Seismic estimation of reservoir properties with Bayesian evidential analysis
- Anshuman Pradhan (Stanford University) | Tapan Mukerji (Stanford University)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Machine learning, Statistics, Reservoir characterization, Inversion, Seismic attributes
- 0 in the last 30 days
- 18 since 2007
- Show more detail
We present a framework that enables estimation of low-dimensional reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential analysis approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties, as well as generate samples from the posterior distribution. We discuss methods of learning highly informative summary statistics from seismic data, which help minimizing computational costs of the approach. We demonstrate the efficacy of our approach by estimating the posterior distribution of reservoir net-to-gross for sub-resolution thin-sand synthetic reservoir.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
|File Size||834 KB||Number of Pages||5|
Beaumont,M. A.,2010,Approximate Bayesian computation in evolution and ecology:Annual Review of Ecology, Evolution, and Systematics,41,379–406,10.1146/annurev-ecolsys-102209-144621.
Bengio,Y.,A.Courville, andP.Vincent,2013,Representation learning: A review and new perspectives:IEEE Transactions on Pattern Analysis and Machine Intelligence,35,1798–1828,10.1109/TPAMI.2013.50.
Blum,M. G. B.,2010a,Approximate Bayesian computation: A nonparametric perspective:Journal of the American Statistical Association,105,1178–1187,10.1198/jasa.2010.tm09448.
Blum,M. G. B.,M. A.Nunes,D.Prangle, andS. A.Sisson,2013,A comparative review of dimension reduction methods in approximate Bayesian computation:Statistical Science,28,189–208,10.1214/12-STS406.
Bosch,M.,C.Carvajal,J.Rodrigues,A.Torres,M.Aldana, andJ.Sierra,2009,Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir:Geophysics,74,no.2,O1–O15,10.1190/1.3043796.
Bosch,M.,T.Mukerji, andE. F.Gonzalez,2010,Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review:Geophysics,75,no.5,75A165–75A176,10.1190/1.3478209.
Buland,A., andH.Omre,2003,Bayesian linearized AVO inversion:Geophysics,68,185–198,10.1190/1.1543206.
Buland,A., andY.El Ouair,2006,Bayesian time-lapse inversion:Geophysics,71,no.3,R43–R48,10.1190/1.2196874.
Dejtrakulwong,P.,T.Mukerji, andG.Mavko,2012,Using kernel principal component analysis to interpret seismic signatures of thin shaly-sand reservoirs:82nd Annual International Meeting, SEG,Expanded Abstracts,10.1190/segam2012-1013.1.
Fearnhead,P., andD.Prangle,2012,Constructing summary statistics for approximate Bayesian computation: Semi-automatic approximate Bayesian computation:Journal of the Royal Statistical Society: Series B (Statistical Methodology),74,419–474,10.1111/j.1467-9868.2011.01010.x.
Grana,D.,T.Mukerji,J.Dvorkin, andG.Mavko,2012,Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method:Geophysics,77,no.4,M53–M72,10.1190/geo2011-0417.1.
Satija,A., andJ.Caers,2015,Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space:Advances in Water Resources,77,69–81,10.1016/j.advwatres.2015.01.002.
Scheidt,C.,P.Renard, andJ.Caers,2014,Prediction-focused subsurface modeling: Investigating the need for accuracy in flow-based inverse modeling:Mathematical Geosciences,47,173–191,10.1007/s11004-014-9521-6.
Scott,D. W.,R. A.Tapia, andJ. R.Thompson,1977,Kernel density estimation revisited:Nonlinear Analysis, Theory, Methods and Applications,1,339–372,10.1016/S0362-546X(97)90003-1.