The efficient development of condensate gas reservoirs requires accurate fluid phase properties data

among which accurate prediction of dew point pressure is an important issue in the development of condensate gas reservoirs. In order to solve the problem of low accuracy of traditional prediction methods for dew point pressure of condensate gas reservoirs

based on optimization theory and applied statistical analysis

and by fitting measured data

a non-parametric regression model determined by alternating conditional expectation tr

ansformation（ACE） is proposed

and an explicit correlation of dew point pressure with statistical significance is obtained. Based on Pearson correlation analysis

the independent variables of the model are gas reservoir temperature

mole fraction of （C

1

C

2

-C

6

C

7+

）

and molecular weight and relative density of C

7+

. The potential function relation between independent and dependent variables is analyzed by 27 sets of experimental data for published dew point pressure

and 9 groups of measured dew point pressure data of TLM oilfields are predicted. The results show that the model has high precision and good generalization ability. The average absolute relative deviation（

AARD

） of model regression is 2.16 %

and the predicted

AARD

is only 4.8 %. The maximum absolute relative deviation（

ARD

） is 9.21 % and the minimum is 0.34 %. This study provides a reference method for dew point pressure prediction of condensate gas reservoirs.