Roy D. Adams¹, Masoud Nikravesh¹, Mark Sippel², Douglas D. Ekart¹, James W. Collister¹, Raymond A. Levey¹
(1) Energy & Geoscience Institute at the University of Utah, Salt Lake City, UT
(2) Sippel Engineering Inc, Denver, CO

Abstract: Results of the application of soft-computing techniques to characterization of a complex carbonate reservoir in the Ellenburger Group, Permian Basin, West Texas

Analysis of an integrated data set with soft-computing techniques, including neural networks and fuzzy logic, combined with conventional numerical techniques such as geostatistics and pattern recognition, formed the basis for characterization of complex carbonate reservoirs. Geologic, petrophysical, geophysical, geochemical, and reservoir engineering data comprised the diverse dataset collected in a gas field producing from the Ellenburger Group. The complex carbonate reservoirs consist of chaotically fractured dolostone resulting from the collapse of paleocave systems. The methodology developed in this GRI-funded study is now being successfully applied to other regions of petroleum production.

Geologic and petrophysical data included cores, core analyses, image logs, conventional logging suites, and mud logs. Geophysical data consisted of a 3-D seismic survey with attribute analysis. Geochemical data, derived from cuttings-gas measurements, were used in an unconventional analysis that yielded a potential proximity indicator and semi-quatitative predictor of production. Reservoir engineering analyses included conventional production analyses and single-well reservoir simulations.

The numerical analysis of the integrated data lead to prediction of optimal locations for in-fill and step-out wells. Semi-quantitative predictions were made based on probability distributions of the likelihood of gas production displayed in 3-D images derived from the seismic data.

AAPG Search and Discovery Article #90914©2000 AAPG Annual Convention, New Orleans, Louisiana