Abstract

Modern mining facilities are generating an increasingly large amount of data, information and knowledge that can be used to support engineers and owners to build, maintain, and safely operate facilities, globally. Geotechnical data in particular spans from subsurface in situ instruments to satellite-based technologies; and from field observations to advanced laboratory tests. Without proper management, processing, analysis and communication, the full value of this data can be lost. These datasets require analysis and analytical processes to transform them from data to tangible information that can be converted to actionable knowledge by a trained professional. As the ability to collect data increases exponentially, the challenge to manage, integrate, and make informed geotechnical decisions is amplified by the lack of standardization with respect to file types, processing workflows, and geospatial organization. Efficient and effective management and integration of this data can be time consuming and difficult, but if done correctly it can add considerable value to the data and result in better understanding and decision making. To have mature digital twins that can predict behaviour, recommend and execute proactive measures in real-time, optimize efficiency and manage risk, we must first collect the right data, organize information, and setup systems to act as an integrated centralized knowledge base. The development of an integrated centralized knowledge base allows a project team, including owners, contractors, and consultants to access one source of data, which provides an unprecedented ability to work collaboratively to solve complex problems and turn data into actionable knowledge.

The design and establishment of data systems is fundamental to the development of a successful geotechnical digital twin. The early development of a geotechnical twin for various facilities owned and operated by Suncor is focused on establishing an integrated centralized knowledge base consists of the following:

• Cloud hosted, secure, web-accessible platform
• 2-dimensional (2D) and 3-dimensional (3D) map-based organization and visualization of data
• Efficient organization, analysis, visualization, and analytics of 3D surficial data and change detection from drones and airplanes using lidar and photogrammetry
• Integration of live instrumentation data, status, and thresholds
• Integration of 3D geological information
• Integration of design shells and as-constructed surfaces
• Integration of laboratory and borehole data
• Integration of groundwater information
• Digital field data collection and integration with map-based systems
• Summarized dashboards for high-level reporting and notifications
• Integration of construction data including material information, equipment locations.

The ability to quickly examine and track changes in these data systems through space and time will be an essential part of future four-dimensional (4D) analysis and decision-making systems. Key components of the analysis and decision-making systems will be:

• Contextualization of the information that has been integrated
• Using and applying machine learning algorithms for automation and anomaly detection
• Applying thresholds so that real-time risk assessment can be performed, and notifications can be issued for non-compliance
• Dashboards and system health metrics can be utilized to help optimize performance and minimize risk.

This case study presents the process of turning asset and earth science data into actionable knowledge through the development of a geotechnical digital twin.