Print this pageAAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA
Modeling Depositional Heterogeneity of Carbonate Ramps Using Outcrop Analogs and Multiple Point Statistics
(1) Chevron Energy Technology Company, San Ramon, CA.
(2) Chevron Energy Technology Company, Houston, TX.
Outcrop exposures offer continuity, correlation, and resolution of geological data well beyond that of the subsurface, and serve as ‘idealized’ analogs for reservoir characterization. In this study, measured sections, photomosaic mapping, DGPS, and LIDAR data were collected along a 38 km dip exposure of Lower Jurassic carbonate ramp strata in the High Atlas of Morocco. Observed outcrop heterogeneity was converted into a static model using multiple point statistics (MPS) and facies distribution modeling (FDM). The goal was to generate new modeling strategies through simulation of ramp depositional heterogeneity during both transgressive (TST) and highstand (HST) conditions, and for each of the facies belts observed (inner, middle, outer, and basinal ramp settings).
The MPS/FDM approach uses combinations of hard data constraints (i.e. well data) and soft geologic concepts (i.e. depositional models) to populate 3D grid space by pattern recognition. Outcrop LIDAR and GPS data were integral for the model stratigraphic framework and reproduction of complex stratal patterns. Soft constraints, such as Training Images and Facies Probability Cubes, dictate the juxtaposition relationships, spatial proportions, and 3D probabilities of facies belts, whereas measured section and traced outcrop surfaces serve as hard data constraints during simulation. Using this approach, ramp depositional heterogeneity was successfully simulated within the sequence stratigraphic architecture, including stratigraphic partitioning of facies belts, ramp progradation and retrogradation, and changes in facies belt architecture.
This outcrop-based modeling effort provides strategies that can be incorporated into subsurface modeling workflows. For example, only two systems tract-specific (TST and HST) Training Images were required to capture stratigraphic facies belt partitioning. This enabled simulation of muddier, baffle-prone settings in the TST and grainier, reservoir-prone settings in the HST for all mapped sequences. Other learnings included methods for capturing complex ramp paleogeography, variable facies belt morphology, and fine-scale heterogeneity at facies belt transitions. This study is the first of a series of experiments that include uncertainty analysis of input parameters, flow simulation, and application to the subsurface.