Journal article
Monte Carlo reservoir analysis combining seismic reflection data and informed priors
University of Copenhagen1
CERE – Center for Energy Ressources Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark2
National Space Institute, Technical University of Denmark3
Mathematical and Computational Geoscience, National Space Institute, Technical University of Denmark4
Determination of a petroleum reservoir structure and rock bulk properties relies extensively on inference from reflection seismology. However, classic deterministic methods to invert seismic data for reservoir properties suffer from some limitations, among which are the difficulty of handling complex, possibly nonlinear forward models, and the lack of robust uncertainty estimations.
To overcome these limitations, we studied a methodology to invert seismic reflection data in the framework of the probabilistic approach to inverse problems, using a Markov chain Monte Carlo (McMC) algorithm with the goal to directly infer the rock facies and porosity of a target reservoir zone. We thus combined a rock-physics model with seismic data in a single inversion algorithm.
For large data sets, theMcMC method may become computationally impractical, so we relied on multiple-point-based a priori information to quantify geologically plausible models. We tested this methodology on a synthetic reservoir model. The solution of the inverse problem was then represented by a collection of facies and porosity reservoir models, which were samples of the posterior distribution.
The final product included probability maps of the reservoir properties in obtained by performing statistical analysis on the collection of solutions.
Language: | English |
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Publisher: | Society of Exploration Geophysicists |
Year: | 2015 |
Pages: | R31-R41 |
ISSN: | 19422156 and 00168033 |
Types: | Journal article |
DOI: | 10.1190/GEO2014-0052.1 |
ORCIDs: | 0000-0003-4529-0112 |
Inverse problems Markov chain monte carlo algorithms Markov processes Monte Carlo methods Nonlinear forward model Petroleum reservoir engineering Petroleum reservoirs Porosity Posterior distributions Probabilistic approaches Reflection seismology Seismic prospecting Seismic reflection data Seismic response Seismic waves Seismology Synthetic reservoir models Uncertainty estimation