Publications

Insights on Oligocene-Miocene carbonate mound morphology and evolution from 3D seismic data, East Java basin, Indonesia

Proceedings Title : Proc. Indon. Petrol. Assoc., 32nd Ann. Conv., 2008

The East Java High Density MC3D seismic survey, acquired by PGS in 2002/2003 over the North Madura platform, is excellent data for quantitative interpretation of carbonate mound evolution. Detailed imaging of the growth histories of Oligocene-Miocene carbonate buildups provides insight into geometric parameters characteristic of platform initiation, development, and demise. Mound initiation occurs with development of small (<,100m to 500m diameter), closely spaced, domal buildups, which become the nuclei for the formation of intermediate mounds (2km to 3km diameter). Nucleation mounds build concentrically to form intermediate mounds, which selectively coalesce into amalgamated platforms (>5km diameter), become isolated platforms of varying size (<,5km diameter), or die off altogether. High quality, high resolution 3D seismic data enable visualization and quantitative analysis of geometry, orientation, and spatial distribution of nucleation and intermediate mounds, suggesting models for development of isolated vs. amalgamated platforms. Seismic Discontinuity time-slices datumed at the base of the carbonate section provide clear images of mound size, distribution, and morphology at discrete growth stages, which record a complex history of initiation, aggradation, coalescence or isolation, progradation, potential exposure, and demise. Additional seismic attributes (e.g., isochron) can be exploited by innovative volume interpretation techniques for identifying, isolating, and extracting mounded seismic features within carbonate platforms. Geometric attributes such as mound area, aspect ratio, and orientation are calculated directly from extracted elements of the seismic data. The resulting data provide dimensional, spatial, and seismic facies characteristics of carbonate buildups for conditioning geologic models. Additional ties to well and outcrop data improve prediction of reservoir presence, quality, and distribution.

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