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

Runhai Feng, Stefan Luthi, Dries Gisolf, Allard Martinius

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publication80th EAGE Conference and Exhibition 2018, 11-14 June, Copenhagen, Denmark
Number of pages5
DOIs
Publication statusPublished - 2018
Event80th EAGE Conference and Exhibition 2018: Opportunities presented by the energy transition - Copenhagen, Denmark
Duration: 11 Jun 201814 Jun 2018
Conference number: 80
https://events.eage.org/2018/EAGE%20Annual%202018

Conference

Conference80th EAGE Conference and Exhibition 2018
Abbreviated titleEAGE 2018
CountryDenmark
CityCopenhagen
Period11/06/1814/06/18
Internet address

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