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AAPG Bulletin

Abstract

AAPG Bulletin, V. 98, No. 9 (September 2014), P. 18111835.

Copyright copy2014. The American Association of Petroleum Geologists. All rights reserved.

DOI: 10.1306/02271413028

Automatic calibration of stratigraphic forward models for predicting reservoir presence in exploration

O. Falivene,1 A. Frascati,2 S. Gesbert,3 J. Pickens,4 Y. Hsu,5 and A. Rovira6

1Shell International Exploration and Production Inc., 3333 Highway 6 South, Houston, Texas; [email protected]
2Shell Global Solutions International B.V., Kessler Park 1, Rijswijk, Netherlands; [email protected]
3Shell Global Solutions International B.V., Kessler Park 1, Rijswijk, Netherlands; [email protected]
4Presently at Shell UK Limited, London, United Kingdom; Shell International Exploration and Production B.V., Carel van Bylandtlaan 30, The Hague, Netherlands; [email protected]
5Shell International Exploration and Production B.V., Carel van Bylandtlaan 30, The Hague, Netherlands; present address: Brunei Shell Petroleum Co., Brunei Darussalam; [email protected]
6Shell International Exploration and Production B.V., Carel van Bylandtlaan 30, The Hague, Netherlands; present address: Petroleum Development Oman, Muscat, Oman; [email protected]

ABSTRACT

Understanding and predicting reservoir presence and characteristics at regional to basin scales is important for evaluating risk and uncertainty in hydrocarbon exploration. Simulating reservoir distribution within a basin by a stratigraphic forward model enables the integration of available prior information with fundamental geologic processes embedded in the numerical model. Stratigraphic forward model predictions can be significantly improved by calibrating the models to independent constraints, such as thicknesses from seismic or well data. A three-dimensional basin-scale stratigraphic forward-modeling tool is coupled with an inversion algorithm. The inversion algorithm is a modification of the neighborhood algorithm (a type of genetic algorithm), which is designed to sample complex multimodal objective functions and is parallelized on computer clusters to accelerate convergence. The process generates a set of representative geological models that are consistent with prior ranges for uncertain parameters, calibration constraints, and associated tolerance thresholds. The workflow is first demonstrated on two data sets: a synthetic example based on a clastic passive margin and a real hydrocarbon exploration example for slope and basin-floor stratigraphic traps in the Neocomian (Lower Cretaceous) of the West Siberian Basin. The analysis of calibrated models provides constraints on stratigraphic controls, and allows prediction of locations with higher potential to develop stratigraphic traps. These locations are related to complex interactions between paleobathymetry, subsidence, eustatic fluctuations, characteristics of sediment-input sources, and sediment-transport parameters. Results show the potential of stratigraphic forward modeling combined with inverse methods as an additional tool to support conventional play-based exploration and reservoir-presence prediction.

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