Print this pageThree-Dimensional Modeling of a Shoreface-Shelf Parasequence Reservoir Analog, Part One: Surface-Based Modeling to Capture High Resolution Facies Architecture
Conventional reservoir modeling approaches are developed to account for uncertainty associated with sparse subsurface data, but are not equipped for detailed reconstruction of high-resolution geologic datasets. We present a surface-based modeling procedure that enables explicit representation of heterogeneity across a hierarchy of lengthscales. Numerous surfaces are used to construct complex sandbody geometries and distributions prior to generating a grid, allowing sampled and conceptual data to be fully incorporated. Our approach is driven by the improved efficiency that surfaces introduce to reservoir modeling through their geologically intuitive design, rapid construction and ease of manipulation. Adaptive gridding of the architecture defined by the surfaces reduces the number of cells required to represent complex geometries, thus preserving geologic detail and rendering upscaling unnecessary for fluid-flow simulations.
The application of surface-based modeling is demonstrated by reconstructing the detailed three-dimensional facies architecture of a wave-dominated, shoreface-shelf parasequence from a rich outcrop dataset. The studied outcrop dataset describes reservoir architecture in a generic analog for many shallow-marine reservoirs. The process of model construction has refined our sedimentologic understanding of shoreface-shelf reservoirs by emphasizing the role of (1) shoreface-shelf clinoforms, (2) paleogeographic changes in shoreline orientation, and (3) storm-event-bed amalgamation in controlling facies architecture. These subtle geometric features cannot be accurately represented using conventional stochastic reservoir modeling algorithms, which results in poor estimation of facies proportions and associated hydrocarbon volumes in place. In contrast, the surface-based modeling approach honors all data and captures subtle geometric facies relationships, thus allowing detailed and robust reservoir characterization.
AAPG Search and Discovery Article #90090©2009 AAPG Annual Convention and Exhibition, Denver, Colorado, June 7-10, 2009