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

Masoud Babaei, Indranil Pan, Anna Korre, Ji Quan Shi, Rajesh Govindan, Sevket Durucan*, Martyn Quinn

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)5349-5356
Number of pages8
JournalEnergy Procedia
Volume63
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event12th International Conference on Greenhouse Gas Control Technologies, GHGT 2014 - Austin, United States
Duration: 5 Oct 20149 Oct 2014

Keywords

  • CO storage
  • Genetic algorithm
  • Single objective optimisation
  • Surrogate modelling
  • Upscaling

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