GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 354-11
Presentation Time: 9:00 AM-6:30 PM


STRIGHT, Lisa, Department of Geosciences, Colorado State University, Fort Collins, CO 80523-1482, DURKIN, Paul R., 2018 24th Avenue NW, 2018 24th Avenue NW, 1280 Main Street West, Calgary, AB T2M 1Z5, Canada; Department of Geoscience, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada, SOUTHERN, Sarah J., Department of Geoscience, University of Calgary, 118 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada, ROMANS, Brian W., Geosciences, Virginia Tech, 4044 Derring Hall, Blacksburg, VA 24061 and HUBBARD, Stephen M., Department of Geoscience, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada,

The increasing use of drones to construct high-resolution photogrammetric models (digital terrain models, DTMs) of outcropping stratigraphic units is providing copious amounts of high-quality data for qualitative and quantitative characterization of sedimentary systems. Data include, but are not limited to, detailed geometric descriptions of depositional bodies, and measurements of length scales and spatial relationships of facies distributions within and between depositional bodies. These robust characterizations are: 1) aiding better quantification of and support for sedimentological interpretations; and 2) providing statistical data constraints for predictive subsurface models.

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.