An Integrated Approach for Characterizing a Sandstone Reservoir in the Anadarko Basin

Ampomah, W. (Petroleum Recovery Research Center) | Balch, R. S. (Petroleum Recovery Research Center) | Ross-Coss, D. (Petroleum Recovery Research Center) | Hutton, A. (Petroleum Recovery Research Center) | Cather, M. (Petroleum Recovery Research Center) | Will, R. A. (Schlumberger Carbon Services)

OnePetro 

Abstract

This paper presents field scale reservoir characterization of the Farnsworth field unit undertaken as part of a Phase III project conducted by the Southwest Regional Partnership on Carbon Sequestration. Farnsworth Unit (FWU) is located on the northwest shelf of the Anadarko Basin. The target unit for CO2 injection, storage, and enhanced oil recovery (EOR) is an upper Morrow sandstone informally named the Morrow B Sand.

The Morrow B reservoir was deposited during early Pennsylvanian time as incised valley fill fluvial sands. Core and thin sections were examined to determine lithology, mineralogy, porosity types, depositional environment and diagenetic history. Data from XRD analysis, optical and scanning electron microscopy, and microprobe analysis were compared with results from an elemental log analysis (ELAN). This information, together with additional core, well log, borehole image log, vertical seismic profile and 3D surface seismic data was used to characterize and subsequently create a fine scale lithofacies-based geological model of the field. Structural modeling was based on integration of 3D seismic and compressional sonic well log data to create a velocity model convertin the seismic z-axis into the depth-domain. Converting domains allowed the 3D seismic data to be correlated to other depth-domain datasets, such as new and legacy well log data and core sections. Seismic attributes were able to illuminate previously unknown faults and structural elements within the field.

During the petrophysical modeling, several deterministic and stochastic techniques were compared and analyzed to ascertain which method best populates the geological properties into the model. These techniques included kriging, sequential Gaussian simulation (SGS) and Gaussian random function simulation (GRFS). A data analysis approach was used as a quality check to reduce uncertainty in the modeling. Studies of depositional environment provided data used constructing porosity—permeability crossplots; this proved to be a useful approach to assigning permeability to the lithofacies-based geological model. The accepted static model was upscaled for dynamic reservoir simulation.

The approach illustrated in this study presents an improved methodology in characterizing heterogeneous and complex reservoirs that can be applied to reservoirs with similar geological features.