Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: Applying prior geological information

Runhai Feng, Stefan M. Luthi, Dries Gisolf, Erika Angerer

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

Hidden Markov Models (HMMs) have been applied to predict reservoir lithologies using seismic inversion results as inputs. This approach takes into account the conditional probabilities between different lithologies, i.e. the vertical transitions in sedimentary sequences. These properties are used as prior geological information. In order to relate the seismic inversion results to the true well-log data, HMMs need to be trained based on the Expectation-Maximization theory. Application of the resulting model on a synthetic example from the Book Cliffs (Utah, USA) showed that most lithologies are classified correctly, even for some thin layers. A comparison with point-wise methods in which data samples are treated independently from each other, such as k-means and fuzzy logic classifiers, leads to the conclusion that the spatial correlation in HMMs allows better lithological predictions because the prior information accounts for the geological depositional processes. A real case study with data from the Vienna Basin (Austria) is performed, in which lithologies in a 3D cube are obtained based on properties from seismic inversions, via trained HMMs. While the vertical sequences are shown to be reasonably well predicted, the horizontal continuities are not. This indicates that the future research should focus on the lateral geological relationships.

Original languageEnglish
Pages (from-to)218-229
Number of pages12
JournalMarine and Petroleum Geology
Volume93
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Hidden Markov Models (HMMs)
  • Lateral geological information
  • Lithology classification
  • Seismic inversion
  • Vertical transitions

Fingerprint

Dive into the research topics of 'Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: Applying prior geological information'. Together they form a unique fingerprint.

Cite this