Monte Carlo reservoir analysis combining seismic reflection data and informed priors

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Standard

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 tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Zunino, A, Mosegaard, K, Lange, K, Melnikova, Y & Hansen, TM 2015, 'Monte Carlo reservoir analysis combining seismic reflection data and informed priors', Geophysics, bind 80, nr. 1, s. R31-R41. https://doi.org/10.1190/geo2014-0052.1

APA

Zunino, A., Mosegaard, K., Lange, K., Melnikova, Y., & Hansen, T. M. (2015). Monte Carlo reservoir analysis combining seismic reflection data and informed priors. Geophysics, 80(1), R31-R41. https://doi.org/10.1190/geo2014-0052.1

Vancouver

Zunino A, Mosegaard K, Lange K, Melnikova Y, Hansen TM. Monte Carlo reservoir analysis combining seismic reflection data and informed priors. Geophysics. 2015 jan. 1;80(1):R31-R41. https://doi.org/10.1190/geo2014-0052.1

Author

Zunino, Andrea ; Mosegaard, Klaus ; Lange, Katrine ; Melnikova, Yulia ; Hansen, Thomas Mejer. / Monte Carlo reservoir analysis combining seismic reflection data and informed priors. I: Geophysics. 2015 ; Bind 80, Nr. 1. s. R31-R41.

Bibtex

@article{a5082542b2854a6aa8ae42d2b36f9ea8,
title = "Monte Carlo reservoir analysis combining seismic reflection data and informed priors",
abstract = "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.",
author = "Andrea Zunino and Klaus Mosegaard and Katrine Lange and Yulia Melnikova and Hansen, {Thomas Mejer}",
year = "2015",
month = jan,
day = "1",
doi = "10.1190/geo2014-0052.1",
language = "English",
volume = "80",
pages = "R31--R41",
journal = "Geophysics",
issn = "0016-8033",
publisher = "Society of Exploration Geophysicists",
number = "1",

}

RIS

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