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Evolutionary optimisation for CO2 storage design using upscaled models: application on a proximal area of the Forties Fan System in the UK Central North Sea

Babaei, Masoud; Pan, Indranil; Korre, Anna; Shi, Ji Quan; Govindan, Rajesh; Durucan, Sevket; Quinn, Martyn. 2014 Evolutionary optimisation for CO2 storage design using upscaled models: application on a proximal area of the Forties Fan System in the UK Central North Sea. Energy Procedia, 63. 5349-5356. 10.1016/j.egypro.2014.11.567

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Abstract/Summary

Optimisation of injection rates is an important design consideration for meeting operational objectives and ensuring long term geological storage of CO2 in saline aquifers. The optimal design should also take into account the uncertainties associated with the subsurface (e.g., petrophysical attribution and structural relationships). Detailed geological models along with different realisations for handling uncertainties increase the computational overheads, making the optimisation problem intractable. To circumvent this problem, upscaled models can be used to speed up the identification of optimal solutions. Nevertheless, a grid resolution, which does not compromise the accuracy of the optimisation in an upscaled model, must be carefully determined. The methodology described in this paper aims to address this requirement. In this study, a 3D geological model, comprising the main oil reservoirs of the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, was built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution for this area within the UK Central North Sea. Simulation results for single objective optimisation show that an upscaled grid resolution can be identified which is a trade-off between accuracy and computational time. The outlined methodology is generic in nature and can be ported to other similar optimisation problems for CO2 storage.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1016/j.egypro.2014.11.567
Additional Keywords: single objective optimisation; CO2 storage; surrogate modelling; genetic algorithm
NORA Subject Terms: Earth Sciences
Date made live: 06 Mar 2015 14:54 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/509923

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