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Book Chapter

Understanding the Seismic Expression of Complex Turbidite Reservoirs Through Synthetic Seismic Forward Modeling: 1D-Convolutional Versus 3D-Modeling Approaches

By
Tomas van Hoek
Tomas van Hoek
Shell International Exploration and Production Kessler Park 1, 2288 GS Rijswijk ZH The Netherlands
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Boudewijn Salomons
Boudewijn Salomons
Shell International Exploration and Production Kessler Park 1, 2288 GS Rijswijk ZH The Netherlands
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Published:
December 01, 2006

Abstract

Synthetic seismic forward modeling has been used for many years to gain a better understanding of the seismic expression of subsurface geology and to ensure consistency between quantitative models and available data. With improvement in static model-building capabilities, increased computing power, and the ongoing need to optimally use seismic information to condition exploration and production models, synthetic seismic modeling approaches have evolved towards 3D modeling of realistic and complex input models.

The 1D-convolutional method of generating 3D synthetic seismic models is computationally very fast and convenient to apply. However, influences of spatially varying lateral resolution, acquisition, processing, and overburden effects on the resulting seismic image are fully or partially neglected. Given the simplifying assumptions of the 1D-convolutional modeling method, it is important to understand the degree to which results are representative of the actual seismic expression of the subsurface geology. It is desirable to know under which circumstances the 1D-convolutional approach can be assumed to be a sufficiently close approximation and under which conditions the more sophisticated 3D techniques are required.

As a contribution to addressing this question, two suites of 3D synthetic seismic models were constructed from high resolution, realistic, and representative static facies models of complex turbidite reservoir architecture; one using the 1D-convolutional method and the other employing a 3D-modeling technique. The latter approach honors lateral resolution, processing, acquisition, and overburden effects. Comparison of results of the two methods suggests potential pitfalls in applying inferences from the 1D method in reservoir characterization (e.g., lithofacies distribution, net-to-gross, and connectivity).

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GCSSEPM

Reservoir Characterization: Integrating Technology and Business Practices

Roger M. Slatt
Roger M. Slatt
Houston, Texas
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Norman c. Rosen
Norman c. Rosen
Houston, Texas
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Michael Bowman
Michael Bowman
Houston, Texas
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John Castagna
John Castagna
Houston, Texas
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Timothy Good
Timothy Good
Houston, Texas
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Robert Loucks
Robert Loucks
Houston, Texas
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Rebecca Latimer
Rebecca Latimer
Houston, Texas
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Mark Scheihing
Mark Scheihing
Houston, Texas
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Hu Smith
Hu Smith
Houston, Texas
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SEPM Society for Sedimentary Geology
Volume
26
ISBN electronic:
978-0-9836096-4-3
Publication date:
December 01, 2006

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