m. isazadeh

In the present study, the information of 31 synoptic weather stations in the period of 1987-2010 were used to delineate Iran’s precipitation regions. For this purpose, each month’s data were standardized and written in the (n*m) matrix, where n is the number of stations (31) and m is the number of months (12). Principal component analysis was conducted on the mentioned matrix. The main components were determined according to the criterion of having an eigen value greater than one. Then principal components scores were calculated for the selected components. These valeus were used as an input for Ward method of cluster analysis. Then according to the cluster diagram, precipitation climates of the country were identified. The Procrustes analysis (PA) was used to answer the question of which stations could be considered as the indicator of rainfall climates? Furthermore, from this method those months, which their precipitation can be recognized as an indicator of annual rainfall, were selected. Results showed that the first three components incorporated more than 97% of total variance. Based on the selected components the six distinct precipitation regions were distinguished across the country. Furthermore, PA results indicated that the precipitation of May, August and December approximately had whole of the annual precipitation information. Moreover, it was found that seven stations located in different points of the country namely Zahedan, Tehran, Urmia, Ilam, Yasooj, Gorgan and Rasht could be considered as the indicator stations. These stations incorporated over 87 percent of total variance of data of all selected stations in the country.

Compressibility factor (z-factor) values of natural gases are necessary in most petroleum engineering calculations. Necessity arises when there are no available experimental data for the required composition, pressure and temperature conditions. One of the most common methods of calculating z-factor values is empirical correlation.Firstly, a new correlation based on the famous Standing-Katz (S-K) Chart is presented to predict z-factor values. The advantage of this correlation is that it is explicit in Z and thus does not require an iterative solution as is required by other methods, so it can be utilized at PLC (Programmable Logic Controller) system simply. Secondly, the comparison between new one and other correlations is carried out and the results indicate the superiority of the new correlation over the other correlations used to calculate z-factor.