Identification of Facies from Multiple Well Logs Accounting for Spatial Dependencies and Convolution Effects

D.V. Lindberg; E. Rimstad; H. Omre

doi:10.3997/2214-4609.20140664

Identification of Facies from Multiple Well Logs Accounting for Spatial Dependencies and Convolution Effects
Authors D.V. Lindberg¹, E. Rimstad², H. Omre¹
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Affiliations: ¹ Norwegian University of Science and Technology ² Statoil ASA
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, 76th EAGE Conference and Exhibition 2014, Jun 2014, Volume 2014, p.1 - 5
DOI: https://doi.org/10.3997/2214-4609.20140664

Abstract

Summary

Facies identification from multiple well logs is performed in a case study on a pair of wells from a reservoir offshore Norway. The inversion is cast in a Bayesian setting, with spatial dependencies in the facies enforced in the prior model by a Markov chain assumption and with possible convolution effects accounted for in the likelihood model. The proposed method outperforms a simpler model without these two properties in terms of correct facies classification, with more reliable predictions especially on layer thickness.

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2014-06-16

2024-04-24

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References

Li, Y., and Anderson-Sprecher, R.
, (2006). Facies identification from well logs: A comparison of discriminant analysis and naive Bayes classifier. Journal of Petroleum Science and Engineering, 53, 149–157.
[Google Scholar]
Lindberg, D.V., Rimstad, E., and Omre, H.
, (2014). Inversion of well logs into facies accounting for spatial dependencies and convolution effects, Paper submitted
[Google Scholar]
Reeves, R., and Pettitt, A.N.
, (2004). Efficient recursions for general factorisable models. Biometrika, 91, 751–757.
[Google Scholar]
Rimstad, K., and Omre, H.
, (2013). Approximate posterior distributions for convolutional two-level hidden Markov models. Computational Statistics & Data Analysis, 58, 187–200.
[Google Scholar]

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Identification of Facies from Multiple Well Logs Accounting for Spatial Dependencies and Convolution Effects

Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20140664

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Abstract

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