This paper describes the development of a discrete feature network (DFN)model for the South Oregon Basin field in the Big Horn Basin of Wyoming. This DFN model is being developed to support well placement and gel treatment in order to recover previously bypassed oil in this highly heterogeneous, structurally controlled carbonate reservoir.
The DFN model developed in this paper is based on data from outcrop studies, fracture and lithologic data from wells and lineament maps from 3D seismic. The model is calibrated against tracer test results.
Introduction
The South Oregon Basin field in the Big Horn Basin of Wyoming, currently operated by Marathon Oil Co., was discovered in 1912 (Fig. 1). Thisfield has produced over 83 million bbl of oil from the Permo-TriassicPhosphoria Formation, a thin marine limestone. This unit has moderate matrix permeability, but the significant structural deformation to which the reservoir units have been subjected has produced good large-scale fracture connection. However, local smaller-scale fracture connections appear to be variable and often results in poor fracture permeability.
The field has been under waterflood since the 1960's. Because of the significant heterogeneity in reservoir permeability, current production suffers from very high >95%) water cuts, while the oil saturation remains high (upto 80%).
Several options are being considered for improving recovery from this reservoir that require better knowledge of the fracture system. These options include improved targeting of water injection for waterflooding, optimal horizontal drilling to better connect and link together lower recovery portions of the reservoir, and the selective reduction in water cycling through improved gel conformance treatment design and placement. As part of a DOE-funded study, the discrete fracture network (DFN) technique is being applied to optimize well placement and gel treatment in order to recover previously bypassed oil.
As the basis for the fracture study, four independent data sets were analyzed: outcrops at Wind River Canyon and Zeisman Dome, fracture and lithologic data from field wells, lineament maps from 3D seismic, and tracer tests. Individually, each data set confirms that fractures dominate the fluidflow in the reservoir rocks. Collectively, these data sets were used to construct a consistent, calibrated DFN reservoir model.
The DFN approach is well-suited for these goals, since it creates realistic3D models of the fracture "plumbing" in the reservoir, can be calibrated against a wide variety of production tests and data, and can be used to carryout numerical simulations of flow and transport. DFN models also integrate data from different scales, including individual wellbore-scale data that is treated stochastically, and reservoir-scale, deterministic fault models derived from seismic and outcrop data. The flow parameters of the DFN model were calibrated using breakthrough times and concentrations from tracer tests. The final DFN model, conditioned to the structural geology, lithology, production, and tracertest data from this complex field, quantifies fracture intensity, surface area, volume, permeability, and connectivity. This calibrated DFN model is being used to support optimization of well placements and gel treatment.
DFN Model Implementation
The DFN model implemented for the South Oregon Basin is illustrated inFig. 2. This model was derived by integration of structural geological and hydraulic data as described below. The structural information is synthesized to obtain parameters for thespatial model,orientation distribution,size, andintensity of the natural fractures.
