Clayton V. Deutsch, PhD, PEng

Professor 
Alberta Chamber of Resources Industry Chair in Mining Engineering
Canada Research Chair in Natural Resources Uncertainty Management

School of Mining and Petroleum Engineering
University of Alberta
6-232 Donadeo Innovation Cntr for Engineering
Edmonton, Alberta, Canada T6G 1H9
Phone: (780) 492-9916
E-fax: (425) 969-9566
E-mail: cdeutsch@ualberta.ca
Personal website: http://www.ualberta.ca/~cdeutsch/

Research Interests

My research develops best-practice for prediction of geospatial variables with uncertainty. Theoretical developments are aimed at discovering techniques for improved modelling of natural heterogeneity and uncertainty. Computational and tradecraft developments facilitate application of the theory to a range of disciplines.

Theoretical studies have been undertaken into the mathematics of accounting for multiple disparate data from different measurement techniques, at different scales and with different error content. Theoretical advances have also been made in the use of high order statistics beyond the traditional covariance, calculation of non-linear distances for geological modeling, and accounting for parameter uncertainty in geological modeling.

Application to mining and petroleum has included classification of resources/reserves for disclosure, optimal well placement, process-mimicking modeling of different geological processes, multivariate modeling of correlated variables, and characterization of the McMurray formation for optimal planning of in-situ and mining projects.

Research Currently in Progress

My research continues in the direction of characterizing heterogeneity and uncertainty in the Clearwater, Grossmont and McMurray formations. These formations are of critical importance to Alberta. Constructing high resolution models for performance prediction, assessing uncertainty in resources and reserves, and developing best practice are selected research topics. More specifically, one area of research is the use of locally varying statistics in geomodeling. Natural phenomena tend to show gradational and abrupt changes in spatial variation; we are developing a unified framework to account for such variations. On another subject, full physics flow simulation of thermal processes is very professional and CPU intensive. We are developing proxy models to make approximate predictions of performance. The use of experimental design and semianalytical models has contributed to this line of research. In many situations, particularly offshore reservoir development, there are few data available for geological modelling. Special techniques and tools are required to take full advantage of the available data and account for conceptual data. Other research areas include: parameter selection for numerical techniques, matrix solution methods for ill conditioned systems, multiscale modelling, direct multivariate modelling for data integration, the use of distance functions for geometric uncertainty, and modelling of geometallurgical properties in mineral deposits.