Accelerating Results in Carbon Storage Studies Using an Integrated and Automated Approach

Abstract

Geological storage of carbon dioxide (CO2) is considered an enabler of different business models aligned with decarbonization of the energy market. Partnerships are forming worldwide to develop large-scale carbon capture, utilization and storage (CCUS) projects: 2021 was a record year for project pipeline growth for these types of projects. This growth will result in an increasing need for subsurface technologies that can unlock fast time-to-results throughout all the steps of the project, from site selection to storage monitoring. At an early stage of a carbon storage project, a thorough verification of the technical and economic viability of the project is critical. The high degree of geological uncertainties in the case of storage in under-explored saline aquifers can make this step challenging. As the project progresses, fast assimilation of monitoring data to prove conformance and update predictions of the storage complex performance is key. An advanced technology from AspenTech can serve as a catalyst for efficient carbon storage studies. It tightly integrates static and dynamic domains and offers the propagation of uncertainties, from seismic characterization through to geological modelling and simulation. Using results from a large set of models increases predictability of the subsurface and enables more efficient analysis of uncertainty in predicted storage capacity and containment. This fully automated workflow can be run at will with new data, drastically reducing the time needed by carbon storage teams to update the model and the predictions as monitoring data is acquired.