CO2 injection is a proven EOR (enhanced oil recovery) method, which has been extensively applied in the field. CO2 promotes oil recovery through a number of mechanisms including; CO2 dissolution, viscosity reduction, oil swelling, and extraction of light hydrocarbon components of crude oil. One of the main advantages considered for CO2 injection is that it can develop miscibility with most of light crude oils at a pressure lower than what would be required for other gases. Miscibility development is a function of reservoir pressure, temperature and also oil composition. In water flooded oil reservoirs, water can adversely affect the performance of CO2 injection as it reduces the contact between oil and CO2. However, CO2 will be able to dissolve into water and diffuse from water into the oil. The dynamic interplay between these various mechanisms is complicated and cannot be captured by existing models and simulations.
In this paper we present the highlights of the results of a series of visualization (micromodel) experiments performed using three different crude oils. CO2 injection was carried out to investigate the pore-scale interactions between CO2, crude oil and water inside the porous medium under liquid, vapour and super-critical conditions. In particular, we reveal a new mechanism that can lead to the recovery of the disconnected oil ganglia that do not come to direct contact with injected CO2. Our results reveal that, under certain conditions, a new phase can be formed in trapped oil ganglia and grows in size and can eventually connect the ganglia to the flowing CO2 stream and lead to their production. The increase in the size of the new phase continues without limit as long as CO2 injection continues and is much more than what can be achieved by the swelling of the oil due to CO2 dissolution. In the injection strategies where CO2 injection is associated or followed by water injection, e.g. CO2-WAG or CO2-SWAG, formation of the new phase can also divert the flow of water towards the unswept regions of the porous media and lead to additional oil recovery.
Modeling complex transport processes in naturally fractured reservoirs (NFRs) using classical continuum models may not be practically possible because the algorithms used in this type of modeling approach for the detailed structure of fracture/matrix systems require unreasonable computational time. Also, fractured reservoirs are highly irregular, and finite-difference calculations for such models often cause convergence problems. In addition, an exact representation of a complex fracture network in classical continuum modeling algorithms is highly difficult. An alternative is to use a nonclassical technique known as the random-walk particle-tracking (RWPT) algorithm.
We showed earlier (Stalgorova and Babadagli 2009) that the random-walk (RW) technique can be adapted to model miscible flooding in a fractured porous medium at the laboratory scale. The unknown parameters used to match the model results were only the diffusion coefficients for oil and solvent, as the diffusive/dispersive transport (effective in fracture and matrix) was coupled with viscous (effective in fracture) and gravity (effective in fracture and matrix) displacement. Advantages of this method over classical simulation include a shorter computational time, which allows avoidance of simplifications; the ability to model the matrix/fracture diffusion process without any transfer function; and the representation of a complex and irregular fracture network system.
In this paper, we modified this laboratory-scale RW model for field-scale applications. A series of tracer-test results from the Midale field in Canada was used to test the model. A fracture-network model was constructed on the basis of geological data, and then we used the RWPT model to calibrate the fracture network against tracer-test results. The results were compared to those obtained using continuum (dual-porosity) models, and it was observed that the connectivity and breakthrough times can be captured more correctly with the RWPT model.
We performed a sensitivity analysis to identify the importance of different parameters for the simulation results. The new model and observations can be used to validate and calibrate stochastically generated fracture-network models and to estimate the enhanced-oil-recovery (EOR) performance of NFRs.