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.
The Citation in Applied Geostatistics fits an important niche between the conventional on-week short course and the 2 year Masters degree program. It is ideally suited to those from industry who seek a more indepth understanding of modern geostatistical tools. Restricted to Applied Geostatistics program students.Continuing Ed Summer 2021
The Citation in Applied Geostatistics fits an important niche between the conventional on-week short course and the 2 year Masters degree program. It is ideally suited to those from industry who seek a more indepth understanding of modern geostatistical tools. Restricted to Applied Geostatistics program students.Continuing Ed Fall 2020
Mining concepts and terminology, company operations, stages of mining, unit mining operations, surface and underground mine development and methods, feasibility studies and mine costs, ethics, equity, sustainable development and environmental stewardship, public and worker safety and health considerations including the context of the Alberta Occupational Health and Safety Act.Fall Term 2020
Cell based methods for geology modeling, including indicator formalism for categorical data and truncated Gaussian simulation. Object based and process-based approaches for fluvial reservoirs. Indicators for continuous variable estimation and simulation. Multivariate geostatistics including models of coregionalization, cokriging, Gaussian cosimulation, Markov-Bayes simulation and multivariate data transformation approaches. Introduction to advanced simulation approaches including direct simulation, simulated annealing and multiple point simulation. Prerequisite: Consent of instructor.Fall Term 2020