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Multiscale Natural Fracture Characterization and Controlling Parameters Contribution Quantification Analysis: Woodford Example


Natural fracture characterization has become an integral part and key screening parameter in unconventional reservoir development and management since fracture failure is the main contributor of flowability in low permeability matrix shale. Fracture density is a direct parameter to evaluate the impact of natural fractures on total rock (matrix plus fracture) permeability. The frequency of natural fracture along a bed is related to rock brittleness, bed thickness, the extent of regional strain, burial depth etc. This study selected the upper Woodford Shale member as an example from two areas (1) Woodford Outcrop (I-35 roadcut) on the edge of the Ardmore Basin (2) a Woodford core (Hall 2B) along the Wichita Mountain Front to characterize and understand fractures in a shale reservoir under different reservoir conditions. The upper Woodford member is exceptionally brittle due to the abundance of recrystallized radiolaria (chert) interbedded with clay-rich shale and fractures are mainly bed bounded.

2D area fracture density was measured for 75 ft2 outcrop sections and 375 in2 core sections. Three corresponding touchable, upscaled parameters were calculated: hardness (average hardness from each bed within the unit measuring 2D window), hard bed ratio (number of the hard beds within a unit vertical section), bed frequency (number of interbeds within a unit vertical section).

A data analysis workflow including Principal Component Analysis (PCA) and Partial Least Square Regression (PLS) was used to quantify the parameters’ relationship and the PLS model was able to obtain the Variable Importance for Projection (VIP) which reveals the contribution of each parameter to the total fracture density. Bed frequency (anisotropy) is statistically the dominant parameter that contributes in average three times more than hard bed ratio and hardness to fracture density. Hardness contribution is decreased by 22% due to the increase in confining pressure in the subsurface condition. The result highlights the importance of the interbed effect on fracture density which can easily be ignored when estimating the fracture density in the subsurface. The relationship obtained from the PLS model can be projected to different scales for fracture density prediction.