Characterization of Thin-Bedded Reservoir in the Gulf of Mexico: An Integrated Approach.
An important fraction of the reservoirs in the Outer Continental Shelf of the Gulf of Mexico is comprised of thin-bedded deposits from channel-levee systems. These reservoirs are particularly difficult to describe. Not only is their architecture complex but the quality of the reservoir is determined by connection and length of beds below the resolution of usual reflection data. Improved characterization is needed to improve development and production of these reservoirs. This study presents an integrated approach to build a geologically consistent reservoir model, based on the 8 sand reservoir in Northern Green Canyon block 18. The underlying idea of the construction of this model is that reservoir quality is influenced more by the internal architecture than by the statistical values of petrophysical parameters. Seismic interpretation and attribute extraction provided the reservoir geometry and stratigraphy. The structural framework and the limits of the reservoir have been determined, showing the preeminent role of salt and faults in the constitution of this reservoir. Seismic attributes are calibrated to extract areal information on reservoir architecture. Gross thickness and net thickness maps have been estimated using geostatistical methods. Lateral variations in the quality of the 8 sand and the definition zones with different average properties were inferred from geostatistical results. Lithofacies characterization from core showed that 3 facies could be used to describe the internal variability. The fine-scale heterogeneity is described in each zone from vertical facies distribution determined from wells. A truncated Gaussian sequential simulation was performed to reflect both the regional trend and the internal variability on a 1501501 ft grid. The major contribution of this work is to show the efficiency of this approach to describe complex reservoirs where the impact of internal variability is a major control of flow efficiency. This is especially valuable when the well information is scarce or not uniformly distributed. This model will be used for flow simulation and sensitivity analysis to improve the field description.