AVA Stochastic Inversion of Pre-Stack Seismic Data and Well Logs for 3D Reservoir Modeling

Contreras, Arturo; Torres‐Verdín, Carlos; Kvien, Knut; Fasnacht, Tim; Chesters, William

doi:10.3997/2214-4609-pdb.1.f014

Cited by 27 publications

(2 citation statements)

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“…Haas and Dubrule (1994) have developed a rejection-based stochastic method using trace-by-trace Sequential Gaussian Simulation (SGS), where a simulated acoustic impedance trace is accepted only if the corresponding match between synthetic and real seismic is good. More recently, Debeye et al (1996) and Contreras et al (2005) have extended this work using simulated annealing and MCMC techniques, respectively. These methods, which apply sample-by-sample model perturbations, remain very computer-intensive.…”

Section: Introductionmentioning

confidence: 99%

Fast geostatistical stochastic inversion in a stratigraphic grid

Escobar

Williamson

Cherrett

et al. 2006

SEG Technical Program Expanded Abstracts 2006

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We have developed an efficient stochastic AVA inversion technique that works directly in a fine-scale stratigraphic grid, and is conditioned by well data and multiple seismic angle stacks. We use a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for P-and S-wave impedances. We apply a Sequential Gaussian Simulation algorithm to sample the posterior PDF. We perform a trace-bytrace decomposition of the global posterior into local posterior distributions, conditioned by previously simulated traces. Trace-by-trace sampling of the local PDFs generates multiple, high-resolution realizations of the elastic properties. The new sequential algorithm has been implemented to take full advantage of parallel architectures and scales approximately linearly with the number of CPUs. The technique has been successfully tested using real data and a large layered model containing more than 30 × 10 6 grid-cells.

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Section: Introductionmentioning

confidence: 99%

Fast geostatistical stochastic inversion in a stratigraphic grid

Escobar

Williamson

Cherrett

et al. 2006

SEG Technical Program Expanded Abstracts 2006

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show abstract

“…Some of these methods have been combined with optimization techniques (such as simulated annealing, genetic algorithms, gradual deformation, probability perturbation method and ensemble Kalman filter) to obtain optimal models of reservoir properties (see Sams and Saussus 2010a;Sams et al 2011). These methods have been applied to elastic inversion or simultaneous inversion of elastic and reservoir properties and facies (see Sams et al 1999;Contreras et al 2005;Merletti and Torres-Verdin 2006).…”

Section: Introductionmentioning

confidence: 99%

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Grana

Paparozzi

Mancini

et al. 2012

Geophysical Prospecting

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In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.

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