The Dykstra-Parsons method involves plotting the frequency distribution of the permeability on a log-normal probability graph paper. This is done by arranging the permeability values in descending order and then calculating for each permeability, the percent of the samples with permeability greater than that value. to avoid values of zero or 100%, the percent greater than or equal to value is normalized by n+1, where n it is the number of samples.
data = read.csv('C:/Users/dell/Downloads/Telegram Desktop/karpur.csv')
kcore= data$k.core
depth=data$depth
summary(data)
## depth caliper ind.deep ind.med
## Min. :5667 Min. :8.487 Min. : 6.532 Min. : 9.386
## 1st Qu.:5769 1st Qu.:8.556 1st Qu.: 28.799 1st Qu.: 27.892
## Median :5872 Median :8.588 Median :217.849 Median :254.383
## Mean :5873 Mean :8.622 Mean :275.357 Mean :273.357
## 3rd Qu.:5977 3rd Qu.:8.686 3rd Qu.:566.793 3rd Qu.:544.232
## Max. :6083 Max. :8.886 Max. :769.484 Max. :746.028
## gamma phi.N R.deep R.med
## Min. : 16.74 Min. :0.0150 Min. : 1.300 Min. : 1.340
## 1st Qu.: 40.89 1st Qu.:0.2030 1st Qu.: 1.764 1st Qu.: 1.837
## Median : 51.37 Median :0.2450 Median : 4.590 Median : 3.931
## Mean : 53.42 Mean :0.2213 Mean : 24.501 Mean : 21.196
## 3rd Qu.: 62.37 3rd Qu.:0.2640 3rd Qu.: 34.724 3rd Qu.: 35.853
## Max. :112.40 Max. :0.4100 Max. :153.085 Max. :106.542
## SP density.corr density phi.core
## Min. :-73.95 Min. :-0.067000 Min. :1.758 Min. :15.70
## 1st Qu.:-42.01 1st Qu.:-0.016000 1st Qu.:2.023 1st Qu.:23.90
## Median :-32.25 Median :-0.007000 Median :2.099 Median :27.60
## Mean :-30.98 Mean :-0.008883 Mean :2.102 Mean :26.93
## 3rd Qu.:-19.48 3rd Qu.: 0.002000 3rd Qu.:2.181 3rd Qu.:30.70
## Max. : 25.13 Max. : 0.089000 Max. :2.387 Max. :36.30
## k.core Facies
## Min. : 0.42 Length:819
## 1st Qu.: 657.33 Class :character
## Median : 1591.22 Mode :character
## Mean : 2251.91
## 3rd Qu.: 3046.82
## Max. :15600.00
make sort the permeability in decreasing order
sorted_k = sort(kcore,decreasing = TRUE)
head(sorted_k)
## [1] 15600.00 14225.31 13544.98 13033.53 11841.74 11117.40
n = length(sorted_k)
k_percent = c(1:n)/(n+1)
summary(k_percent)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00122 0.25061 0.50000 0.50000 0.74939 0.99878
mod1=lm(log10(sorted_k) ~ k_percent)
plot(k_percent, sorted_k, log="y", lwd = 2)
abline(mod1)
k50=10^predict(mod1,data.frame(k_percent=c(0.5)))
k84.1=10^predict(mod1,data.frame(k_percent=c(0.841)))
V=(k50 - k84.1)/(k50)
print(V)
## 1
## 0.7661618