Viability of fiber optic sensors for borehole measurement of in-situ stresses and stress change monitoring

This project will support efforts to develop a new instrument and techniques to measure the state of stress in the ground. This is a key parameter used in the engineering design of underground mines, tunnels and boreholes for geothermal and shale gas extraction. Existing techniques suffer from reliability issues and it is proposed to use new fiber optic technologies to more reliably measure stress. The intern for this project will work towards a proof of concept, which will later lead to development of a prototype instrument and possible commercialization opportunities.

Flexible and semitransparent solar cells

Solar power is the fastest growing source of renewable energy worldwide. Developing low cost, high efficiency and clean solar energy technologies will be of significant long-term interests. All around the world, silicon solar cells dominate the rooftop solar energy production market due to their high efficiency and stability. However, silicon modules have limited use cases as they are bulky, opaque and difficult to apply to complex surfaces.

Geological and Geochemical Controls on the Source of Hydrogen Sulfide (H2S) in the Early Triassic Montney Tight Gas Reservoir, Northeast British Columbia, Canada

The Montney Formation in the Western Canadian Sedimentary Basin (WCSB) is a productive low permeability natural gas reservoir. Alongside natural gas, it contains high concentrations of hydrogen sulfide (H2S) gas, which is both economically and environmentally detrimental to the exploration and production of natural gas from the reservoir. This research proposal aims to examine the source(s) of the H2S in this reservoir in northeast British Columbia and the geological and geochemical conditions in which H2S gas has formed in or migrated into this reservoir.

Fracture patterns and their control on erosion and geohazards on the Niagara Escarpment, Hamilton, Ontario

The Niagara Escarpment is a dominant landform in southern Ontario and provides the region with exceptional sites of natural beauty including numerous waterfalls and exposed rocky cliffs. However, the escarpment is also a geomorphic feature formed by ongoing erosion processes that create many challenges for those living near or enjoying its natural beauty. Unfortunately, there is very little information or quantitative data regarding the nature of erosion processes or the rates at which they operate along the escarpment.

Comparison of “Maxwell” Plate Electromagnetic Modelling and General, Full-Physics Modelling for Thin, Conductive Mineral Exploration Targets

This proposal outlines research work that will compare the capabilities and accuracy of
a common, fast but approximate approach to computer modelling of geophysical electromagnetic (EM) data with an approach that considers a much more complete description of the modelling problem.

Enhanced carbon capture in manipulated mine tailings

This project will study the effects of CO2 sequestration in mine waste tailings using a robotic mixing system. Mine waste tailings represent a large opportunity for carbon storage, assisting mining companies with goals to improve their carbon footprint. Robotic mixing of the surface of tailings is expected to accelerate carbon storage and mixing and measurement systems will be tested.

Subsurface Mapping and 3D Reconstruction of Silurian Clinton-Medina Groups, Southwestern Ontario

The Silurian age Clinton-Medina Groups is a succession of sedimentary rocks found across Ontario, in outcrop and in the subsurface. The history of the stratigraphy is complex, and therefore confusion exists relating the stratigraphy regionally in the subsurface across Ontario. The Clinton-Medina Group formations are important for Ontario’s energy sector, providing petroleum resources from natural gas pools found in Eastern Lake Erie.

Ice coverage prediction for the St-Lawrence River

This project aims at creating a model for forecasting ice formation in the St. Lawrence Seaway between the Welland Canal and Quebec City. This will improve drastically the planning of all maritime operations during the winter transition period, before the freeze-up.

Deep Neural Networks for applications in public safety

Deep Neural networks have revolutionized machine learning and in particular computer vision. The revolution was achieved by a combination of big data, graphical processing units and advances in numerical optimization. In this work we propose to extend and develop machine learning techniques, focusing on deep learning methods for public health and safety applications. We will use and extend deep learning methodology to deal with 3D seismic and electromagnetic data for signals that are emitted for public safety

Development of numerical algorithms to customize acoustic treatment

Dymedso, a Canadian-based medical device SME specialized in pulmonary disease therapeutic and treatment equipment is a patented pioneer in using sound (acoustics) to treat patients requiring airway clearance such as Cystic Fibrosis COPD and the coronavirus family, SARS, MERS and naturally the COVID-19.

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