondary recovery, which is in close agreement with experimental results. Simulation studies on tertiary flooding studies showed crude oil recoveries of 7.65 cm3, 8.01 cm3 and 8.41 cm3 in the presence of {14-6-14 GS + chase water}, {14-6-14 GS + PHPA + chase water} and {14-6-14 GS + PHPA + SiO2 chase water} respectively.
Fig. 11. Cumulative oil production versus time plots for:(a) 14-6-14 GS; (b) 14-6-14 GS + PHPA; (c)14-6-14 GS + PHPA + SiO2 showing match between experimental and CMG-STARS results.
The error between experimental and simulated results for cumulative flooding studies was obtained with the help of CMG-DECE (Designed Exploration Controlled. Evolution) engine with 2000 experiments. Figs. 12(a), 12(b) and 12(c) presents the global history match (HM) error versus experiment ID plots for cases I, II and III respectively. It is evident that the simulation models were tailored during history match to achieve optimized result(s) in the search direction of minimal error. Error percentages with values ≤ 6.00% between the history matched and experimental models was achieved during the simulation run. Cases I, II and III registered the most optimal results for experiment ID nos. 1791 (within ± 5.85% error), 1017 (± 4.38 % error) and 1753 (± 5.23% error) respectively. This optimized model was validated to match imput fluid and rock conditions, and used as the best-fitted model to explain flooding performances of different fluid systems. Table 5 shows the rock-fluid parameters and flooding results for different case scenarios. The parametric results were obtained from careful analysis of histogram plots over significant number of simulation runs. The recovery rates, oil/water permeability curves, rock-wetting properties and fluid flow parameters were tuned during the history matching process. The study ultimately presents the success of surfactant flood, surfactant-polymer flood and surfactant-polymer-nanoparticle flood models in simulating the experimental outcomes, and confirms their relative efficiencies [17,18,26,27,35].
Fig. 12. History matching error between experimental and simulated models, showing the base case, general solutions and optimal solution for cumulative flooding characterized by : (a) 14-6-14 GS; (b) 14-6-14 GS + PHPA; (c) 14-6-14 GS + PHPA + SiO2