Application of Data Analytics for Warm Applied Solvent Process 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 reducing GHG emissions associated with bitumen extraction processes. Although the recent developments in digital oilfield technologies have enabled real-time surveillance of downhole operating conditions and production data, analyzing a 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. Machine-/deep-learning approaches will be integrated to establish relationships between reservoir characteristics, operational parameters, and production responses. The project outcomes will offer important insights into how to optimize solvent operations.

Faculty Supervisor:

Juliana Leung

Student:

Partner:

ConocoPhillips Canada

Discipline:

Engineering

Sector:

Mining

University:

University of Alberta

Program: