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Updated: Jul 1, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
Published on: August 7, 2017
Seismic attributes analysis and petrophysical modeling for reservoir characterization and prospect identification in
Adel Mahmoud Negm1, Dhyaa H Haddad2, Mohamed I Abdel-Fattah3,4
1Geology Department, Faculty of Science, Al-Azhar University, Cairo, 11884, Egypt.
Abstract:
The Upper Ordovician Mamuniyat Formation represents the principal hydrocarbon reservoir in the I/R oil fields, Murzuq Basin, Libya, where reservoir quality and distribution are strongly influenced by glacial depositional processes and structural complexity. This study presents an integrated reservoir characterization workflow combining multi-well petrophysical analysis, 3D seismic interpretation, seismic attribute analysis (variance, RMS amplitude, and energy), spectral decomposition, and 3D static reservoir modeling. Petrophysical evaluation from eleven wells indicates that the reservoir is dominated by quartz-rich sandstones with shale volume generally < 25%, effective porosity ranging from ~ 12 to 15%, and water saturation varying between ~ 20 and 55%. Seismic attribute analysis reveals a dominant NW-SE-trending fault system controlling reservoir compartmentalization, while amplitude-based attributes delineate sand-prone fairways associated with improved reservoir quality. Spectral decomposition further enhances imaging of channelized depositional elements and demonstrates lateral continuity of reservoir bodies. The integration of seismic-derived indicators with petrophysical properties within a 3D modeling framework identifies zones of enhanced reservoir quality concentrated in the central, northwestern, and southwestern parts of the field. Two new prospective zones are delineated on the upthrown side of the main fault, characterized by favorable seismic responses, higher porosity, and reduced water saturation. Blind well validation confirms good agreement between modeled and observed properties, demonstrating the predictive capability of the approach. Compared to previous studies relying on isolated datasets, this integrated workflow provides a more quantitative and spatially consistent characterization of reservoir heterogeneity, significantly reducing uncertainty and supporting improved exploration and development strategies in structurally complex clastic systems.
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