Facies modeling using 3D pre-stack simultaneous seismic inversion and multi-attribute probability neural network transform in the Wattenberg field, Colorado

Abstract

The Niobrara/Codell unconventional tight reservoir play at Wattenberg Field, Colorado has potentially two billion barrels of oil equivalent requiring hundreds of wells to access this resource. The Reservoir Characterization Project (RCP), in conjunction with Anadarko Petroleum Corporation (APC), began reservoir characterization research to determine how to increase reservoir recovery while maximizing operational efficiency. Past research results indicate that targeting the highest rock quality within the reservoir section for hydraulic fracturing is optimal for improving horizontal well stimulation through multi-stage hydraulic fracturing. The reservoir is highly heterogeneous, consisting of alternating chalks and marls. Modeling the facies within the reservoir is very important to be able to capture the heterogeneity at the well-bore scale; this heterogeneity is then upscaled from the borehole scale to the seismic scale to distribute the heterogeneity in the inter-well space. I performed facies clustering analysis to create several facies defining the reservoir interval in the RCP Wattenberg Field study area. Each facies can be expressed in terms of a range of rock property values from wells obtained by cluster analysis. I used the facies classification from the wells to guide the pre-stack seismic inversion and multi-attribute transform. The seismic data extended the facies information and rock quality information from the wells. By obtaining this information from the 3D facies model, I generated a facies volume capturing the reservoir heterogeneity throughout a ten square mile study-area within the field area. Recommendations are made based on the facies modeling, which include the location for future hydraulic fracturing/re-fracturing treatments to improve recovery from the reservoir, and potential deeper intervals for future exploration drilling targets.