Interpretation 7 (2):T467-T476 (2019)
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Abstract |
Carbonate mounds, as described herein, often present seismic characteristics such as low amplitude and a high density of faults and fractures, which can easily be oversampled and blur other rock features in simple geobody extraction processes. We have developed a workflow for combining geometric attributes and hybrid spectral decomposition to efficiently identify good-quality reservoirs in carbonate mounds within the complex environment of the Brazilian presalt zone. To better identify these reservoirs within the seismic volume of carbonate mounds, we divide our methodology into four stages: seismic data acquisition and processing overview, preconditioning of seismic data using structural-oriented filtering and imaging enhancement, calculation of seismic attributes, and classification of seismic facies. Although coherence and curvature attributes are often used to identify high-density fault and fracture zones, representing one of the most important features of carbonate mounds, HSD is necessary to discriminate low-amplitude carbonate mounds from low-amplitude clay zones. Finally, we use a multiattribute facies classification to generate a geologically significant outcome and to guide a final geobody extraction that is calibrated by well data and that can be used as a spatial indicator of the distribution of good reservoir quality for static modeling.
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DOI | 10.1190/int-2018-0004.1 |
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References found in this work BETA
A Comparison of Classification Techniques for Seismic Facies Recognition.Tao Zhao, Vikram Jayaram, Atish Roy & Kurt J. Marfurt - 2015 - Interpretation: SEG 3 (4):SAE29-SAE58.
Attribute Expression of Fault-Controlled Karst — Fort Worth Basin, Texas: A Tutorial.Jie Qi, Bo Zhang, Huailai Zhou & Kurt Marfurt - 2014 - Interpretation: SEG 2 (3):SF91-SF110.
Geologic Pattern Recognition From Seismic Attributes: Principal Component Analysis and Self-Organizing Maps.Rocky Roden, Thomas Smith & Deborah Sacrey - 2015 - Interpretation: SEG 3 (4):SAE59-SAE83.
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