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). Although the recent developments in digital oilfield technologies have enabled real-time surveillance of downhole operating conditions and production data, analyzing the large amount of collected data remains challenging without customized data analytics tools.
The industry partner has gathered a comprehensive data set in a pilot study. Data-driven approaches will be integrated to establish relationships between the collected data and relevant reservoir (operational) parameters. The project outcomes will offer important insights on how to optimize solvent operations. This knowledge is crucial for field-scale operations design.

Faculty Supervisor:

Juliana Leung

Student:

Seyide Hunyinbo

Partner:

Cenovus Energy Inc.

Discipline:

Engineering - civil

Sector:

Mining and quarrying

University:

University of Alberta