zFactor.DL

The goal of zFactor.DL is to create a correlation using neural networks.

Dataset

These observations have been taken from a digital scan of the 1942 Standing-Katz chart for hydrocarbon gases.

Original Standing-Katz dataset

The original data was gracefully provided by the authors of the paper [Kamyab, M., et al., Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases, J. Pet. Sci.Eng. (2010), doi:10.1016/j.petrol.2010.07.006] (http://www.sciencedirect.com/science/article/pii/S0920410510001427?via%3Dihub).

The original dataset, for Ppr from 0 to 15, is an Excel file named SK_data.xls with Ppr, Tpr and z in columnar format with 2853 rows and 60 columns. It is under the folder [./inst/extdata] (https://github.com/f0nzie/zFactor.DL/tree/master/inst/extdata) in this repository. They are three columns or set per Tpr curve.

row    Ppr.0 Tpr.0 z.0    Ppr.1 Tpr.1 z.1  ...    Ppr.19 Tpr.19 z.19
 1
 . 
 .
 .
2853

Tidy Standing-Katz dataset

The tidy dataset contains 57964 observations of compressibility factors at different Pseudo-reduced Temperatures and Pseudo-reduced pressures. The original data was converted to tidy format using R. Refer to notebook 01-read_raw_data.Rmd for step by step process.

The original file has been converted to a tidy format file named tidy_SK.csv with 57,060 observations and 4 variables with the column names Tpr, Ppr, z and set number. This file is under the same folder [./inst/extdata] (https://github.com/f0nzie/zFactor.DL/tree/master/inst/extdata).

The tidy dataset format looks like this, 57,060 observations (rows) and 4 variables (columns):

row     Tpr   Ppr   z   set
   1       
   2
   . 
   .
   .
57060

data("sk_tidy")

dim(sk_tidy)
#> [1] 57964     5

names(sk_tidy)
#> [1] "Tpr"   "Ppr"   "z"     "set"   "range"

min(sk_tidy$Tpr)
#> [1] 1.05
max(sk_tidy$Tpr)
#> [1] 3

min(sk_tidy$Ppr)
#> [1] 0
max(sk_tidy$Ppr)
#> [1] 30

min(sk_tidy$z)
#> [1] 0.251754
max(sk_tidy$z)
#> [1] 2.66

summary(sk_tidy)
#>       Tpr             Ppr               z               set        
#>  Min.   :1.050   Min.   : 0.000   Min.   :0.2518   Min.   : 0.000  
#>  1st Qu.:1.300   1st Qu.: 2.558   1st Qu.:0.8477   1st Qu.: 4.000  
#>  Median :1.600   Median : 5.680   Median :0.9921   Median : 9.000  
#>  Mean   :1.743   Mean   : 7.008   Mean   :1.0335   Mean   : 9.406  
#>  3rd Qu.:2.200   3rd Qu.:11.853   3rd Qu.:1.2621   3rd Qu.:14.000  
#>  Max.   :3.000   Max.   :30.000   Max.   :2.6600   Max.   :19.000  
#>     range          
#>  Length:57964      
#>  Class :character  
#>  Mode  :character  
#>                    
#>                    
#> 

data("sk_tidy_lp")
summary(sk_tidy_lp)
#>       Tpr             Ppr               z              set           
#>  Min.   :1.050   Min.   : 0.000   Min.   :0.2518   Length:57060      
#>  1st Qu.:1.288   1st Qu.: 2.525   1st Qu.:0.8439   Class :character  
#>  Median :1.550   Median : 5.577   Median :0.9882   Mode  :character  
#>  Mean   :1.738   Mean   : 6.754   Mean   :1.0214                     
#>  3rd Qu.:2.050   3rd Qu.:11.647   3rd Qu.:1.2509                     
#>  Max.   :3.000   Max.   :15.001   Max.   :1.7536

data("sk_tidy_hp")
summary(sk_tidy_hp)
#>       Tpr            Ppr             z             set           
#>  Min.   :1.40   Min.   :16.0   Min.   :1.363   Length:904        
#>  1st Qu.:1.75   1st Qu.:19.5   1st Qu.:1.585   Class :character  
#>  Median :2.10   Median :23.0   Median :1.749   Mode  :character  
#>  Mean   :2.10   Mean   :23.0   Mean   :1.792                     
#>  3rd Qu.:2.45   3rd Qu.:26.5   3rd Qu.:1.945                     
#>  Max.   :2.80   Max.   :30.0   Max.   :2.660