A stochastic approach to modeling multiple geological horizons in a layer-cake model is presented. Intercorrelations between the different horizons and interval velocity fields are specified and handled properly. Bayesian statistical methods applied to Gaussian random fields provide stable and consistent predictions and simulations for all horizons and interval velocities in the model. Realistic estimates for the uncertainty can be calculated. The methodology is demonstrated by means of a instructive example as well as on a data set from a petroleum reservoir on the the Norwegian continental shelf.

Keywords:
gaussian random field, stochastic model, prediction variance, simulated surface, subsurface, regression function, coefficient parameter, artificial intelligence, seismic depth conversion, prediction
Subjects:
Reservoir Characterization, Seismic processing and interpretation
Copyright 1991, Society of Petroleum Engineers
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