Application of Data Analytics Approaches for Solvent-Assisted Bitumen Recovery Pilot Analysis

This project will develop data-driven models for production performance analysis and optimization for solvent-assisted bitumen recovery operations and related processes. Effective operations of solvent processes are crucial for improving oil production and maintaining a low solvent-to-oil ratio (SOR).

Experimental investigation and validation of flow control device (FCD) performance and design in thermal oil production

Oil recovery processes use flow control devices (FCDs) to ensure uniforms flow of fluids with minimized potential for well failure. These devices operate by restricting the flow through nozzles causing its velocity and pressure to significantly change. For the flow to keep its momentum, its pressure has to drop which unfortunately increases the likelihood of local well failure to occur. In this research, the performance of various nozzle types will be tested to investigate the effect of geometry on the pressure drop.

Deep Learning Method for Micromotion Detection and Mental Health Disease Diagnosis

Over the past few decades, mental disorders (e.g., depressive and anxiety) have become a significant medical burden for people of all ages. According to the survey performed by the World Health Organization (WHO), at least one out of ten people in the world suffers from mental health diseases (i.e., mental disorders, neurological disorders and addition). Many factors, such as heredity, work pressure and aging, can attribute to these disorders and degradations. However, some of these mental health diseases are preventable and treatable.

Wearable and High-Frequency Bias-Switchable Row-Column 2D Ultrasound Array Development

Most ultrasound scans require time-consuming manual scanning of transducer arrays to obtain 2D images of the body. 3D images can be acquired by so-called matrix probes but these are large and bulky, and typically offer inferior image quality. Such probes do not exist in high-frequencies important for pre-clinical applications and to date no wearable 2D probe exists. Our vision is to create wearable flat-form-factor 2D arrays that could be used for longitudinal monitoring of the heart or other critical parameters in a hands-free way.

Stratigraphy and Sedimentology of the Clearwater Formation in Marten Hills and Nipisi north-central Alberta

The general objective of this research is to understand the geology of the Clearwater Formation in a region of Alberta where new oil and gas reserves have been discovered. The research will include detailed mapping throughout the region to understand where the best oil and gas resources are. This area is especially interesting as enhance production techniques, such as using steam injection or fracking the reservoir are not needed to produce the resources.

Developing measuring techniques for online monitoring of ex-vivo organ support system (EVOSS)

The ex-vivo organ support system (EVOSS) being developed by Tevosol, Inc.
Organ transplantation remains the standard therapy for treatment of end-stage heart and lung failure. Tevosol Inc., a Canadian organ transport device company in the market, is developing an ex-vivo organ support system (EVOSS). The EVOSS is a system that is used to preserve donor organs in a working state at body temperature during the time between donation and transplantation into a patient.

Development of climate sensitive growth functions for western North America’s boreal tree species - Year two

The Mixedwood Growth Model (MGM) is used by forest managers in estimating growth and yield outcomes for common boreal tree species in North America. MGM has been shown to effectively model both managed and unmanaged stands in Alberta and surrounding regions. Currently, climate effects are not accounted for in growth functions used in MGM. Recent work for black spruce has shown that there is need to understand and model the effect of climate for other boreal tree species including white spruce, aspen, balsam poplar, lodgepole pine and jack pine.

XFEM-based fatigue crack growth simulation and surrogate model development for probabilistic remaining fatigue life prediction of pipelines

Pipelines have significantly contributed to the Canadian energy industry and overall economy. Specifically, nearly 60% of energy consumed in Canada comprises of oil and gas delivered through pipelines. However, in pipeline steel, many failures were caused by cracks during pipeline operation. The proposed research project aims at developing a reliable and effective tool to predict fatigue crack growth under cyclic fatigue loading.

Summer-season streamflow prediction model for the Oldman River Basin

Reliable monthly and seasonal streamflow predictions are essential for optimal planning of water resources, particularly for reservoir operation and planning applications. Streamflow predictions can also improve water use efficiency and provide early drought and flood warning. The importance of streamflow forecasting is rising with climate change, causing more frequent and hazardous flood and drought events.

High Throughput Phenotyping of Spectrally-Optimized Plant Growth

Light is essential to plant growth and development. Despite being established in plant growth systems, current light systems on the market lack the ability to replicate key daily events such as dawn and dusk differences in light quality and intensity that occur naturally. Edmonton, Alberta based technology company G2V Optics Inc has commercialized a precision, programmable-spectra lighting technology aimed at reducing energy inputs for greenhouse food production. Collaboration between the Uhrig lab and G2V Optics successfully generated large amounts of data (e.g.