Reservoir Characterization and the Application of Geostatistics to Three-Dimensional Modeling of a Shallow Ramp Carbonate, Mabee San Andres Field, Andrews and Martin Counties, Texas
Dennis W. Dull, 1995. "Reservoir Characterization and the Application of Geostatistics to Three-Dimensional Modeling of a Shallow Ramp Carbonate, Mabee San Andres Field, Andrews and Martin Counties, Texas", Hydrocarbon Reservoir Characterization: Geologic Framework and Flow Unit Modeling, Emily L. Stoudt, Paul M. Harris
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The Grayburg/San Andres formations of the Permian Basin of west Texas are known to contain almost 50 percent of the original oil in place, with remaining mobile oil exceeding 15 billion barrels. This large volume of residual, mobile oil is the impetus for enhanced oil recovery projects. The Mabee San Andres Field is currently under a CO2 miscible flood targeting high residual oil saturation, which, after waterflooding, is about 75 percent of the original oil in place.
The San Andres at Mabee Field is an upward-shoaling, regressive-progradational carbonate sequence that draped porous and permeable dolomites over paleostructure of late Mississippian to early Pennsylvanian age. The cyclical nature of the reservoir is illustrated by varied facies interpreted as multiple glacio-eustatic sea-level fluctuations typical of the San Andres in the Permian Basin of west Texas. The detailed description of cores from 45 wells and over 1200 thin sections revealed vertical and lateral heterogeneity that was critical to the exploitation of the reservoir. Six major facies have been identified: supratidal, oncolite, subtidal, ooid, sandstone, and open marine. The facies are representative of a sabkha-type environment of deposition similar to that found in the present-day Persian Gulf. Three distinct cycles were recognized based on the facies distribution and reservoir performance. The zonation is based on the interpretation of three recognizable cycles that constitute the upward-shoaling sequence that comprises the San Andres Reservoir at the Mabee Field.
A major problem with carbonates such as the San Andres Formation is the generally poor correlation between porosity and permeability. This is the result of facies variation that controlled subsequent diagenesis and dolomitization. In addition, a limited amount of permeability data is available from cores, and extrapolation of this information to the entire reservoir is problematic. At Mabee Field extrapolation of permeability data was accomplished with Gridstats (a Texaco in-house, three-dimensional personal computer-based geostatistical program). Variograms of the permeability were computed to quantify the vertical and horizontal spatial variability. The normalized porosity logs were converted to permeability using a linear transform from the regression of the crossplot of core porosity versus permeability. Within the Gridstats program, the “hard” data (core permeability), “soft” data (normalized porosity logs transformed to permeability), and the correlation coefficient (variance observed in the core data) were kriged to produce a three-dimensional distribution of permeability. A three-dimensional distribution that includes both the spatial variability and data variance is a powerful reservoir evaluation tool. The ability of Gridstats to quickly and easily generate cross sections through the three-dimensional model has been used to evaluate completions, pattern conformity, daily fluid production versus permeability-height, drill depths, and horizontal laterals from existing vertical injection wells.
The accuracy of the three-dimensional model was tested by comparing (1) data extracted from the model to the input core data, and (2) the model to past and present reservoir performance.
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Hydrocarbon Reservoir Characterization: Geologic Framework and Flow Unit Modeling
This collection of papers presents documentation for (1) approaches to be taken in developing a geologic framework for explaining layering, heterogeneity, and compartmentalization of a reservoir; (2) the value of outcrop data in improving understanding of reservoir performance; (3) methods for integrating, analyzing, and displaying geologic, petrophysical rock property, and engineering data to be used during field evaluation, management, and simulation; (4) geostatistical approaches that are being used to characterize the spatial distribution of reservoir properties and augment geologic descriptions, and (5) methods of displaying quantitative models of reservoir properties and reservoir simulation in three dimensions.