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.

Carbon capture and storage (CCS) is vital to reducing greenhouse gas emissions and mitigating climate change. Most CCS projects rely on the permanent geological storage of CO2 within deep sedimentary rock formations, but accurately constraining the capacity of these reservoirs usually involves detailed and computationally demanding reservoir modelling and simulation of the pressure evolution and CO2 plume migration. In the absence of this, efficiency factors are often used within volumetric capacity estimates, but this often results in overestimations of storage capacity. As an alternative, we propose a workflow harnessing various, existing, reduced complexity models that account for the surface topography and dynamic fluid behaviour in a computationally efficient manner. We first undertook a static analysis using algorithms available within MRST-co2lab. The reservoir topography is used to determine the locations of structural traps, the trapping routes that link them and downdip filling areas that feed a given trap. This analysis provides indications of the optimal well placement and helps us refine the total capacity of the area into the capacity available just from structural trapping. We followed this with a dynamic analysis, also within MRST-co2lab, using computationally efficient Vertical Equilibrium models. This efficiency allowed us to performing hundreds of simulations and use these results to map storage efficiency and determine the optimal well placement where efficiency is greatest. We tested this workflow within an area of the Malay Basin with illustrative reservoir parameters and estimated storage efficiency, capacity and the optimal well placement within the area without performing any full-physics simulations. The results from VE modelling indicate that the amount that can be contained within this area is 15 times less than the 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.