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In deepwater-reservoir modeling, the proper representation of the spatial distribution of architectural elements is important to account for pore-volume distribution and the connectivity of reservoir sand bodies. This is especially critical for rock and fluid-volume estimates, reservoir-performance predictions, and development-well planning.
A new integrated stochastic reservoir-modeling approach (ModDRE : Modeling Deepwater Reservoir Elements) accounts for geomorphic and stratigraphic controls to generate the deepwater-reservoir architecture. Information on stratal-package evolution and sediment provenance can be integrated into the reservoir-modeling process. A slope-area analytical approach is implemented to account for topographical constraints on channel and sheet-form reservoir architectures and their distribution. Inferred sediment-source statistics and architectural-element variability (from seismic, outcrop, and stratigraphic studies) associated with relative changes in sea-level can also be used to constrain the deepwater-reservoir-element statistics. Based on these geomorphic and stratigraphic constraints, deepwater-reservoir elements (channels, lobes) are built into the model sequentially (in stratigraphic order).
Integration of realistic geological and engineering attributes into deepwater-reservoir models is vital for optimal reservoir management. This approach is unique in that it is more directly constrained to geomorphic and stratigraphic parameters than traditional object-based or surface-based techniques for stochastic deepwater-reservoir modeling.
Supporting organization: American Chemical Society, Petroleum Research Fund
Figures that show a few of the deepwater modeling stages: (1) initial bathymetric surface; (2) modeled channel and lobe architectural elements based on surface slope, paleochannel direction, contributing area, width and thickness statistics, aspect ratio of the elements, etc.; (3) subsequent modeled surface representing a condensed section. |
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Schematic diagram that illustrates the selection of the initial channel aspect ratio and channel thickness. These initial values are randomly selected from corresponding input triangular histograms. Updip-to-downdip aspect ratios (width-to-thickness ratios) of the channel fills range from as low as 10:1 to approximately 50:1. Channel width increases downdip for all channels. Channel-fill thickness decreases across the channel width. |
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This is a 3D view of a deepwater channel-lobe architectural element model generated using ModDRE. Twelve depostional events are modeled in this example. |
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This is another 3D view of a deepwater channel-lobe architectural element model generated using ModDRE. Twelve depostional events are also modeled in this example. |
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Cross section shows the proximal stacking patterns of the channel-lobe-condensed section events. |
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