Plot Standing-Katz low-pressure `lp` charts

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

Plot a Standing-Katz curve

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

If we invoke the function getStandingKatzCurve without any parameters, by defult it will use a Tpr=1.3 for the low-pressure set of Tpr curves and plot it.

library(zFactor)
sk <- getStandingKatzCurve()

If we want to specify a Tpr curve:

library(zFactor)
getStandingKatzCurve(tpr = 1.05, toSave = FALSE, toView = FALSE)

Or for a couple of Tpr curves, we specify a vector with 2 or more components:

library(zFactor)
tpr_vec <- c(1.05, 1.1)
result <- getStandingKatzCurve(tpr = tpr_vec, toSave = FALSE, toView = FALSE)

A dataframe for each Tpr dataset

The returning object of getStandingKatzCurve is a list of dataframes, which contains a dataframe for each of the Tpr curves specified:

class(result)      # class of the object `result` is a list of dataframes
names(result)      # name of each dataframe within the list

For Tpr = 1.05:

library(tibble)
as_tibble(result[["1.05"]])

And, for Tpr = 1.1:

as_tibble(result[["1.1"]])

Plot all the lp Standing-Katz curves

Or we can plot all the lp Standing-Katz curves:

# view all the `Tpr` SK individual charts
tpr_vec <- getStandingKatzTpr(pprRange = "lp")
result <- getStandingKatzCurve(tpr_vec, toSave = FALSE,  toView = FALSE)

Changing the scale for all the plots

The plotting function in getStandingKatzCurve has a default value for the Y scale of 0.2 to 1.2, just to make the plot uniform. But you can change that.

Let's say you don't like the Tpr curve at 1.7 (shown above), just change the ylim parameter:

getStandingKatzCurve(tpr = 1.7, toSave = FALSE, toView = FALSE, 
                     ylim = c(0.8, 1.1))

And here is the list of dataframes of the digitized points for the lp Standing-Katz curves:

names(result)

Getting only the SK data

We have this other simpler function that will return only the data without saving, plotting or viewing the object. It is called getStandingKatzData:

library(zFactor)

tpr_vec <- c(1.05, 1.3, 1.5)
all_tpr2 <- getStandingKatzData(tpr_vec)

names(all_tpr2)

Similarly, to what we did with getStandingKatzCurve, we could extract a dataframe for any Tpr:

library(tibble)
as_tibble(all_tpr2[["1.5"]])

Plotting multiple Tpr curves

To be able to plot multiple Tpr curves in one figure we have to convert the multiple dataframes to a tidy dataset. We will take a look on two ways of doing this:

The hard way

Following from the previous example, we have to create a tidy dataset from the list of dataframes.

Let's plot the curves:

library(data.table)
library(ggplot2)

# join the dataframes with rbindlist adding an identifier column
all_tpr_df <- data.table::rbindlist(all_tpr2, idcol = TRUE)
colnames(all_tpr_df)[1] <- "Tpr"    # name the identifier as Tpr

ggplot(all_tpr_df, aes(x=Ppr, y=z, group=Tpr, color=Tpr)) + 
    geom_line() +
    geom_point()

The one step way

Using this function multiplotStandingKatz you can achieve the same result:

tpr_vec <- c(1.05, 1.3, 1.5)
multiplotStandingKatz(tpr_vec)

The dots are the digitized points of the Standing-Katz chart.

All the Tpr curves of Standing-Katz chart

Using lapply, getStandingKatzData and data.table::rbindlist:

library(zFactor)
library(ggplot2)
library(data.table)

tpr_vec <- c(1.05, 1.1, 1.2, 1.3, 1.5, 1.6, 1.7, 1.9, 2.0, 2.4, 2.6, 2.8, 3.0)
all_tpr2 <- (lapply(tpr_vec, function(x) getStandingKatzData(tpr = x)))
names(all_tpr2) <- tpr_vec

all_tpr_df <- data.table::rbindlist(all_tpr2, idcol = TRUE)
colnames(all_tpr_df)[1] <- "Tpr"

ggplot(all_tpr_df, aes(x=Ppr, y=z, group=Tpr, color=Tpr)) + 
    geom_line() +
    geom_point()

Or the one-liner:

tpr_vec <- getStandingKatzTpr(pprRange = "lp")
multiplotStandingKatz(tpr_vec)

Plotting the low-range Tpr

If you just prefer the low range Tpr curves:

library(zFactor)
library(ggplot2)
library(data.table)

low_tpr_vec <- c(1.05, 1.1, 1.2, 1.3, 1.4)
low_tpr_li <- (lapply(low_tpr_vec, function(x) getStandingKatzData(tpr = x)))
names(low_tpr_li) <- low_tpr_vec

low_tpr_df <- data.table::rbindlist(low_tpr_li, idcol = TRUE)
colnames(low_tpr_df)[1] <- "Tpr"

ggplot(low_tpr_df, aes(x=Ppr, y=z, group=Tpr, color=Tpr)) + 
    geom_line() +
    geom_point()

With the one liner:

low_tpr_vec <- c(1.05, 1.1, 1.2, 1.3, 1.4)
multiplotStandingKatz(low_tpr_vec)

The medium-range Tpr curves

library(zFactor)
library(ggplot2)
library(data.table)

med_tpr_vec <- c(1.5, 1.6, 1.7, 1.8, 1.9)
med_tpr_li <- (lapply(med_tpr_vec, function(x) getStandingKatzData(tpr = x)))
names(med_tpr_li) <- med_tpr_vec

med_tpr_df <- data.table::rbindlist(med_tpr_li, idcol = TRUE)
colnames(med_tpr_df)[1] <- "Tpr"

ggplot(med_tpr_df, aes(x=Ppr, y=z, group=Tpr, color=Tpr)) + 
    geom_line() +
    geom_point()

The one liner:

med_tpr_vec <- c(1.5, 1.6, 1.7, 1.8, 1.9)
multiplotStandingKatz(low_tpr_vec)

The high-range of Tpr curves

library(zFactor)
library(ggplot2)
library(data.table)

hi_tpr_vec <- c(2.0, 2.4, 2.6, 2.8, 3.0)
hi_tpr_li <- (lapply(hi_tpr_vec, function(x) getStandingKatzData(tpr = x)))
names(hi_tpr_li) <- hi_tpr_vec

hi_tpr_df <- data.table::rbindlist(hi_tpr_li, idcol = TRUE)
colnames(hi_tpr_df)[1] <- "Tpr"

ggplot(hi_tpr_df, aes(x=Ppr, y=z, group=Tpr, color=Tpr)) + 
    geom_line() +
    geom_point()

hi_tpr_vec <- c(2.0, 2.4, 2.6, 2.8, 3.0)
multiplotStandingKatz(hi_tpr_vec)

How does the tidy dataframe look

By performing a summary of the tidy dataset we can learn a lot more of the data we are plotting:

• The number of observations or rows is 117
• The Ppr ranges from 0.3 to 8.005
• The z values go from 0.937 to 1.097. This is understandle since we are referring to the high side of the Tpr curves.
• From all the digitized points, only 1 out of 117 was digitized far from the 1/10 grid.

library(tibble)
as_tibble(hi_tpr_df)

summary(hi_tpr_df)

With this we are in a position to compare the original Standing-Katz chart against the most common compresssibility correlations.

zFactor documentation built on Aug. 1, 2019, 5:04 p.m.