Carbonate Digital Outcrop Reservoir Models: From Lidar to MPS Simulation*
Jerome A. Bellian1,2, Xavier Janson1, and Mitch Harris2
with contribution from Darrin Madriz1,3 and Sanjay Srivinasan3
Search and Discovery Article #40353 (2008)
Posted November 26, 2008
*Adapted from oral presentation at AAPG International Conference and Exhibition, Cape Town, South Africa, October 26-29, 2008.
Click to view list of articles adapted from presentations by P.M. (Mitch) Harris or by his co-workers and him at AAPG meetings from 2000 to 2008.
1Bureau of Economic Geology, The Jackson School of Geosciences, University of Texas at Austin ([email protected])
2Chevron Energy Technology Company, San Ramon, CA ([email protected])
3GAMMA, Dept. of Petroleum & Geosystems Engineering, University of Texas at Austin
Digital Outcrop Models (DOMs) are one of the best but under-utilized resources for understanding reservoir continuity and geometry. The potential of these data have not been fully realized due to the complex nature and size of the information recorded. An average light detection and ranging (Lidar) DOM can be tens of gigabytes and require special software to manipulate effectively, thus making it impractical for the reservoir modeler to sift through. It has taken nearly a decade to surpass the hurdle of gathering and processing these data effectively, and as a result less energy has been devoted to bringing this type of data directly into the reservoir modeling workflow. In the meantime a large number of datasets have been collected internationally that are readily available to interrogate.
The objective of this study was to address this issue and build Digital Outcrop Reservoir Models (DORMs), which are geocellular models, populated away from the outcrop surface, that may be scaled up to realistic reservoir model size and compared back to the full data from whence they came. For this study, a carbonate ramp with mound geometries similar to those expected in the Middle East reservoirs was chosen. Multiple Point Statistics (MPS) was used to populate facies geometries between lower-order stratal surfaces. This methodology offers a robust logic-based approach to facies distribution while including the detailed geometric data captured with Lidar. The results of this effort have helped bridge the gap between the need for quantitative spatial data by modelers and the adherence to depositional reality observed by geologists.
Why Multipoint Statistics
“The popularity of variogram-based geostatistics lies in the mathematical simplicity of the variogram model, not in its power to generate different types of geologic models…Outcrop data provide a rich source of geologic structure in terms of faults, fractures, facies distribution, and bedding configuration. The variogram is too limiting to capture this rich amount of information” (Caers and Zhang, 2004).
Model constructed by Janson (RCRL)
Caers, J.,and T. Zhang, 2004, Multiple-point geostatistics; a quantitative vehicle for integrating geologic analogs into multiple reservoir models, in Integration of outcrop and modern analogs in reservoir modeling: AAPG Memoir 80, p. 383-394.
Janson, X., 2005, Interaction of tectonism and eustasy in icehouse carbonate buildups and shelf strata, Pennsylvanian Holder Formation, New Mexico: Bulletin West Texas Geological Society, v. 45/2, p. 25.