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7th Middle East Geosciences Conference and Exhibition
Manama, Bahrain
March 27-29, 2006
Modeling
and Bayesian AVO Inversion to Predict Hydrocarbon vs. Brine
Occurrence in Sand Reservoirs
ENI - E&P Division, Via Emilia 1, San Donato Milanese 20097 Italy, [email protected]
This paper presents the summary of some 10 years long experience with a quite new approach at exploiting seismic
AVO
information. The request for increased effectiveness and reliability and in general of a more advanced implementation of the
AVO method, capable of quantitatively predicting the distribution and characteristics of fluids, or even the petrophysical
characteristics of the reservoir was set years ago as a top level development goal. This need has been targeted by
implementing a bayesian inversion of
seismic
AVO data, which is based upon a stochastic AVO modelling phase, that allow
“interpretation-steered” extrapolation of known AVO information from the available wells in the area. The method is aimed at
determining the probability that an assigned AVO response, measured from real pre -stack
seismic
data, can be ascribed to
the presence of either brine, gas, oil in a sand reservoir, given the specific geological parameterization. The developed SW
tool compares the real AVO response at the several targets in the study area with a generalized I/G model, which takes into
account the expected (or guessed) variability of all the petrophysical parameters which are expected to impact onto the
AVO phenomenon. This probabilistic model is developed through the statistical analysis of all available wireline logs and
borehole processed data in a large area of interest. From a practical viewpoint, the AVO Fluid Inversion allows effective and
powerful extrapolation of the AVO information to any new exploration target belonging to an homogeneous geologicalpetrophysical
scenario. The computation results, typically provided in the form of fluid probability maps, represent a new
way to leverage pre-stack
seismic
information to benefit the prospect generation and ranking process.