In f0nzie/zFactor: Calculate the Compressibility Factor 'z' for Hydrocarbon Gases

knitr::opts_chunk$set(echo=T, comment=NA, error=T, warning=F, message = F, fig.align = 'center', results="hold")

The DAK correlation

The work by P.M. Dranchuk and J.H. Abou-Kassem was looking to examine z outside the regions established by the Standing-Katz chart. They used as a basis the generalized Starling equation of state. They provided the code in FORTRAN. See [@Dranchuk1975]

Get z at selected Ppr and Tpr

Use the the correlation to calculate z and from Standing-Katz chart obtain z a digitized point at the given Tpr and Ppr.

# get a z value
library(zFactor)

ppr <- 1.5
tpr <- 2.0

z.calc <- z.DranchukAbuKassem(pres.pr = ppr, temp.pr = tpr)

# get a z value from the SK chart at the same Ppr and Tpr

z.chart <- getStandingKatzMatrix(tpr_vector = tpr, 
                      pprRange = "lp")[1, as.character(ppr)]


# calculate the APE
ape <- abs((z.calc - z.chart) / z.chart) * 100

df <- as.data.frame(list(Ppr = ppr,  z.calc =z.calc, z.chart = z.chart, ape=ape))
rownames(df) <- tpr
df
# HY = 0.9580002; # DAK = 0.9551087

Get z at selected Ppr and Tpr=1.1

From the Standing-Katz chart we read z at a digitized point:

library(zFactor)

ppr <- 1.5
tpr <- 1.1

z.calc <- z.DranchukAbuKassem(pres.pr = ppr, temp.pr = tpr)

# From the Standing-Katz chart we obtain a digitized point:
z.chart <- getStandingKatzMatrix(tpr_vector = tpr, 
                      pprRange = "lp")[1, as.character(ppr)]

# calculate the APE (Average Percentage Error)
ape <- abs((z.calc - z.chart) / z.chart) * 100

df <- as.data.frame(list(Ppr = ppr,  z.calc =z.calc, z.chart = z.chart, ape=ape))
rownames(df) <- tpr
df

At lower Tpr we can see that there is some difference between the values of z from the DAK calculation and the z value read from the Standing-Katz chart. See the APE.

Get values of z for combinations of Ppr and Tpr

In this example we provide vectors instead of a single point. With the same ppr and tpr vectors that we use for the correlation, we do the same for the Standing-Katz chart. We want to compare both and find the absolute percentage error or APE.

# test DAK with 1st-derivative using the values from paper 
ppr <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5) 
tpr <- c(1.05, 1.1, 1.7, 2) 

# calculate using the correlation
z.calc <- z.DranchukAbuKassem(ppr, tpr)

# With the same ppr and tpr vector, we do the same for the Standing-Katz chart
z.chart <- getStandingKatzMatrix(ppr_vector = ppr, tpr_vector = tpr)
ape <- abs((z.calc - z.chart) / z.chart) * 100

# calculate the APE
cat("z.correlation \n"); print(z.calc)
cat("\n z.chart \n"); print(z.chart)
cat("\n APE \n"); print(ape)

You can see errors of r round(ape["1.05", "1.5"],2)% and r round(ape["1.05", "2.5"],2)% in the isotherm Tpr=1.05 at Ppr 0.5 and 2.5. Other errors, greater than one, can also be found at the isotherm Tpr=1.1: r round(ape["1.1", "2.5"],2)%. Then, the rest of the Tpr curves are fine.

Analyze the error at the isotherms

Applying the function summary over the transpose of the matrix:

sum_t_ape <- summary(t(ape))
sum_t_ape

We can see that the errors in z are considerable with a r sum_t_ape[1,1]% and r sum_t_ape[6,1]% for Tpr=1.05, and a r sum_t_ape[1,2]% and r sum_t_ape[6,2]% for Tpr=1.10. We will explore later a comparative tile chart where we confirm these early calculations.

Analyze the error for greater values of Tpr

library(zFactor)
# enter vectors for Tpr and Ppr
tpr2 <- c(1.2, 1.3, 1.5, 2.0, 3.0) 
ppr2 <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5) 

# get z values from the SK chart
z.chart <- getStandingKatzMatrix(ppr_vector = ppr2, tpr_vector = tpr2, pprRange = "lp")

# We do the same with the HY correlation:
# calculate z values at lower values of Tpr
z.calc <- z.DranchukAbuKassem(pres.pr = ppr2, temp.pr = tpr2) 
ape <- abs((z.calc - z.chart) / z.chart) * 100

# calculate the APE
cat("z.correlation \n"); print(z.calc)
cat("\n z.chart \n"); print(z.chart)
cat("\n APE \n"); print(ape)

We observe that at Tpr above or equal to 1.2 the DAK correlation behaves very well.

Analyze the error at the isotherms

Applying the function summary over the transpose of the matrix to observe the error of the correlation at each isotherm.

sum_t_ape <- summary(t(ape))
sum_t_ape
 # Hall-Yarborough
 #      1.2               1.3              1.5               2         
 # Min.   :0.05224   Min.   :0.1105   Min.   :0.1021   Min.   :0.0809  
 # 1st Qu.:0.09039   1st Qu.:0.2080   1st Qu.:0.1623   1st Qu.:0.1814  
 # Median :0.28057   Median :0.3181   Median :0.1892   Median :0.1975  
 # Mean   :0.30122   Mean   :0.3899   Mean   :0.2597   Mean   :0.2284  
 # 3rd Qu.:0.51710   3rd Qu.:0.5355   3rd Qu.:0.3533   3rd Qu.:0.2627  
 # Max.   :0.57098   Max.   :0.8131   Max.   :0.5162   Max.   :0.4338  
 #       3          
 # Min.   :0.09128  
 # 1st Qu.:0.17466  
 # Median :0.35252  
 # Mean   :0.34820  
 # 3rd Qu.:0.52184  
 # Max.   :0.59923  

We can see that the errors in z with DAK are far lower than Hall-Yarborough with a r sum_t_ape[1,1]% and r sum_t_ape[6,1]% for Tpr=1.2, and a r sum_t_ape[1,2]% and r sum_t_ape[6,2]% for Tpr=1.3.

Prepare to plot SK vs DAK correlation

The error bars represent the difference between the calculated values by the Dranchuk-AbouKassem corrrelation and teh values of z read from the Standing-Katz chart.

library(zFactor)
library(tibble)
library(ggplot2)

tpr2 <- c(1.05, 1.1, 1.2, 1.3) 
ppr2 <- c(0.5, 1.0, 1.5, 2, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5) 

sk_dak_2 <- createTidyFromMatrix(ppr2, tpr2, correlation = "DAK")
as_tibble(sk_dak_2)


p <- ggplot(sk_dak_2, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
    geom_line() +
    geom_point() +
    geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.4,
                  position=position_dodge(0.05))
print(p)

We observe that with Dranchuk-AbouKassem there are still errors or differences between the z values in the Standing-Katz chart and the values obtained from the correlation but they are not as bad as in the HY correlation.

Analysis at the lowest Tpr

This is the isotherm where we see the greatest error.

library(zFactor)

sk_dak_3 <- sk_dak_2[sk_dak_2$Tpr==1.05,]
sk_dak_3

p <- ggplot(sk_dak_3, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
    geom_line() +
    geom_point() +
    geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.2,
                  position=position_dodge(0.05))
print(p)

Analyzing performance of the DAK correlation for all the Tpr curves

In this last example, we compare the values of z at all the isotherms. We use the function getCurvesDigitized to obtain all the isotherms or Tpr curves in the Standing-Katz chart that have been digitized. The next function createTidyFromMatrix calculate z using the correlation and prepares a tidy dataset ready to plot.

library(ggplot2)
library(tibble)

# get all `lp` Tpr curves
tpr_all <- getStandingKatzTpr(pprRange = "lp")
ppr <- c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5) 
sk_corr_all <- createTidyFromMatrix(ppr, tpr_all, correlation = "DAK")
as_tibble(sk_corr_all)

p <- ggplot(sk_corr_all, aes(x=Ppr, y=z.calc, group=Tpr, color=Tpr)) +
    geom_line() +
    geom_point() +
    geom_errorbar(aes(ymin=z.calc-dif, ymax=z.calc+dif), width=.4,
                  position=position_dodge(0.05))
print(p)

The greatest errors are localized in two of the Tpr curves: at 1.05 and 1.1.

Range of applicability of the correlation

# MSE: Mean Squared Error
# RMSE: Root Mean Squared Error
# RSS: residual sum of square
# ARE:  Average Relative Error, %
# AARE: Average Absolute Relative Error, %
library(dplyr)
grouped <- group_by(sk_corr_all, Tpr, Ppr)

smry_tpr_ppr <- summarise(grouped, 
          RMSE= sqrt(mean((z.chart-z.calc)^2)), 
          MPE = sum((z.calc - z.chart) / z.chart) * 100 / n(),
          MAPE = sum(abs((z.calc - z.chart) / z.chart)) * 100 / n(), 
          MSE = sum((z.calc - z.chart)^2) / n(), 
          RSS = sum((z.calc - z.chart)^2),
          MAE = sum(abs(z.calc - z.chart)) / n(),
          RMLSE = sqrt(1/n()*sum((log(z.calc +1)-log(z.chart +1))^2))
          )

ggplot(smry_tpr_ppr, aes(Ppr, Tpr)) + 
    geom_tile(data=smry_tpr_ppr, aes(fill=MAPE), color="white") +
    scale_fill_gradient2(low="blue", high="red", mid="yellow", na.value = "pink",
                         midpoint=12.5, limit=c(0, 25), name="MAPE") + 
    theme(axis.text.x = element_text(angle=45, vjust=1, size=11, hjust=1)) + 
    coord_equal() +
    ggtitle("Dranchuk-AbouKassem", subtitle = "DAK")

The greatest errors are localized in two of the Tpr curves: 1.05 and barely at 1.1. But the errors are smaller than that we saw in the HY plot.

Plotting the Tpr and Ppr values that show more error

The MAPE (mean average percentage error) gradient bar indicates that the more red the square is, the more error there is.

library(dplyr)

sk_corr_all %>%
    filter(Tpr %in% c("1.05", "1.1")) %>%
    ggplot(aes(x = z.chart, y=z.calc, group = Tpr, color = Tpr)) +
    geom_point(size = 3) +
    geom_line(aes(x = z.chart, y = z.chart), color = "black") +
    facet_grid(. ~ Tpr) +
    geom_errorbar(aes(ymin=z.calc-abs(dif), ymax=z.calc+abs(dif)), 
                  position=position_dodge(0.5))

Looking numerically at the errors

Finally, the dataframe with the calculated errors between the z from the correlation and the z read from the chart:

as_tibble(smry_tpr_ppr)

References

f0nzie/zFactor documentation built on Aug. 2, 2019, 1:42 a.m.