Seismic-Based Stochastic Porosity Prediction in a Giant Field: A Fine-Scale Quantitative Approach in Carbonates.

Abstract

Stochastic inversion of 3-D seismic data is used increasingly for reservoir characterization. It provides information on the reservoir at a much finer scale than deterministic inversion and delivers multiple scenarios for uncertainty analysis. In this work, a stochastic seismic inversion workflow has been developed to characterize porosity variations in the Thamama reservoir of the giant Field, onshore Abu Dhabi. In contrast to traditional band-limited inversion, this stochastic inversion workflow first generates high frequency models of acoustic impedance which can be used directly for geomodelling without downscaling. Next, a collocated co-kriging sequential Gaussian simulation technique has been applied to generate fine scale 3-D porosity realizations constrained by the impedance stochastic models and by log porosity data. In the example, post-stack stochastic inversion is combined with stochastic porosity modeling to characterize the uncertainty in the spatial distribution of thin, low porosity / permeability intra-Thamama layers, which adversely affect the field water flood performance. These thin layers have been mapped using seismic-constrained stochastic workflow. P10, P50 and P90 porosity realizations have been generated which represent more or less pessimistic scenarios of lateral extent of the tight zone along the flank of the field. A number of blind wells demonstrate that the seismic-based workflow provides more accurate porosity predictions than a purely well-based reservoir model. Specific technical contributions of the work include:Demonstrate the value of seismic information for characterizing mature carbonate reservoirsImplement field-specific stochastic workflow to characterize uncertainty in spatial extent of thin reservoir flow barriers from 3-D seismic dataPerform seismic inversion directly in fine-scale stratigraphic grid to facilitate integration with the field geomodel