R Markdown
# Data call
data2<-read.csv("c:/Users/InteL/Desktop/rd/karpur.csv")
head(data2)
## 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 X phi.core.frac
## 1 -0.033 2.205 33.9000 2442.590 F1 NA 0.339000
## 2 -0.067 2.040 33.4131 3006.989 F1 NA 0.334131
## 3 -0.064 1.888 33.1000 3370.000 F1 NA 0.331000
## 4 -0.053 1.794 34.9000 2270.000 F1 NA 0.349000
## 5 -0.054 1.758 35.0644 2530.758 F1 NA 0.350644
## 6 -0.058 1.759 35.3152 2928.314 F1 NA 0.353152
# Plot Porosity Model
plot(data2$phi.N,data2$phi.core.frac)
PoroModel<-lm(phi.core.frac~phi.N+Facies-1,data=data2)
par(mfrow=c(2,2))
plot(PoroModel)
summary(PoroModel)
##
## Call:
## lm(formula = phi.core.frac ~ phi.N + Facies - 1, data = data2)
##
## 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(PoroModel,data2)
head(cbind(data2$phi.core.frac,predict(PoroModel)))
## [,1] [,2]
## 1 0.339000 0.3202847
## 2 0.334131 0.3189082
## 3 0.331000 0.3175183
## 4 0.349000 0.3163957
## 5 0.350644 0.3157275
## 6 0.353152 0.3154468
# now plot porosity corrected to log scale
plot(corrected_porosity)
permModel <- lm(data2$k.core~corrected_porosity,data=data2)
summary(permModel)
##
## Call:
## lm(formula = data2$k.core ~ corrected_porosity, data = data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2936.5 -1229.5 -641.6 355.4 13194.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4056.5 492.9 -8.231 7.34e-16 ***
## corrected_porosity 23422.6 1810.7 12.935 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2038 on 817 degrees of freedom
## Multiple R-squared: 0.17, Adjusted R-squared: 0.169
## F-statistic: 167.3 on 1 and 817 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(permModel)
corrected_permeability <- predict(permModel)
k.corel<-predict(permModel,data2)
plot(k.corel)
head(cbind(data2$k.core,corrected_permeability))
## corrected_permeability
## 1 2442.590 3445.336
## 2 3006.989 3413.094
## 3 3370.000 3380.539
## 4 2270.000 3354.245
## 5 2530.758 3338.594
## 6 2928.314 3332.020
karpur2<-cbind(data2,k.corel)
write.csv(karpur2,"karpur2.csv")
head(karpur2)
## 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 X phi.core.frac k.corel
## 1 -0.033 2.205 33.9000 2442.590 F1 NA 0.339000 3445.336
## 2 -0.067 2.040 33.4131 3006.989 F1 NA 0.334131 3413.094
## 3 -0.064 1.888 33.1000 3370.000 F1 NA 0.331000 3380.539
## 4 -0.053 1.794 34.9000 2270.000 F1 NA 0.349000 3354.245
## 5 -0.054 1.758 35.0644 2530.758 F1 NA 0.350644 3338.594
## 6 -0.058 1.759 35.3152 2928.314 F1 NA 0.353152 3332.020
# And finally plot permeability corrected to log scale
par(mfrow=c(1,3))
plot(y=y<-(data2$depth),ylim=rev(range(data2$depth)),x=x<-(data2$ phi.core),type="l", col="darkgreen", lwd = 5, pch=17, xlab='phi.core.frac',
ylab='Depth, m', xlim=c(14,38), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
plot(y=y<-(data2$depth),ylim=rev(range(data2$depth)),x=x<-(data2$ k.core),type="l", col="red", lwd = 5, pch=17, xlab='k.core',
ylab='Depth, m', xlim=c(0.22,15800.00), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
plot(y=y<-(data2$depth),ylim=rev(range(data2$depth)),x=x<-(data2$ phi.N),type="l", col="yellow", lwd = 5, pch=17, xlab='phi.N',
ylab='Depth, m', xlim=c(0.012,0.510), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(mfrow=c(1,3))
plot(y=y<-(data2$depth),ylim=rev(range(data2$depth)),x=x<-(k.corel),type="l", col="blue", lwd = 5, pch=17, xlab='k.corel',
ylab='Depth, m', xlim=c(10,6400), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
