--> --> Carbonate Digital Outcrop Reservoir Models: From Lidar to MPS Simulation, by Jerome A. Bellian, Xavier Janson, and Mitch Harris, #40353 (2008).

Datapages, Inc.Print this page

Click to view presentation in PDF format.

 

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

 

Abstract

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.

uAbstract

uFigures

uMPS

uRCRL summary

uETC study

uSummary

uImprovement

uReferences

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uFigures

uMPS

uRCRL summary

uETC study

uSummary

uImprovement

uReferences

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uFigures

uMPS

uRCRL summary

uETC study

uSummary

uImprovement

uReferences

 

Multipoint Statistics

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).

Training Image

  • MPS is based on the Training Image.
  • Numerical representation of the spatial law.
  • Explicit conceptual description of the geological structures and patterns.
  • More intuitive than variogram, since patterns can be directly observed.
  • Like 2 point statistics, MPS assume stationarity.

Reservoir Characterization Research Laboratory (RCRL) Ramp Study
An SGS Approach: Summary

Model constructed by Janson (RCRL)

  • Cell size 10 x 10 x 1 meter.
  • Modeled facies with SG simulated surfaces.
  • Modeled mound geometries not facies relationships.
  • Used thickness trends to control vertical mound growth patterns following energy regimes.
  • Unique approach with good results but did not directly use laser data in facies modeling.

Energy Technology Company (ETC) Ramp Study

           Summary

  • Cell size 8 x 8 x 1 meter.
  • Simulated facies with FDM/MPS methodology.
  • Statistically honors geobodiesdistribution and geometries but completely obliterate the stratigraphic architecture.
  • Did not honor the conceptual geological model.
  • Directly incorporating laser data in facies modeling provide an order of magnitude more conditioning data.

           Improvement

  • More complex/multiple training image(s) will improve match to observed (Darrin Madriz MScThesis).
  • Or improve the stationarity modifier (FDM) to include the thickness trends observed in outcrop and explain with the conceptual model.

Selected References

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.

 

Return to top.