%0 Book Section %1 zunino2014 %A Zunino, Andrea %A Lange, Katrine %A Melnikova, Yulia %A Hansen, ThomasMejer %A Mosegaard, Klaus %B Mathematics of Planet Earth %D 2014 %E Pardo-Igúzquiza, Eulogio %E Guardiola-Albert, Carolina %E Heredia, Javier %E Moreno-Merino, Luis %E Durán, Juan José %E Vargas-Guzmán, Jose Antonio %I Springer Berlin Heidelberg %K geostatistics reservoir %P 683-687 %R 10.1007/978-3-642-32408-6_148 %T Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion %U http://dx.doi.org/10.1007/978-3-642-32408-6_148 %X We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear, multi-step forward model (rock physics and seismology) and to provide realistic estimates of uncertainties. To generate realistic models which represent samples of the prior distribution, and to overcome the high computational demand, we reduce the search space utilizing an algorithm drawn from geostatistics. The geostatistical algorithm learns the multiple-point statistics from prototype models, then generates proposal models which are tested by a Metropolis sampler. The solution of the inverse problem is finally represented by a collection of reservoir models in terms of facies and porosity, which constitute samples of the posterior distribution. %@ 978-3-642-32407-9

@incollection{zunino2014, abstract = {We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear, multi-step forward model (rock physics and seismology) and to provide realistic estimates of uncertainties. To generate realistic models which represent samples of the prior distribution, and to overcome the high computational demand, we reduce the search space utilizing an algorithm drawn from geostatistics. The geostatistical algorithm learns the multiple-point statistics from prototype models, then generates proposal models which are tested by a Metropolis sampler. The solution of the inverse problem is finally represented by a collection of reservoir models in terms of facies and porosity, which constitute samples of the posterior distribution.}, added-at = {2014-06-19T00:19:47.000+0200}, author = {Zunino, Andrea and Lange, Katrine and Melnikova, Yulia and Hansen, ThomasMejer and Mosegaard, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/2d629987aa86c9ae39bc60b16652575d9/fbordignon}, booktitle = {Mathematics of Planet Earth}, description = {Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion - Springer}, doi = {10.1007/978-3-642-32408-6_148}, editor = {Pardo-Igúzquiza, Eulogio and Guardiola-Albert, Carolina and Heredia, Javier and Moreno-Merino, Luis and Durán, Juan José and Vargas-Guzmán, Jose Antonio}, interhash = {e632ef96e952b87b6c12c0d3895e8d4f}, intrahash = {d629987aa86c9ae39bc60b16652575d9}, isbn = {978-3-642-32407-9}, keywords = {geostatistics reservoir}, language = {English}, pages = {683-687}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Earth System Sciences}, timestamp = {2014-06-19T00:19:47.000+0200}, title = {Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion}, url = {http://dx.doi.org/10.1007/978-3-642-32408-6_148}, year = 2014 }