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Above‐zone pressure monitoring and geomechanical analyses for a field‐scale CO 2 injection project in Cranfield, MS

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  • Seunghee Kim
  • Seyyed Abolfazl Hosseini

Abstract

Pressure and temperature monitoring of an above‐zone monitoring interval (AZMI), as well as of an injection zone (IZ), has been attempted at a field‐scale CO 2 injection site in Cranfield, MS. Recorded pressure data in the AZMI revealed a certain amount of increase with no evidence of direct fluid flow between the IZ and the AZMI. We therefore attempted to interpret the field‐measurement data from a geomechanical perspective. First, we tried an analytical approach that combined Green's functions with a poroelastic theory that is based on Segall's derivation (1992). The analysis was shown to provide fast first‐order and probabilistic estimation. Next, we attempted a numerical simulation in which fully coupled calculation between fluid flow and poroelasticity was implemented. Numerical‐simulation results using COMSOL matched well with the field data at one monitoring location in the AZMI. However, field data differ from those of a numerical simulation at the other monitoring well. We suggest that field measurement at the other location in the AZMI might be disturbed during the pressure monitoring, based on bottom‐hole pressure records in the IZ and thermal signals. Following the numerical simulation, we discuss the effect of single‐phase fluid flow assumption, observations for thermal effect and pore‐pressure–stress coupling, and desirable resolution of pressure gauges for the optimal utilization of above‐zone pressure monitoring.

Suggested Citation

  • Seunghee Kim & Seyyed Abolfazl Hosseini, 2014. "Above‐zone pressure monitoring and geomechanical analyses for a field‐scale CO 2 injection project in Cranfield, MS," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 4(1), pages 81-98, February.
  • Handle: RePEc:wly:greenh:v:4:y:2014:i:1:p:81-98
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    File URL: http://hdl.handle.net/10.1002/ghg.1388
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    1. Jin, Wencheng & Atkinson, Trevor A. & Doughty, Christine & Neupane, Ghanashyam & Spycher, Nicolas & McLing, Travis L. & Dobson, Patrick F. & Smith, Robert & Podgorney, Robert, 2022. "Machine-learning-assisted high-temperature reservoir thermal energy storage optimization," Renewable Energy, Elsevier, vol. 197(C), pages 384-397.

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