Digital Synergy in Carbon Storage: Real-Time Data and AI-Enhanced Modelling for Dynamic CCS Optimization

As the urgency to combat climate change intensifies, carbon capture and storage (CCS) has emerged as a vital technology for reducing Carbon emissions. The success of CCS projects hinges on accurate and reliable modelling tools that simulate the behavior of Carbon in subsurface reservoirs, ensuring safe and efficient storage operations. Recent advancements in digital technologies are enhancing these modelling capabilities, enabling real-time data integration and automation to optimize carbon storage processes. The abstract of this paper examines how innovative digital workflowsthat combine modelling tools with real-time subsurface dataare transforming CCS operations. Traditionally, reservoir and well modelling tools have been essential for feasibility studies, helping define key parameters such as injection pressure, temperature, and Carbon volume to be maintained for safe CCS operations. However, to ensure these parameters remain optimal throughout the lifetime of a CCS project, it is critical to continuously update the models based on real-time data. This integration is achieved through a seamless connection between the modelling tools and real-time sensor data from subsurface environments. Sensors monitor key variables like pressure, temperature, and CO2 plume movement, feeding this data into the models to allow for dynamic adjustments.The modelling system incorporates machine learning (ML) and artificial intelligence (AI) algorithms that can continuously update and refine its predictions as new data streams in. These ML and AI-powered workflows ensure that injection parameters are consistently aligned with optimal conditions, maintaining safety and efficiency. The automation of process parameter updates, driven by real-time sensor data, reduces human intervention and minimizes the risk of operational errors. Moreover, automated alert generation based on modelling outcomes allows for rapid detection of potential riskssuch as changes in injection pressures or temperature deviations—that could threaten the integrity of the storage site. Furthermore, the integration of digital twins, which simulate the physical behaviour of the storage site, provides a real-time, virtual representation of the subsurface environment. This allows operators to visualize the ongoing conditions of the site and make informed decisions promptly. By connecting these digital monitoring solutions with modelling tools, CCS operations are not only safer but also more responsive, with the ability to adapt quickly to changing conditions.