APPLYING OUTCROP ANALOGUE STATISTICS TO SUBSURFACE MODELING WORKFLOWS
We present a method to quantify vertical and horizontal facies probability curves from measured section data within interpretive stratigraphic frameworks of DTMs. For example, separate curves distinguish low energy positions in a depositional system from high energy positions. These curves probabilistically define facies relationships within and across surfaces in the depositional system, assessing, for example, the probability of flow connectivity across surfaces as a function of the thickness of the stratigraphic package. Further, proportion curves are used to generate input constraints for subsurface models to elucidate the impacts of fine scale internal stratigraphic (bed-scale) architecture on fluid flow. The goal is to use realistic, statistically-grounded modeling to evaluate geologic controls on subsurface fluid flow uncertainty and to quantify the associated risk. Challenges in building models that mimic stratigraphic observations are highlighted.
Statistical data from digital terrain models are setting the standard for how we evaluate and interpret sedimentological systems and transfer that knowledge and data into predictive subsurface models for groundwater storage and remediation, CO2 sequestration, or hydrocarbon recovery. Although simplistic workflows and tools to integrate field data and active interpretations and analyses are still lacking, our analysis provides an important step forward to demonstrate where the science is heading.