Determining CO2 storage efficiency within a saline aquifer using reduced complexity models

Abstract

Carbon capture and storage is vital for reducing greenhouse gas emissions and mitigating climate change. Most projects involve the permanent geological storage of CO2 within deep sedimentary rock formations, but accurately constraining storage capacity usually involves detailed and computationally demanding reservoir modeling and simulation. Efficiency factors can also be used but these often lead to capacity overestimations. To address this, a workflow is proposed harnessing various existing, reduced complexity models that account for the surface topography and dynamic fluid behavior in a computationally efficient manner. This workflow was tested in an area of the Malay Basin mapped from three-dimensional seismic data but with illustrative reservoir parameters. A static analysis was first undertaken using algorithms within MRST-co2lab. Structural traps, spill paths and spill regions were identified using the reservoir topography. This provided initial indications into optimal well placement and led to refinement of the total capacity of the area into the capacity available within structural traps. This was followed with a dynamic analysis, also within MRST-co2lab, using computationally efficient Vertical Equilibrium models. Hundreds of simulations were undertaken and the optimal well placement was determined based on the maximum storage efficiency achieved. The results indicated that the amount that can be contained within this area is 15 times less than equivalent predictions using static storage efficiency factors. The advantage of such a light approach is that sensitivity and uncertainty analysis can be carried out at speed, before targeting certain parameters/areas for more detailed study.

Document Type: Original article

Cite as: de Jonge-Anderson, I., Ramachandran, H., Nicholson, U., Geiger, S., Widyanita, A., Doster, F. Determining CO2 storage efficiency within a saline aquifer using reduced complexity models. Advances in Geo-Energy Research, 2024, 13(1): 22-31. https://doi.org/10.46690/ager.2024.07.04

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