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Description:
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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 150*150*1 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 . |