Development of A Digital Twin for SAGD Pads – Training and Integrity Testing of Data Analytic Models

Data analytics have been used extensively across various industries to predict and propose optimal operation conditions, ultimately improving project efficiencies and reducing execution costs. However, these methods have
not been widely utilized by any SAGD (Steam Assisted Gravity Drainage) operators. There is an opportunity to adapt this new technology to further optimize process operation and reduce costs and enhance ESG performance.
In order to de-risk this emerging technology, this project aims to develop a digital twin of several SAGD pads through numerical simulations. Synthetic data sets produced by these simulations will be utilized to train data-driven models, which will be used for digital pads optimization. The numerical simulator will monitor any short or long-term impacts on SAGD performance due to changes in the pad operation constraints, validating the integrity
of the data-driven models for process optimization at the pad/field level.

Faculty Supervisor:

Juliana Leung

Student:

Partner:

Cenovus Energy Inc

Discipline:

Engineering

Sector:

Mining; Professional, scientific and technical services

University:

University of Alberta

Program: