Deepwater Reservoir Modeling Using Sequence-Stratigraphic and Geomorphic Constraints

In deepwater-reservoir modeling, it is important to properly represent the spatial distribution of architectural elements to account for pore-volume distribution and the connectivity of reservoir sand bodies. This is especially critical for rock and fluid-volume estimates, reservoirperformance predictions, and development-well planning. This new integrated stochastic reservoirmodeling approach accounts for stratigraphic and geomorphic controls to generate the 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 paleochannel direction statistics (from outcrop and stratigraphic studies) and simulated high-frequency eustatic sea-level curves are also used to constrain the architectural-element statistics. Based on these geomorphic and sedimentological constraints, architectural elements (channels, lobes, sheets) are built into the model sequentially (in age order). Integration of realistic geological and engineering attributes into numerical reservoir models is vital for optimal reservoir management. This approach is unique in that it is constrained more directly to geomorphic and sedimentological parameters than traditional object-based or surface-based techniques for stochastic deepwater reservoir modeling. Introduction Our understanding of the reservoir architecture of deepwater systems has improved with recent advances in imaging of the shallow and deep subsurface and through characterization with outcrop analogs. However, we do not have a complete knowledge of the subsurface environment, so a high degree of uncertainty remains when building deepwater-reservoir models. Stochasticmodeling approaches are useful because they provide a means of quantifying uncertainty through generation of multiple realizations of reservoir-property models. A number of stochastic modeling techniques are presently available for building deepwater reservoir models that can be broadly classified into three categories: (1) cellbased approaches that primarily implement two-point geostatistics, and more recently multipoint geostatistical concepts; (2) object-based or Boolean approaches have been used to build more geologically realistic reservoir models that incorporate nonlinear features. The geologic objects are conditioned to hard data (e.g. wells) and also honor stratigraphic relationships and interpretations; (3) stochastic surface-based techniques have been used to capture the compensational stacking tendency of flow-event deposits within deepwater lobes. In contrast to stochastic methods, processbased methods attempt to simulate fundamental geological processes to produce a numerical representation of the reservoir geology. These approaches include the rigor of the physics of sedimentation and depositional processes. However, enormous difficulties arise when it comes to conditioning process-based models to existing data (e.g., honoring well and seismic data). We introduce a novel approach to deepwater reservoir modeling that has aspects of process-based techniques but is also related to stochastic surfacebased methods. A combination of concepts is adopted in this approach to honor geomorphic and stratigraphic constraints. In this paper, geomorphic refers to bathymetry and parameters derived from bathymetry. To identify flow paths for deepwater channels and lobes, concepts from hypsometric analysis of channelized flow are incorporated. The spatial variability in deepwater architecture that is common within a sequencestratigraphic framework is incorporated through inputs SPE 95952 Deepwater Reservoir Modeling Using Sequence-Stratigraphic and Geomorphic Constraints M.J. Pranter, SPE, Z.A. Reza, SPE, and P. Weimer, U. of Colorado