Effective monitoring of CO₂ injection and storage is critical for the success of CCUS projects. Traditional seismic attributes often lack the sensitivity required to detect subtle changes associated with CO₂ movement, making it challenging to ensure containment and identify potential leaks. Additionally, conventional workflows for subsurface characterisation can be time-consuming and resource-intensive, hindering the timely assessment of storage sites and the implementation of necessary interventions.

The Smart Tools AI/ML Guided Technology addresses these challenges by utilising advanced algorithms to analyse seismic data and extract sensitive attributes indicative of CO₂ presence and movement. This approach enables the rapid identification of suitable storage sites, accurate assessment of storage capacity, and effective monitoring of CO₂ injection and containment. By automating and optimising the analysis process, the technology significantly reduces the time and cost associated with subsurface evaluation, facilitating more efficient and reliable CCUS operations.

The innovation lies in the application of AI and ML to seismic data analysis, allowing for the extraction of nuanced information that traditional methods may overlook. The technology's ability to detect subtle changes in subsurface properties enhances the monitoring of CO₂ injection and storage, providing early warning of potential issues. Its streamlined workflow and automated processes represent a significant advancement in subsurface characterisation, enabling more proactive and informed decision-making in CCUS projects.