The facies architecture, sequence-stratigraphic framework, and structural configuration of a petroleum reservoir were delineated by an integrated study. These interpretations formed the basis of three-dimensional (3-D) geologic models that were constructed for estimation of reserves, flow-simulation studies, and field-development planning. The study incorporated 3-D seismic interpretations, well-log correlations, facies and petrophysical analyses of cored intervals, and interpretations derived from outcrop exposures of the reservoir.
The reservoir intervals are interpreted to represent a fluvial depositional system that varies systematically along an updip to downdip transect. Proximal (updip) fluvial-facies deposits are inferred to represent amalgamated channel complexes that form widespread sheets. Medial fluvial-facies deposits are interpreted as amalgamated to semiamalgamated braid-bar deposits that are thinner and less laterally persistent. Distal (downdip) fluvial facies are inferred to represent thin yet laterally extensive braid-bar deposits.
Object-based modeling techniques were used to model the internal architecture of the reservoir intervals. Proximal channel facies were generated using standard software to populate the zones with channel elements that are clustered to form channel complexes. Medial and distal bar facies, however, required an innovative method that populates the zones with discrete, user-defined, braid-bar elements that are distributed along thalwegs. Clusters of thalwegs form amalgamated to semiamalgamated bar complexes. This capability, referred to as bar-train modeling, results in a better computer-model representation of the fluvial-sandstone bar geometry and spatial distribution.
The resulting geologic models provide an improved reservoir characterization of the large-scale and small-scale fluvial architecture for the subsurface reservoir. In particular, the geologic models more accurately describe the complex architecture of the lowstand channel and braid-bar fluvial sandstones as well as the internal architecture of the intervening mudstone deposits.
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Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.