Fracture and Production Data Integration Using Discrete Fracture Network Models for Carbonate Reservoir Management, South Oregon Basin Field, Wyoming
- R. Parney (Golder Associates Inc.) | T. Cladouhos (Golder Associates Inc.) | P. La Pointe (Golder Associates Inc.) | W. Dershowitz (Golder Associates Inc.) | B. Curran (Marathon Oil Company)
- Document ID
- Society of Petroleum Engineers
- SPE Rocky Mountain Regional/Low-Permeability Reservoirs Symposium and Exhibition, 12-15 March, Denver, Colorado
- Publication Date
- Document Type
- Conference Paper
- 2000. Society of Petroleum Engineers
- 1.10 Drilling Equipment, 7.6.2 Data Integration, 5.8.7 Carbonate Reservoir, 5.6.5 Tracers, 5.4.1 Waterflooding, 3 Production and Well Operations, 4.3.4 Scale, 4.1.2 Separation and Treating, 5.1.1 Exploration, Development, Structural Geology, 5.1.2 Faults and Fracture Characterisation, 5.2.1 Phase Behavior and PVT Measurements, 6.5.2 Water use, produced water discharge and disposal, 5.6.3 Deterministic Methods, 5.5.8 History Matching, 1.6 Drilling Operations
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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. ThisDFN model is being developed to support well placement and gel treatment inorder 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. Themodel is calibrated against tracer test results.
The South Oregon Basin field in the Big Horn Basin of Wyoming, currentlyoperated 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 matrixpermeability, but the significant structural deformation to which the reservoirunits have been subjected has produced good large-scale fracture connection.However, local smaller-scale fracture connections appear to be variable andoften results in poor fracture permeability.
The field has been under waterflood since the 1960's. Because of thesignificant heterogeneity in reservoir permeability, current production suffersfrom very high >95%) water cuts, while the oil saturation remains high (upto 80%).
Several options are being considered for improving recovery from thisreservoir that require better knowledge of the fracture system. These optionsinclude improved targeting of water injection for waterflooding, optimalhorizontal drilling to better connect and link together lower recovery portionsof the reservoir, and the selective reduction in water cycling through improvedgel conformance treatment design and placement. As part of a DOE-funded study,the discrete fracture network (DFN) technique is being applied to optimize wellplacement and gel treatment in order to recover previously bypassed oil.
As the basis for the fracture study, four independent data sets wereanalyzed: outcrops at Wind River Canyon and Zeisman Dome, fracture andlithologic data from field wells, lineament maps from 3D seismic, and tracertests. Individually, each data set confirms that fractures dominate the fluidflow in the reservoir rocks. Collectively, these data sets were used toconstruct 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 calibratedagainst a wide variety of production tests and data, and can be used to carryout numerical simulations of flow and transport. DFN models also integrate datafrom different scales, including individual wellbore-scale data that is treatedstochastically, and reservoir-scale, deterministic fault models derived fromseismic and outcrop data. The flow parameters of the DFN model were calibratedusing breakthrough times and concentrations from tracer tests. The final DFNmodel, 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 usedto 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 geologicaland hydraulic data as described below. The structural information issynthesized to obtain parameters for the a) spatial model, b) orientationdistribution, c) size, and d) intensity of the natural fractures.
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