The advancement of multiattribute analysis techniques has significantly improved the ability to capture subtle geologic information that is not easily detected on the original seismic. The use of corendering and automated facies clustering/classification techniques with an appropriate set of seismic attributes increases the reliability and accelerates the interpretation process. We have proved the effectiveness of multiattribute corendering and self-organizing maps (SOMs) in characterizing the late Pleistocene fluvial system of the Sunda shelf that was previously studied using the original seismic only. The work aims to document the attribute expression and capture subtle depositional patterns of the fluvial system. We find that amplitude, texture, and spectral attributes delineate the lithology contrast among different depositional features, whereas geometric attributes reveal channel morphology. The red-green-blue and alpha blends significantly increased the data interpretability by manipulating up to four attributes in the same display. In addition, the SOM clustering aids in distinguishing among different sand-prone depositional elements and muddy facies. We use the cross-cutting relation and superposition rules to understand the stratigraphic evolution of the interval. We observe several fluvial depositional elements through a closely spaced time slicing, including straight channel, meander channel, point bar, crevasse splay, and levee. We recognize that the straight and low-sinuosity channels were developed along preexisting faults and find a deeper incision and thicker sediment fill than the other channel types. The widely variable fluvial style in the middle of the interval is attributed to a rapid change in the accommodation space and climate conditions. The small meanders that find a prominent dendritic pattern indicate a marine influence on the less channelized uppermost part of the interval. The well-imaged and documented shallow fluvial system in the area represents an excellent analogous that helps to understand and accurately predict the reservoir distribution in the deeper productive stratigraphic units.

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