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

Authors: D. Grana, E. Paparozzi, S. Mancini and C. Tarchiani
Journal name: Geophysical Prospecting
Issue: Vol 61, No 3, May 2013 pp. 613 - 629
DOI: 10.1111/j.1365-2478.2012.01115
Organisations: Wiley
Language: English
Info: Article, PDF ( 2.14Mb )

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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