Homework: simple linear regression model to predict core porosity and permeability corrected to log scale
Dina Qays-Basrah University For Oil And Gas
Import data :
at the first we import csv file
`karpur.(2)` <- read.csv("C:/Users/gaming/OneDrive/Desktop/Dena Qais/karpur (2).csv")
head(`karpur.(2)`) ## depth caliper ind.deep ind.med gamma phi.N R.deep R.med SP
## 1 5667.0 8.685 618.005 569.781 98.823 0.410 1.618 1.755 -56.587
## 2 5667.5 8.686 497.547 419.494 90.640 0.307 2.010 2.384 -61.916
## 3 5668.0 8.686 384.935 300.155 78.087 0.203 2.598 3.332 -55.861
## 4 5668.5 8.686 278.324 205.224 66.232 0.119 3.593 4.873 -41.860
## 5 5669.0 8.686 183.743 131.155 59.807 0.069 5.442 7.625 -34.934
## 6 5669.5 8.686 109.512 75.633 57.109 0.048 9.131 13.222 -39.769
## density.corr density phi.core k.core Facies phi.core.frac
## 1 -0.033 2.205 33.9000 2442.590 F1 0.339000
## 2 -0.067 2.040 33.4131 3006.989 F1 0.334131
## 3 -0.064 1.888 33.1000 3370.000 F1 0.331000
## 4 -0.053 1.794 34.9000 2270.000 F1 0.349000
## 5 -0.054 1.758 35.0644 2530.758 F1 0.350644
## 6 -0.058 1.759 35.3152 2928.314 F1 0.353152View(`karpur.(2)`)
plot(`karpur.(2)`$phi.N,`karpur.(2)`$phi.core.frac) 
Creat Simple Linear Regression Model between core porosity and log porosity :
porosity_model<- lm(phi.core.frac~phi.N+Facies-1,data=`karpur.(2)`)
summary(porosity_model) ##
## Call:
## lm(formula = phi.core.frac ~ phi.N + Facies - 1, data = `karpur.(2)`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.103530 -0.011573 -0.000206 0.010463 0.102852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## phi.N 0.013364 0.018060 0.74 0.46
## FaciesF1 0.314805 0.002777 113.37 <2e-16 ***
## FaciesF10 0.207680 0.005072 40.95 <2e-16 ***
## FaciesF2 0.175233 0.009390 18.66 <2e-16 ***
## FaciesF3 0.231939 0.004955 46.81 <2e-16 ***
## FaciesF5 0.272953 0.003914 69.74 <2e-16 ***
## FaciesF7 0.225164 0.008730 25.79 <2e-16 ***
## FaciesF8 0.305884 0.005019 60.94 <2e-16 ***
## FaciesF9 0.264448 0.004825 54.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02326 on 810 degrees of freedom
## Multiple R-squared: 0.9928, Adjusted R-squared: 0.9928
## F-statistic: 1.246e+04 on 9 and 810 DF, p-value: < 2.2e-16Prediction :
corrected_poro.<-predict(porosity_model,)
permeabilty_model<-lm(`karpur.(2)`$k.core~corrected_poro.+Facies-1,data=`karpur.(2)`)
summary(permeabilty_model)##
## Call:
## lm(formula = `karpur.(2)`$k.core ~ corrected_poro. + Facies -
## 1, data = `karpur.(2)`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5613.4 -596.9 -130.3 475.0 10449.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## corrected_poro. -412352 89814 -4.591 5.11e-06 ***
## FaciesF1 132659 28386 4.673 3.47e-06 ***
## FaciesF10 87869 18969 4.632 4.21e-06 ***
## FaciesF2 73980 16049 4.610 4.69e-06 ***
## FaciesF3 97910 21087 4.643 4.00e-06 ***
## FaciesF5 118916 24729 4.809 1.81e-06 ***
## FaciesF7 95868 20496 4.677 3.40e-06 ***
## FaciesF8 130990 27786 4.714 2.86e-06 ***
## FaciesF9 111324 24050 4.629 4.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1546 on 810 degrees of freedom
## Multiple R-squared: 0.7652, Adjusted R-squared: 0.7626
## F-statistic: 293.2 on 9 and 810 DF, p-value: < 2.2e-16corrected_perm.<-predict(permeabilty_model,`karpur.(2)`)Add plots :
par(mfrow=(c(1,5)))
plot(`karpur.(2)`$phi.core.frac,`karpur.(2)`$depth,ylim =rev(c(5667,6083)), xlim=c(0.1570,0.3630)
,type="l", lwd=2, xlab='core porosity' ,ylab='depth m')
plot(corrected_poro.,`karpur.(2)`$depth,ylim =rev(c(5667,6083)), xlim=c(0.1570,0.3630)
,type="l", lwd=2, xlab='corrected core porosity' ,ylab='depth m')
plot(`karpur.(2)`$k.core,`karpur.(2)`$depth,ylim =rev(c(5667,6083)), xlim=c(0.42,15600.00)
,type="l", lwd=2, xlab='core permeability' ,ylab='depth m')
plot(corrected_perm.,`karpur.(2)`$depth,ylim =rev(c(5667,6083)), xlim=c(0.42,15600.00)
,type="l", lwd=2, xlab='corrected core permeability' ,ylab='depth m')
boxplot(depth~Facies,data=`karpur.(2)`,ylim =rev(c(5667,6083)))