TY - JOUR
T1 - Reservoir Lithology Determination by Hidden Markov Random Fields Based on a Gaussian Mixture Model
AU - Feng, Runhai
AU - Luthi, Stefan M.
AU - Gisolf, Dries
AU - Angerer, Erika
PY - 2018/7/6
Y1 - 2018/7/6
N2 - In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the assumption of Gaussian distributions. Compared with other statistical methods, such as the Gaussian mixture model or k-Means, which do not take spatial relationships into account, the hidden MRFs approach can connect the same or similar lithologies horizontally while ensuring a geologically reasonable vertical ordering. It is, therefore, able to exclude randomly appearing lithologies caused by errors in the inversion. The prior information consists of a Gibbs distribution function and transition probability matrices. The Gibbs distribution connects the same or similar lithologies internally, which does not need a geological definition from the outside. The transition matrices provide preferential transitions between different lithologies, and an estimation of them implicitly depends on the depositional environments and juxtaposition rules between different lithologies. Analog cross sections from the subsurface or outcrop studies can contribute to the construction of these matrices by a simple counting procedure.
AB - In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the assumption of Gaussian distributions. Compared with other statistical methods, such as the Gaussian mixture model or k-Means, which do not take spatial relationships into account, the hidden MRFs approach can connect the same or similar lithologies horizontally while ensuring a geologically reasonable vertical ordering. It is, therefore, able to exclude randomly appearing lithologies caused by errors in the inversion. The prior information consists of a Gibbs distribution function and transition probability matrices. The Gibbs distribution connects the same or similar lithologies internally, which does not need a geological definition from the outside. The transition matrices provide preferential transitions between different lithologies, and an estimation of them implicitly depends on the depositional environments and juxtaposition rules between different lithologies. Analog cross sections from the subsurface or outcrop studies can contribute to the construction of these matrices by a simple counting procedure.
KW - Bayes methods
KW - Gaussian mixture model
KW - Hidden Markov models
KW - Hidden Markov random fields (HMRFs)
KW - lithology determination
KW - Markov processes
KW - Reservoirs
KW - Rocks
KW - seismic inversion
KW - transition matrix.
UR - http://www.scopus.com/inward/record.url?scp=85049684235&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:38816a60-318f-42b4-95ee-0ab76b47881d
U2 - 10.1109/TGRS.2018.2841059
DO - 10.1109/TGRS.2018.2841059
M3 - Article
AN - SCOPUS:85049684235
SN - 0196-2892
VL - 56
SP - 6663
EP - 6673
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 11
ER -