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1 Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, United Kingdom; present address: ExxonMobil Upstream Research Company, P.O. Box 2189, Houston, Texas 77252; richard.sech{at}exxonmobil.com
2 Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, United Kingdom; m.d.jackson{at}imperial.ac.uk
3 Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, United Kingdom
Richard Sech is a research scientist at ExxonMobil Upstream Research Company, Houston. He holds a B.S. degree in exploration geology from Cardiff University, an M.S. degree in reservoir evaluation and management from Heriot-Watt University, and a Ph.D. in petroleum engineering from Imperial College, London. His research interests are in reservoir modeling and quantifying the influence of geologic heterogeneity on fluid flow behavior.
Matthew Jackson is a senior lecturer in reservoir engineering in the Department of Earth Science and Engineering, Imperial College, London. He holds a B.S. degree in physics from Imperial College and a Ph.D. in geological fluid mechanics from the University of Liverpool. His research interests include simulation of multiphase flow through porous media, representation of geologic heterogeneity in simulation models, and downhole monitoring and control in instrumented wells.
Gary Hampson is a senior lecturer in sedimentary geology in the Department of Earth Science and Engineering, Imperial College, London. He holds a B.A. degree in natural sciences from the University of Cambridge and a Ph.D. in sedimentology and sequence stratigraphy from the University of Liverpool. His research interests lie in the understanding of siliciclastic depositional systems and their preserved stratigraphy, and in applying this knowledge to reservoir characterization.
ABSTRACT
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 data sets. We present a surface-based modeling procedure that enables explicit representation of heterogeneity across a hierarchy of length scales. Numerous surfaces are used to construct complex facies-body geometries and distributions prior to generating a grid, allowing sampled and conceptual data to be fully incorporated within field-scale models. 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. Cornerpoint 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 data set. The studied outcrop data set describes reservoir architecture in a generic analog for many shallow-marine reservoirs. The process of model construction has demonstrated the function 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.
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