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Abstract

Summary

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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/content/papers/10.3997/2214-4609.201801076
2018-06-11
2024-04-20

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Determination of Reservoir Lithology from Seismic Data by a 2D Hidden Markov Random Field Model
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201801076
/content/papers/10.3997/2214-4609.201801076
/content/papers/10.3997/2214-4609.201801076
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