Critical evaluation of empirical gas condensate correlations

Abstract Compositionally sensitive retrograde fluids or gas condensates are particularly challenging from the standpoint of phase behavior and fluid property measurement as well as modeling. The problem is even more pronounced when dealing with the two extreme types of fluids, i.e., lean (insignificant amount of retrograde liquid) and rich (significant amount of liquid drop out, few psi below the saturation pressure). Given these complications, many researchers have proposed empirical correlations that make use of either the field data such as gas to condensate ratio (GCR) or laboratory data such as compositions, plus fraction characteristics to obtain PVT properties. In this work, using the reported experimental data of 81 gas condensate fluids in the open literature, we have critically evaluated four such empirical correlations. The evaluation focuses on the relationship between C7+ (heptanes plus) mole% and initial producing GCR; dew point prediction; molecular weight prediction; and maximum retrograde condensate (MRC). The C7+ mole% predicted from GCR showed deviations as high as 45% or the differences between the predicted GCR's from the two evaluated correlations were as high as 5000 scf/STB; dew point predictions were over and under estimated by as much as 2000 psia; overall fluid molecular weight differences were as high as 7 units; and MRC overestimated by as much as 115%. Based on this critical evaluation we demonstrate that such type of simplistic empirical approach lacks the accuracy and reliability of obtaining PVT properties that is crucial in the efficient management of gas condensate reservoirs.

[1]  Freddy Crespo,et al.  A New Multi-Sample EOS Model for the Gas Condensate Phase Behavior Analysis , 2011 .

[2]  M. A. Al-Marhoun,et al.  New Correlations for Dew-Point Pressure for Gas Condensate , 2011 .

[3]  Guangjin Chen,et al.  Experiments and Modeling of Volumetric Properties and Phase Behavior for Condensate Gas under Ultra-High-Pressure Conditions , 2012 .

[4]  D. Katz,et al.  Reservoir depletion calculations for gas condensates using extended analyses in the peng‐robinson equation of state , 1978 .

[5]  Ali Abdallah Al-Meshari New strategic method to tune equation-of-state to match experimental data for compositional simulation , 2005 .

[6]  Farhad Gharagheizi,et al.  Toward a predictive model for estimating dew point pressure in gas condensate systems , 2013 .

[8]  K. S. Pedersen,et al.  Characterization of gas condensate mixtures , 1988 .

[9]  K. A. Fattah Gas–oil ratio correlation (Rs) for gas condensate using genetic programming , 2014, Journal of Petroleum Exploration and Production Technology.

[10]  William D. McCain Heavy components control reservoir fluid behavior , 1994 .

[11]  B. H. Sage,et al.  Volumetric Behavior of Oil and Gas From a Louisiana Field I , 1950 .

[12]  Karen Schou Pedersen,et al.  Phase Behavior of Petroleum Reservoir Fluids , 2006 .

[13]  Harvey T. Kennedy,et al.  A Correlation of Dewpoint Pressure With Fluid Composition and Temperature , 1967 .

[14]  M. F. Hawkins,et al.  Applied Petroleum Reservoir Engineering , 1991 .

[15]  Feridun Esmaeilzadeh,et al.  Prediction of gas condensate properties by Esmaeilzadeh–Roshanfekr equation of state , 2007 .

[16]  A. Danesh PVT and Phase Behaviour of Petroleum Reservoir Fluids , 1998 .

[17]  Karen Schou Pedersen,et al.  Properties of oils and natural gases , 2016 .

[18]  D. Peng,et al.  A New Two-Constant Equation of State , 1976 .

[19]  G. Soave Equilibrium constants from a modified Redlich-Kwong equation of state , 1972 .

[20]  B. Dindoruk Development of a Correlation for the Estimation of Condensate to Gas Ratio (CGR) and Other Key Gas Properties From Density Data , 2012 .

[21]  Phase behavior and compressibility factor of two China gas condensate samples at pressures up to 95 MPa , 2013 .

[22]  P. L. Moses,et al.  Engineering Applications of Phase Behavior of Crude Oil and Condensate Systems (includes associated papers 16046, 16177, 16390, 16440, 19214 and 19893 ) , 1986 .

[23]  A. Vatani,et al.  Gas-condensate production improvement using wettability alteration: A giant gas condensate field case study , 2014 .

[24]  Adriana Patricia Ovalle,et al.  Tools To Manage Gas/Condensate Reservoirs; Novel Fluid-Property Correlations on the Basis of Commonly Available Field Data , 2007 .

[25]  J. E. Paredes,et al.  Correlations to Estimate Key Gas Condensate Properties through Field Measurement of Gas Condensate Ratio , 2014 .

[26]  B. Dabir,et al.  Application of a smart mesh generation technique in gas condensate reservoirs: Auto-tune PVT package for property estimation , 2015 .