Bureau of Economic Geology

2021 Delaware Basin 3D Geologic Modeling, Unconventional Reservoirs in the Western Third of the Permian Basin

December 17, 2021 9:00 AM


Brian Casey, Ph.D.
Senior Geologic Modeler
Bureau of Economic Geology
The University of Texas at Austin


The 2021 Delaware Basin Geologic Model project provides a 3D geo-cellular model interpretation for the Delaware Basin, unconventional reservoir system of the late Pennsylvanian, Wolfcampian and Leonardian. The project was constructed by:

  • Subdividing the Bone Spring and Wolfcamp formations into major zones, and by mapping the basin and slope facies within these zones to their depositional limits against the shelf edge.
  • Updating major faults that were active within the Wolfcamp formation.
  • Adding petrophysical analyses from across the entire basin region. Changes in salinity were incorporated and key reservoir and geomechanical properties were interpreted. Pressure data was provided as input for hydrocarbon storage estimation.
  • Correlating formation volume factors to API gravity, GOR and reservoir pressure, so that hydrocarbon-in-place could be distributed across the entire basin and Bone Spring - Wolfcamp stratigraphic column.
  • Selecting a 3D geo-cellular grid size that is sufficiently fine to capture significant facies, petrophysical, and hydrocarbon variations between well control points, and yet coarse enough for model simulations to run within a day to a few hours.
  • Conducting variogram analyses so that geostatistical Gaussian simulations could be run on all properties.
  • Generating multiple realizations on the properties needed for hydrocarbon volume estimation so that uncertainty could be evaluated.

The resulting model covers 23,630 square miles, with 3D grid cells dimensions (XYZ) that average about 1500 ft x 1500 ft x 5 ft (1874 layers). The total 3D grid contains almost 972 million cells. The basement-involved fault interpretation (Morris, et. al., 2021), has been projected to the base and top of Wolfcamp, and includes mapped Wolfcamp surface faults. The model boundaries extend out to the hypothetical, structural limits of the basin. These structural limits allow depositional limits for Bone Spring and Wolfcamp formation zones to be mapped, except where the formations crop out or have been removed by uplift and erosion.

Petrophysical interpretations of well logs provide a pore pressure gradient model across the entire stratigraphic column. This has been converted to a reservoir pressure model by determining the depth of each data point below the ground surface. Hydrocarbons analyzed by commercial laboratories (PVT analyses) indicate that hydrocarbon properties vary widely and can be mapped as 3-separate oil and 5-separate gas-condensate regions. These hydrocarbon regions correspond to different API oil-gravity, gas-oil-ratio (GOR), and reservoir pressure values, and the correlation of these properties allow a unique range of formation volume factors (FVF) to be estimated for each oil and gas-condensate region.

For most petrophysical, hydrocarbon, geo-mechanical, and mineralogic property models, well data was first converted to a position within the 3D grid. The data was then analyzed relative to its population distribution within each zone. Data potentially influenced by litho-facies (e.g., porosity and water saturation) were also evaluated relative to facies-type. The data distributions were further evaluated relative to their geo-statistical semi-variance (Variogram analysis). Gaussian simulation of the data distributions was used to populate 3D grid cells that did not contain well data. Since each simulation run (realization) will provide a different result dependent on the initial cell location (seed location) used in simulation, multiple realizations were run for porosity, water saturation, GOR, reservoir pressure and FVF. Final hydrocarbon-inplace (HCIP) estimations for each formation zone were estimated using the equation: HCIP = [Bulk-Volume * porosity * (1 - water saturation)] / FVF. Lastly, uncertainty analysis has been applied to the HCIP estimations by using mean and standard deviation values, generated from the multiple realizations of property models.

Brian Casey

University of Texas at Austin

University of Texas

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