GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 234-2
Presentation Time: 9:00 AM-6:30 PM


GANSHIN, Yuri, MCLAUGHLIN, J. Fred and BENTLEY, Ramsey, Carbon Management Institute, University of Wyoming, 1020 E. Lewis Street, Energy Innovation Center, Dept. 4902, 1000 E. University Ave., Laramie, WY 80271,

Knowledge of the vertical extent of the reservoir and its zonation is of great importance in hydrocarbon exploration and underground carbon sequestration projects. Fundamental properties of reservoir and confining rocks are usually understood by their detailed description in the field (lithofacies analysis) and laboratory (petrofacies analysis). However, in most subsurface studies, the facies (lithofacies and petrofacies) determination is impractical, due to lack of cores and cuttings. In situations where wireline logs are the only sufficient data available, the logfacies or electrofacies are determined instead. Reliably predicting lithological and depositional facies is an essential component of reservoir characterization and geological modeling. In this study we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir and sealing lithologies. One of the innovations was to use logfacies profile (obtained from interactive cluster analysis of wireline log data) in seismic data interpretation. The methods were applied to a study area on the Rock Springs Uplift (RSU) in southwest Wyoming, a potential long-term CO2 sequestration site. Initial investigations at this site focused on the Weber Sandstone and the Madison Limestone as potential reservoirs, and the overlying Triassic Chugwater Group and Dinwoody Formations as the major sealing strata (Surdam, 2013, Springer). We integrated well log measurements from the RSU #1 stratigraphic test well with numerous core samples that resulted in subdivision of the targeted formations into six petrofacies classes based on their composition and texture. Interactive cluster analysis of well logs was used to verify this classification and to build a continuous logfacies profile. This profile clearly illustrates lithological heterogeneity and is better constrained than facies models derived from conventional petrographic examination of cores.