Development of deep neural networks for analyzing the prospectivity of Nova Scotia offshore basins

Unlocking the energy potential of offshore basins is key to fully evaluate Canada’s ability to satisfy its future energy demand while respecting UN Sustainable Development Goal 7, Affordable and Clean Energy. In frontier basins, including offshore Nova Scotia, deep-water exploration entails various risks due to uncertain geological settings, thus requiring comprehensive prospect analyses and risk assessments. One of the major sources of hazard is mass transport deposits (MTDs), which commonly host over-pressured subsurface zones. MTDs constitute a considerable volume of sediments in deep-water environments, and understanding their distribution is crucial in assessing drilling hazards and development planning. Here, we propose designing and implementing the first artificial intelligence platform to automatically identify MTDs in over 10,000 square kilometres of offshore Nova Scotia. Further, we apply state-of-the-art processing and interpretation techniques on multiple geophysical and well datasets to determine the distribution and reservoir potential of identified MTDs and associated sediments. We will explore how submarine sediment failures control the formation of new deep-water channel systems, thus delivering new insight on sediment transport and accumulation mechanisms. The proposed project will contribute to understanding Nova Scotia’s offshore energy resources.

Faculty Supervisor:

Vittorio Maselli

Student:

Partner:

Offshore Energy Research Association of Nova Scotia

Discipline:

Earth science

Sector:

Mining; Professional, scientific and technical services

University:

Dalhousie University

Program: