Impact of seismic-inversion parameters on reservoir pore volume and connectivity
- Leandro Passos de Figueiredo (UFSC and LTrace) | Fernando Luis Bordignon (UFSC and LTrace) | Dario Grana (University of Wyoming) | Mauro Roisenberg (UFSC) | Bruno Barbosa Rodrigues (Petrobras)
- 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
- Statistics, Carbonate, Rock physics, Geostatistics
- 0 in the last 30 days
- 18 since 2007
- Show more detail
In this work, we focus on a Bayesian inversion method for the estimation of reservoir properties from seismic data and we study how the inversion parameters, such as rock-physics and geostatistical parameters, can affect the inversion results in terms of reservoir performance quantities (pore volume and connectivity). We apply a Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of facies, acoustic impedance and porosity. The method is based on a Gibbs algorithm integrated with geostatistical methods that sample spatially correlated subsurface models from the posterior distribution. With the ensemble of multiples scenarios of the subsurface conditioned to the experimental data, we can evaluate two quantities that impact the production of the reservoir: the reservoir connectivity and the connected pore volume. For each set of parameters, the inversion method yields different results. Hence, we perform a sensitivity analysis for the main parameters of the inversion method, in order to understand how the subsurface model may be influenced by erroneous assumptions and parameter settings.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
|File Size||4 MB||Number of Pages||5|
Azevedo,L.,R.Nunes,A.Soares,E. C.Mundin, andG. S.Neto,2015,Integration of well data into geostatistical seismic amplitude variation with angle inversion for facies estimation:Geophysics,80,no.6,M113–M128,10.1190/geo2015-0104.1.
Bongajum,E. L.,J.Boisvert, andM. D.Sacchi,2013,Bayesian linearized seismic inversion with locally varying spatial anisotropy:Journal of Applied Geophysics,88,31–41,10.1016/j.jappgeo.2012.10.001.
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.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,2003a,Bayesian linearized AVO inversion:Geophysics,68,185–198,10.1190/1.1543206.
Buland,A., andH.Omre,2003b,Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model:Geophysical Prospecting,51,531–550,10.1046/j.1365-2478.2003.00390.x.
de Figueiredo,L. P.,D.Grana,M.Santos,W.Figueiredo,M.Roisenberg, andG. S.Neto,2017,Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies:Journal of Computational Physics,336,128–142,10.1016/j.jcp.2017.02.013.
de Figueiredo,L. P.,M.Santos,M.Roisenberg, andG. S.Neto,2013,Stochastic Bayesian algorithm to a jointly acoustic inversion and wavelet estimation:83rd Annual International Meeting, SEG,Expanded Abstracts,3273–3277,10.1190/segam2013-0719.1.
de Figueiredo,L. P.,M.Santos,M.Roisenberg,G. S.Neto, andW.Figueiredo,2014,Bayesian framework to wavelet estimation and linearized acoustic inversion:IEEE Geoscience and Remote Sensing Letters,11,2130–2134,10.1109/LGRS.2014.2321516.
Geman,S., andD.Geman,1984,Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images:IEEE Transactions on Pattern Analysis and Machine Intelligence,PAMI-6,721–741,10.1109/TPAMI.1984.4767596.
Grana,D., andE.Della Rossa,2010,Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion:Geophysics,75,no.3,O21–O37,10.1190/1.3386676.
Gunning,J., andM. E.Glinsky,2004,Delivery: An open-source model-based Bayesian seismic inversion program:Computers & Geosciences,30,619–636,10.1016/j.cageo.2003.10.013.
Hirsche,K.,S.Boerner,C.Kalkomey, andC.Gastaldi,1998,Avoiding pitfalls in geostatistical reservoir characterization: A survival guide:The Leading Edge,17,493–504,10.1190/1.1437999.
Matheron,G.,H.Beucher,C.de Fouquet,A.Galli,D.Guerillot, andC.Ravenne,1987,Conditional simulation of the geometry of fluvio-deltaic reservoirs:Presented at theSPE Annual Technical Conference and Exhibition, SPE,10.2118/16753-MS.
Moyen,R., andP. M.Doyen,2009,Reservoir connectivity uncertainty from stochastic seismic inversion:79th Annual International Meeting, SEG,Expanded Abstracts,2378–2382,10.1190/1.3255337.
Nunez,J.,N.McArdle,J.Lowell,D.Norton, andG.Paton,2014,Adaptive geobodies: Data driven, interpreter controlled geobody delineation of channel and carbonate features:12th International Congress of the Brazilian Geophysical Society & EXPOGEF,774–776,10.1190/sbgf2011-159.
Ravalec,M.,B.Noetinger, andL.Hu,2000,The FFT moving average (FFT-MA) generator: An efficient numerical method for generating and conditioning Gaussian simulations:Mathematical Geology,32,701–723,10.1023/A:1007542406333.
Rimstad,K.,P.Avseth, andH.Omre,2012,Hierarchical Bayesian lithology/fluid prediction: A north sea case study:Geophysics,77,no.2,B69–B85,10.1190/geo2011-0202.1.
Rimstad,K., andH.Omre,2010,Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction:Geophysics,75,no.4,R93–R108,10.1190/1.3463475.
Stolt,R. H., andA. B.Weglein,1985,Migration and inversion of seismic data:Geophysics,50,2458–2472,10.1190/1.1441877.