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Modelling start-up injection of CO2 into highly-depleted gas fields

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  • Sacconi, Andrea
  • Mahgerefteh, Haroun

Abstract

The development and verification of a homogeneous relaxation model for simulating the highly transient flow phenomena taking place during the start-up injection of CO2 into deep highly depleted gas fields is presented. The constituent mass, momentum, and energy conservation equations, incorporating a relaxation time to account for non-equilibrium effects, are solved numerically for single and two-phase flows along the steel lined injection well leading to the storage reservoir. Wall friction, gravitational field effects and heat transfer between the expanding fluid and the outer well layers are taken into account as source terms in the conservation equations. At the well inlet, the opening of the upstream flow regulator valve is modelled as an isenthalpic expansion process; whilst at the well outlet, a formation-specific pressure-mass flow rate correlation is adopted to characterise the storage site injectivity. The testing of the model is based on its application to CO2 injection into the depleted Golden Eye Reservoir in the North Sea for which the required design and operational data are publically available. Three injection scenarios involving a rapid, medium and slow linear ramping up of the injected CO2 flow rate to the peak nominal value of 33.5 kg/s are simulated. In each case, the predicted pressure and temperature transients at the top and bottom of the well are employed to ascertain the risks of well-bore thermal shocking, and interstitial ice or CO2 hydrate formation leading to its blockage due to the rapid expansion cooling of the CO2. The results demonstrate the efficacy of the proposed model as a tool for the development of optimal injection strategies and best-practice guidelines for the minimisation of the risks associated with the start-up injection of CO2 into depleted gas fields.

Suggested Citation

  • Sacconi, Andrea & Mahgerefteh, Haroun, 2020. "Modelling start-up injection of CO2 into highly-depleted gas fields," Energy, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s036054421932225x
    DOI: 10.1016/j.energy.2019.116530
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    Cited by:

    1. Kim, Sungil & Kim, Tea-Woo & Hong, Yongjun & Kim, Juhyun & Jeong, Hoonyoung, 2024. "Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning," Energy, Elsevier, vol. 290(C).

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