Huan Yu – Ph.D. Student, University of Northern British Columbia

Biography:

Huan Yu is a Ph.D. candidate in natural resource and environmental studies at the University of Northern British Columbia, supervised by Dr. Wenbo Zheng. Huan obtained her M.Sc. in geotechnical engineering and B.Sc. in civil engineering from Sichuan Agriculture University, China. Huan’s master’s research focused on the shear strength and dilation of thinly infilled rock joints during shearing. Her current research explores the influence of mineralogical characteristics on the mechanical properties and creep behaviors of tight formations in British Columbia. The aim of her research is to enhance the understanding of correlations between microscale properties and macro behavior in tight formations, thereby facilitating responsible resource extraction and geological risk management.

Project: Influence of Mineralogical Characteristics and Microstructure on Mechanical Properties and Creep Behaviors of Tight Formation in Western Canada

There is significant natural gas development in the Montney Formation, as well as potential in the Horn River, Liard and Cordova unconventional gas plays. This production relies on conductive fracture networks created by hydraulic fracturing and supported by proppants (a solid material designed to keep an induced hydraulic fracture open), which is highly related to the mechanical rock properties of targeted formations. Furthermore, the embedding of proppants into fractures diminishes fracture conductivity, which is also affected by the creep behaviors of fractured rock. This project aims to address the challenge of accurately characterizing the mechanical properties and creep behaviors of tight formations, particularly in the Montney and Cordova plays.

Utilizing advanced techniques such as instrumented indentation testing, image analysis provides a deeper understanding of the relationship between mineralogical/microstructural features and rock behavior. Developing predictive models or correlations between microscale properties and macro behavior using machine learning enhances the ability to anticipate the mechanical response of tight formations.