Project Overview

This project aims to build a Smart Bottomhole Pressure (BHP) Module to support safe and efficient CO₂ injection at the Aquistore CCS site, Saskatchewan. By applying machine learning to historical and live CCS data, the system predicts bottomhole pressure using inputs like Aquistore's wellhead pressure, flow rate, and temperature, offering reliable control even when direct measurements are unavailable. The module will be tested with real-world CCS data and integrated into the Aquistore’s control system to enable automated, adaptive injection management. Its flexible ML-architecture allows for application across other subsurface energy technologies, including geothermal and hydrogen storage. Ultimately, the project advances digital monitoring and control in current and future energy systems, aligning with Canada’s Net Zero goals and strengthening the performance of AI-driven subsurface energy operations.