Determination of Reservoir Lithology from Seismic Data by a 2D Hidden Markov Random Field Model

Conference paper (2018)

Authors

Runhai Feng Applied Geology - CEG
S.M. Luthi Applied Geology - CEG
A. W. Martinius Applied Geology - CEG
Research Group
Applied Geophysics and Petrophysics (CEG) (TU Delft)
Copyright: © 2018 R. Feng, S.M. Luthi, A. Gisolf, A.W. Martinius
DOI: https://doi.org/10.3997/2214-4609.201801076
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Published Date

2018

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Engineering

Research Group

Applied Geophysics and Petrophysics

Abstract

In this study, geological prior information is incorporated in the classification of reservoir lithologies using the Markov Random Field (MRF) technique. The prediction of hidden lithologies in seismic data is based on measured
observations such as seismic inversion results, which are associated with the latent categorical variables derived from the distribution of Gaussian assumptions. The Hidden Markov Random Field (HMRF) approach can connect
similar lithologies laterally (horizontally) while ensure a geologically reasonable stratigraphic (vertical) ordering. It is, therefore, able to exclude randomly appearing lithologies caused by errors in the inversion. In HMRF, the prior
information consists of a Gibbs distribution function and transition probability matrices. The Gibbs distribution connects similar lithologies and does not need a geological definition derived from non-case-related information.
The transition matrices provide preferential transitions between different lithologies and an estimation of these matrices implicitly depends on the depositional environments and juxtaposition rules between different lithologies.

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