TY - GEN
T1 - On the development of a relative permeability equation of state
AU - Purswani, P.
AU - Tawfik, M. S.
AU - Karpyn, Z. T.
AU - Johns, R. T.
N1 - Funding Information:
The authors would like to thank ADNOC, Energi Simulation, OMV, Shell and KOC for their partial financial support of this research through the Enhanced Oil Recovery JIP at Pennsylvania State University at University Park, Pennsylvania. The authors would also like to acknowledge Dr. Ryan T. Armstrong for providing the simulation dataset used in this study.
Publisher Copyright:
© 2018 European Association of Geoscientists and Engineers EAGE. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Standard compositional simulators use composition-dependent cubic equations-of-state (EoS), but saturation-dependent relative permeability (kr). This discrepancy causes discontinuities, increasing computational time and reduced accuracy. To rectify this problem, kr has been recently defined as a state function, so that it becomes compositional dependent. Such a form of the kr EoS can significantly improve the convergence in compositional simulation, in that time step sizes are near the IMPEC stability limit and flash calculation convergence is improved. This paper revisits the development of kr EoS by defining relevant state variables and deriving functional forms of the state function via a methodical approach. The state variables include phase saturation, phase connectivity, wettability, capillary number, and pore topology. The developed EoS is constrained to physical boundary conditions. The model coefficients are estimated through linear regression on data collected from a pore-scale simulation study that estimates kr based on micro-CT image analysis. The results show that a simple quadratic expression gives an excellent match with simulation measurements from the literature. The goodness of fit (R2) value is 0.97 for kr at variable phase saturation and phase connectivity, and constant wettability, pore structure, and capillary number (∼10-4). The quadratic response for kr also shows excellent predictive capabilities.
AB - Standard compositional simulators use composition-dependent cubic equations-of-state (EoS), but saturation-dependent relative permeability (kr). This discrepancy causes discontinuities, increasing computational time and reduced accuracy. To rectify this problem, kr has been recently defined as a state function, so that it becomes compositional dependent. Such a form of the kr EoS can significantly improve the convergence in compositional simulation, in that time step sizes are near the IMPEC stability limit and flash calculation convergence is improved. This paper revisits the development of kr EoS by defining relevant state variables and deriving functional forms of the state function via a methodical approach. The state variables include phase saturation, phase connectivity, wettability, capillary number, and pore topology. The developed EoS is constrained to physical boundary conditions. The model coefficients are estimated through linear regression on data collected from a pore-scale simulation study that estimates kr based on micro-CT image analysis. The results show that a simple quadratic expression gives an excellent match with simulation measurements from the literature. The goodness of fit (R2) value is 0.97 for kr at variable phase saturation and phase connectivity, and constant wettability, pore structure, and capillary number (∼10-4). The quadratic response for kr also shows excellent predictive capabilities.
UR - http://www.scopus.com/inward/record.url?scp=85087555379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087555379&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.201802125
DO - 10.3997/2214-4609.201802125
M3 - Conference contribution
AN - SCOPUS:85087555379
SN - 9789462822603
T3 - 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018
BT - 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018
PB - European Association of Geoscientists and Engineers, EAGE
T2 - 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018
Y2 - 3 September 2018 through 6 September 2018
ER -