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Abstract

Summary

Bayesian Integrated Reservoir Characterization (BIRCh), ( ) is a new technology designed to provide probabilistic outputs of different subsurface properties utilizing Bayesian inference and advanced optimization algorithms such as Stein Variational Gradient Descent, SVGD, ( ). The technology integrates seismic data and a priori knowledge of elastic parameters in the subsurface in a Bayesian framework, in an objective and data driven approach which does not require any interpreted input such as reservoir horizons and fluid contacts. During this study we show and discuss results of the application of BIRCh to a real data example in the Raven field, Egypt. The resulting subsurface predictions have helped locate with confidence unpenetrated Gas Water Contacts (GWCs) in the field and have highlighted areas with poor and good reservoir quality for future well placement.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202310396
2023-06-05
2026-01-15

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References

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/content/papers/10.3997/2214-4609.202310396
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Bayesian Integrated Reservoir Characterization, BIRCh, and its Application to the Raven Field
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202310396
/content/papers/10.3997/2214-4609.202310396
/content/papers/10.3997/2214-4609.202310396
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