Monte Carlo reservoir analysis combining seismic reflection data and informed priors
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Monte Carlo reservoir analysis combining seismic reflection data and informed priors. / Zunino, Andrea; Mosegaard, Klaus; Lange, Katrine; Melnikova, Yulia; Hansen, Thomas Mejer.
I: Geophysics, Bind 80, Nr. 1, 01.01.2015, s. R31-R41.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Monte Carlo reservoir analysis combining seismic reflection data and informed priors
AU - Zunino, Andrea
AU - Mosegaard, Klaus
AU - Lange, Katrine
AU - Melnikova, Yulia
AU - Hansen, Thomas Mejer
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Determination of a petroleum reservoir structure and rockbulk properties relies extensively on inference from reflectionseismology. However, classic deterministic methods toinvert seismic data for reservoir properties suffer from somelimitations, among which are the difficulty of handling complex,possibly nonlinear forward models, and the lack of robustuncertainty estimations. To overcome these limitations,we studied a methodology to invert seismic reflection data inthe framework of the probabilistic approach to inverse problems,using a Markov chain Monte Carlo (McMC) algorithmwith the goal to directly infer the rock facies and porosity ofa target reservoir zone. We thus combined a rock-physicsmodel with seismic data in a single inversion algorithm. Forlarge data sets, the McMC method may become computationallyimpractical, so we relied on multiple-point-based a prioriinformation to quantify geologically plausible models. Wetested this methodology on a synthetic reservoir model. Thesolution of the inverse problem was then represented by acollection of facies and porosity reservoir models, which weresamples of the posterior distribution. The final product includedprobability maps of the reservoir properties in obtainedby performing statistical analysis on the collection ofsolutions.
AB - Determination of a petroleum reservoir structure and rockbulk properties relies extensively on inference from reflectionseismology. However, classic deterministic methods toinvert seismic data for reservoir properties suffer from somelimitations, among which are the difficulty of handling complex,possibly nonlinear forward models, and the lack of robustuncertainty estimations. To overcome these limitations,we studied a methodology to invert seismic reflection data inthe framework of the probabilistic approach to inverse problems,using a Markov chain Monte Carlo (McMC) algorithmwith the goal to directly infer the rock facies and porosity ofa target reservoir zone. We thus combined a rock-physicsmodel with seismic data in a single inversion algorithm. Forlarge data sets, the McMC method may become computationallyimpractical, so we relied on multiple-point-based a prioriinformation to quantify geologically plausible models. Wetested this methodology on a synthetic reservoir model. Thesolution of the inverse problem was then represented by acollection of facies and porosity reservoir models, which weresamples of the posterior distribution. The final product includedprobability maps of the reservoir properties in obtainedby performing statistical analysis on the collection ofsolutions.
U2 - 10.1190/geo2014-0052.1
DO - 10.1190/geo2014-0052.1
M3 - Journal article
VL - 80
SP - R31-R41
JO - Geophysics
JF - Geophysics
SN - 0016-8033
IS - 1
ER -
ID: 129892932