Fracture connectivity in low-permeability formations is a key factor governing fluid and chemical transport in these systems. This study presents an applied workflow for discrete fracture network (DFN) modeling, integrating multi-scale field data to develop stochastic DFN models, generate unstructured grids, perform reservoir simulations, and analyze post-simulation results. The workflow is implemented through a custom MATLAB-based software suite and applied to a real-world field case from the Hydraulic Fracturing Test Site in the Midland Basin, Texas. A novel stochastic DFN generator, based on a first-order model, is developed to populate fracture networks with probability distributions of key fracture attributes, including length, density, aperture, and orientation across the reservoir domain. The technique of fracture intersection refinement and an optimization-based perpendicular bisector grid algorithm are incorporated to accurately represent complex fracture geometries, such as low-angle intersections and non-uniform aperture distributions, while maintaining numerical stability. Field data, including outcrop maps, borehole imaging, core analyses, and microseismic surveys, are used to characterize the fracture systems. Production histories are employed to calibrate fracture and reservoir properties via sensitivity analysis and history matching. The calibrated model is further utilized to evaluate enhanced oil recovery (EOR) performance, demonstrating that surfactant treatment can increase oil production by up to 7.2%. The proposed workflow provides a practical toolkit to flexibly model complex fracture networks, accurately predict production performance, and effectively evaluate EOR strategies in unconventional reservoirs.
Zhang et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: