table_data <- read.csv("karpur.csv")
#Convert phi.core from percentage to fraction
table_data$phi.core <- table_data$phi.core / 100
head(table_data)
plot(table_data$phi.N, table_data$phi.core , xlab = "Porosity log" , ylab = "Porosity core" )
porosity_model <- lm( phi.core~phi.N + Facies - 1, data = table_data)
summary(porosity_model)
##
## Call:
## lm(formula = phi.core ~ phi.N + Facies - 1, data = table_data)
##
## 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-16
corrected_porosity <- predict(porosity_model, table_data)
permeability_model <- lm( k.core~corrected_porosity + Facies - 1 , data = table_data)
summary(permeability_model)
##
## Call:
## lm(formula = k.core ~ corrected_porosity + Facies - 1, data = table_data)
##
## 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_porosity -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-16
corrected_permeability <- predict(permeability_model, data = table_data)
par(mfrow = c(1,5))
boxplot(depth~Facies , data = table_data, ylim = rev(c(5667,6083)), main = "Facies vs Depth" )
plot(table_data$phi.core, table_data$depth, ylim = rev(c(5667,6083)), xlim = c(0.1570,0.3630)
,lwd = 2 , xlab = "Porosity core" , ylab = "depth" , main = "Porosity Core vs Depth" ,type = "l" , col = "red")
plot(corrected_porosity, table_data$depth, ylim = rev(c(5667,6083)), xlim = c(0.1570,0.3630)
,lwd = 2 , xlab = "Coreccted Porosity core" , ylab = "depth" , main = "Corrected Porosity Core vs Depth" , type = "l" , col = "blue")
plot(table_data$k.core, table_data$depth, ylim = rev(c(5667,6083)), xlim = c(0.42,15600.00)
,lwd = 2 , xlab = "Permeability core" , ylab = "depth" , main = "Permeability Core vs Depth" ,type = "l" , col = "red")
plot(corrected_permeability, table_data$depth, ylim = rev(c(5667,6083)), xlim = c(0.42,15600.00)
,lwd = 2 , xlab = "Corrected Permeability core" , ylab = "depth" , main = "Corrected Permeability Core vs Depth" , type = "l" , col = "blue")
