Image-based Visual Analytics for Estimating Particle Size Distribution and Permeability of Drill Core

Accurately estimating permeability is critical for being able to properly predict fluid flow in an oil reservoir. Typical approaches, such as estimating permeability using well production data or in the lab using core samples are impractical or expensive in the Alberta Oil Sands. An alternative approach is to infer permeability from particle size distribution that is measured using core samples. However, collecting these samples in the large number of appraisal wells in the Oil Sands is time consuming and expensive. In this project we are proposing to estimate permeability directly from images of drill core using visual analytics techniques that are available in computer science domain. We collaborate closely with our industry partner, Suncor, and will use an extensive dataset from the McMurray oil sands. Research outcomes will include determining the lowest-cost images to use and contributions to fundamental research in visual analytics applied to reservoir engineering and geoscience.

Faculty Supervisor:

Mario Costa Sousa

Student:

Partner:

Suncor Energy Inc (Calgary, AB)

Discipline:

Engineering

Sector:

Mining; Wholesale trade

University:

University of Calgary

Program: