Carbonate Digital Outcrop Reservoir Models: From Lidar to MPS Simulation

Jerome A. Bellian¹, Xavier Janson¹, and Paul M. Harris^2
1Bureau of Economic Geology, Jackson School of Geosciences, University of Texas, Austin, TX
²Energy Technology Company, Chevron, San Ramon, CA

Digital Outcrop Models (DOMs) are one of the best but underutilized 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 mean time 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) were 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.

AAPG International Conference and Exhibition, Cape Town, South Africa 2008 © AAPG Search and Discovery