Quantification of Uncertainty in Reservoir-Scale Extensional Fault-Propagation Folds, Part 2: Probabilistic Modeling Results

Luc Huyse¹, Kevin J. Smart², Christopher J. Waldhart¹, David S. Riha¹, Barron J. Bichon¹, Alan P. Morris², and David A. Ferrill^2
1Mechanical and Materials Engineering Division, Southwest Research Institute, San Antonio, TX
²Department of Earth, Material, and Planetary Sciences, Geosciences and Engineering Division, Southwest Research Institute, San Antonio, TX

Field data from the Big Brushy Canyon monocline are combined with finite element and probabilistic modeling to bridge the gap between kinematic and mechanical analyses and provide quantifiable measures of uncertainty for a reservoir-scale extensional fault-propagation fold. Because of the inherent variability of geological features and the imperfect knowledge of geologic material properties, an exact match between the natural example and finite element model results is not reasonably achievable. However, a maximum likelihood procedure can be used to compute estimates of uncertainties on and guide assignment of model parameters, and improve the match between models and nature. Model parameters addressed include viscosity of the Del Rio Clay, strength of the Buda layers, and strength of frictional interfaces. The finite element models can be used to make predictions of reservoir properties (e.g., locations of high fracture density) from proxy model outputs (e.g., locations of high differential stress). The advantage of using a probabilistic over a deterministic formulation is that it is possible to not only generate contour maps of locations with the highest mean differential stress but also for any percentile of interest. This can be very different from a deterministic approach because it considers the differential stress variability. In addition, probability contours can be generated for locations where the differential stress exceeds a specific value. This integration of mechanical and probabilistic modeling provides an innovative and robust approach to both guide iterative mechanical modeling and evaluate results to improve understanding of reservoir deformation and uncertainties in reservoir conditions.

AAPG Search and Discovery Article #90078©2008 AAPG Annual Convention, San Antonio, Texas