CFD validation of scaling rules for reduced-scale field releases of carbon dioxide

Carbon Dioxide-Enhanced Oil Recovery (CO2-EOR) has the potential for well blowouts that could cause casualties and environmental damage. To assess the consequence of such accidents, a reduced-scale field experiment of CO2 release was performed based on scaling rules instead of a full-size field test that was economically infeasible. A series of scaling rules was introduced to upscale the reduced-scale field experiment to full-size. To validate the scaling rules, numerical simulation was carried out based on the k–e turbulence model which proved to be an effective way to predict the concentration field for heavy gas dispersion. For concentration variation, the general tendencies of the simulation and experimental observations remained identical except nearby the jet nozzle where the measured CO2 concentration from the experiment was obviously higher than that in the simulation. Statistical performance indicators were introduced to verify the consistency between the scaled results and the simulated ones, and the results showed that using the scaling rules to scale the field experiment exhibited acceptable accuracy at small flow rates and these scaling rules appear applicable for field experiments of accidental releases.

[1]  Lei Zhu,et al.  A real options–based CCS investment evaluation model: Case study of China’s power generation sector , 2011 .

[2]  M. Subhas Abel,et al.  Heat transfer in a liquid film over an unsteady stretching surface with viscous dissipation in presence of external magnetic field , 2009 .

[3]  J. Colls,et al.  CFD and Gaussian atmospheric dispersion models: A comparison for leak from carbon dioxide transportation and storage facilities , 2008 .

[4]  Mathieu Ichard,et al.  CFD model simulation of dispersion from chlorine railcar releases in industrial and urban areas , 2009 .

[5]  Peter Viebahn,et al.  Integrated assessment of carbon capture and storage (CCS) in the German power sector and comparison with the deployment of renewable energies , 2012 .

[6]  S. M. Tauseef,et al.  CFD-based simulation of dense gas dispersion in presence of obstacles , 2011 .

[7]  Ronald P Koopman,et al.  Lessons learned from LNG safety research. , 2007, Journal of hazardous materials.

[8]  Olav R. Hansen,et al.  FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations , 2004 .

[9]  Kazuhiko Suga,et al.  Computation of high Prandtl number turbulent thermal fields by the analytical wall-function , 2007 .

[10]  Vijay Raghunathan,et al.  LNG decision making approaches compared. , 2006, Journal of hazardous materials.

[11]  Rahul Anantharaman,et al.  Pursuing the pre-combustion CCS route in oil refineries – The impact on fired heaters , 2013 .

[12]  Anay Luketa-Hanlin,et al.  A review of large-scale LNG spills: experiments and modeling. , 2005, Journal of hazardous materials.

[13]  Jung-Ho Wee,et al.  A review on carbon dioxide capture and storage technology using coal fly ash , 2013 .

[14]  Feng Wang,et al.  Experimental and numerical study of the dispersion of carbon dioxide plume. , 2013, Journal of hazardous materials.

[15]  Alireza Jafari,et al.  Effects of mechanical dispersion on CO2 storage in Weyburn CO2-EOR field—Numerical history match and prediction , 2013 .

[16]  B. Launder,et al.  Lectures in mathematical models of turbulence , 1972 .

[17]  Assensi Oliva,et al.  Numerical simulation of wind flow around a parabolic trough solar collector , 2013 .

[18]  Peter Dietrich,et al.  Diagnostic monitoring to identify preferential near-surface structures for CO2 degassing into the atmosphere: Tools for investigations at different spatial scales validated at a natural analogue site , 2013 .

[19]  Jia Li,et al.  Perceptions of opinion leaders towards CCS demonstration projects in China , 2011 .

[20]  J. M. Mallikarjuna,et al.  Energy efficient piston configuration for effective air motion – A CFD study , 2013 .

[21]  Xi Jiang,et al.  A review of physical modelling and numerical simulation of long-term geological storage of CO2 , 2011 .

[22]  L. A. Danao,et al.  An Experimental Investigation into the Influence of Unsteady Wind on the Performance of a Vertical Axis Wind Turbine , 2013 .

[23]  Steven R. Hanna,et al.  Technical Descriptions and User's Guide for the BOOT Statistical Model Evaluation Software Package, Version 2.0 , 2005 .

[24]  Spyros Sklavounos,et al.  Validation of turbulence models in heavy gas dispersion over obstacles. , 2004, Journal of hazardous materials.

[25]  Dailin Wang,et al.  Effects of the earth's rotation on convection: Turbulent statistics, scaling laws and Lagrangian diffusion , 2006 .

[26]  Du Chang.  Research advances of heavy gas dispersion , 2012 .

[27]  Ashok Kumar,et al.  Study of the spread of a cold instantaneous heavy gas release with surface heat transfer and variable entrainment. , 2003, Journal of hazardous materials.

[28]  Vishnu Pareek,et al.  Computational fluid dynamics analysis of liquefied natural gas dispersion for risk assessment strategies , 2013 .

[29]  Jyotirmay Mathur,et al.  ‘Derating Factor’ new concept for evaluating thermal performance of earth air tunnel heat exchanger: A transient CFD analysis , 2013 .

[30]  Jinsong Zhang,et al.  Experimental and numerical studies of the pressure drop in ceramic foams for volumetric solar receiver applications , 2010 .

[31]  Steven R. Hanna,et al.  Use of the Kit Fox field data to analyze dense gas dispersion modeling issues , 2001 .

[32]  Vassili Toropov,et al.  Computational fluid dynamics (CFD) investigation of air flow and temperature distribution in a small scale bread-baking oven , 2012 .

[33]  Mike Harper,et al.  Modelling of discharge and atmospheric dispersion for carbon dioxide releases , 2009 .