This paper presents the result of a reservoir study where seismic data have been fully utilized to obtain a better rock-type based reservoir characterization model. The study was conducted for a highly faulted carbonate reservoir in the Middle East. The available 3D Seismic data have been used to determine the structure map, fault and fracture network, and porosity distribution. Additionally, two seismic attributes (Energy Half Time and Max. Peak Amplitude) had also been used to assist the determination of stationarity regions used for geostatistical simulation.

The reservoir characterization strategy for this study was started with the detailed development of a geological reservoir rock type (RRT) scheme. The RRT scheme was developed based on depositional facies, diagenetic overprints and petrophysical properties, including pore throat size distribution, porosity and permeability. The scheme was then used to constrain the property modeling inside a geological framework that was built based on the seismic interpretation.

Prior to the development of 3D porosity model, four porosity maps were generated based on the available interpreted Acoustic Impedance (AI) and well log data. Each map represents the difference of the influence of seismic data, from full seismic influence to no seismic influence. One map has been selected to represent the seismic derived porosity based on the degree of correlation between the AI data and well-log porosity.

The integration of seismic-derived porosity into the 3D simulation model was conducted using Bayessian Update principle inside a conditional simulation technique. In this technique, 3D porosity distribution honors the underlying RRT and at the same time its vertical-column average honors seismic-derived porosity. The comparison of the results between the simulation with seismic and simulation without seismic has clearly indicated the value added by seismic data, namely improving the simulation result in the areas of poor well-coverage. This is important in order to honor the diagenesis effect that is difficult to model due to a limited number of wells.

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