Abstract

Summary 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 2012) 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.

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