data <- read.csv("C:/Users/Haneen/Desktop/haneen/karpur.csv",header=TRUE)
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 phi.core.frac
## Min. : 0.42 Length:819 Min. :0.1570
## 1st Qu.: 657.33 Class :character 1st Qu.:0.2390
## Median : 1591.22 Mode :character Median :0.2760
## Mean : 2251.91 Mean :0.2693
## 3rd Qu.: 3046.82 3rd Qu.:0.3070
## Max. :15600.00 Max. :0.3630
head(data)
## 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 0.353152
tail(data)
## depth caliper ind.deep ind.med gamma phi.N R.deep R.med SP
## 814 6080.5 8.578 683.847 672.125 24.003 0.208 1.462 1.488 -33.714
## 815 6081.0 8.590 678.002 669.171 27.855 0.214 1.475 1.494 -4.808
## 816 6081.5 8.588 668.633 661.949 32.591 0.228 1.496 1.511 -4.727
## 817 6082.0 8.588 656.328 647.918 38.547 0.243 1.524 1.543 -21.390
## 818 6082.5 8.588 643.216 628.536 45.555 0.256 1.555 1.591 -31.597
## 819 6083.0 8.588 631.098 608.163 52.244 0.265 1.584 1.644 -36.109
## density.corr density phi.core k.core Facies phi.core.frac
## 814 0.003 2.147 26.9000 1300.790 F5 0.269000
## 815 -0.002 2.162 25.7547 1249.706 F5 0.257547
## 816 -0.008 2.158 24.5569 1196.282 F5 0.245569
## 817 -0.006 2.136 23.3592 1142.859 F5 0.233592
## 818 -0.002 2.115 22.1614 1089.435 F5 0.221614
## 819 -0.003 2.109 20.9637 1036.012 F5 0.209637
range(data$depth)
## [1] 5667 6083
par(mfrow=c(1,2))
hist(data$k.core, main='Histogram of Permeability', xlab='Permeability, md', col='red')
hist(data$phi.core, main='Histogram of Porosity', xlab='Porosity', col='blue',xlim=c(0,45))
model1 <- lm(k.core ~ depth+gamma+R.deep+R.med+SP+density+phi.core+Facies-1,data=data)
summary(model1)
##
## Call:
## lm(formula = k.core ~ depth + gamma + R.deep + R.med + SP + density +
## phi.core + Facies - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5461.7 -545.5 37.0 505.0 9072.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## depth 8.088 1.059 7.639 6.23e-14 ***
## gamma -51.915 4.526 -11.471 < 2e-16 ***
## R.deep -22.467 6.261 -3.588 0.000353 ***
## R.med 48.859 8.730 5.597 2.99e-08 ***
## SP -6.490 3.118 -2.082 0.037671 *
## density 2013.607 1047.054 1.923 0.054818 .
## phi.core 188.002 21.791 8.628 < 2e-16 ***
## FaciesF1 -51703.918 5802.150 -8.911 < 2e-16 ***
## FaciesF10 -50893.923 5917.391 -8.601 < 2e-16 ***
## FaciesF2 -51009.565 5854.625 -8.713 < 2e-16 ***
## FaciesF3 -51322.213 5871.731 -8.741 < 2e-16 ***
## FaciesF5 -51174.044 6008.663 -8.517 < 2e-16 ***
## FaciesF7 -52302.690 5971.976 -8.758 < 2e-16 ***
## FaciesF8 -53362.629 6033.789 -8.844 < 2e-16 ***
## FaciesF9 -54796.231 6100.716 -8.982 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1266 on 804 degrees of freedom
## Multiple R-squared: 0.8436, Adjusted R-squared: 0.8407
## F-statistic: 289.1 on 15 and 804 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(model1)
model1.red <- lm(k.core ~ depth+gamma+R.deep+R.med+SP+density+phi.core+Facies-1, data =data)
summary(model1.red)
##
## Call:
## lm(formula = k.core ~ depth + gamma + R.deep + R.med + SP + density +
## phi.core + Facies - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5461.7 -545.5 37.0 505.0 9072.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## depth 8.088 1.059 7.639 6.23e-14 ***
## gamma -51.915 4.526 -11.471 < 2e-16 ***
## R.deep -22.467 6.261 -3.588 0.000353 ***
## R.med 48.859 8.730 5.597 2.99e-08 ***
## SP -6.490 3.118 -2.082 0.037671 *
## density 2013.607 1047.054 1.923 0.054818 .
## phi.core 188.002 21.791 8.628 < 2e-16 ***
## FaciesF1 -51703.918 5802.150 -8.911 < 2e-16 ***
## FaciesF10 -50893.923 5917.391 -8.601 < 2e-16 ***
## FaciesF2 -51009.565 5854.625 -8.713 < 2e-16 ***
## FaciesF3 -51322.213 5871.731 -8.741 < 2e-16 ***
## FaciesF5 -51174.044 6008.663 -8.517 < 2e-16 ***
## FaciesF7 -52302.690 5971.976 -8.758 < 2e-16 ***
## FaciesF8 -53362.629 6033.789 -8.844 < 2e-16 ***
## FaciesF9 -54796.231 6100.716 -8.982 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1266 on 804 degrees of freedom
## Multiple R-squared: 0.8436, Adjusted R-squared: 0.8407
## F-statistic: 289.1 on 15 and 804 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(model1.red)
k.core.pred <- predict(model1.red,)
range(data$k.core)
## [1] 0.42 15600.00
Adjr.sq1 <- 1-sum((k.core.pred - data$k.core)^2)/sum((data$k.core-mean(data$k.core))^2)
Adjr.sq1
## [1] 0.6847518
mspe.model1 <- sqrt(sum((k.core.pred - data$k.core)^2)/nrow(data))
mspe.model1
## [1] 1254.46
par(mfrow=c(1,1))
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(data$k.core),type="p", col="black", lwd = 5, pch=16, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred),type="l", col="green", lwd = 5, pch=17, xlab='',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2, main='Adjr.sq1=0.6847 & RMSE=1254')
grid()
legend('topright', legend=c("Observed", "Predicted"), pch=c(16,17), col=c("black", "green"))
model2<-lm(log10(k.core) ~ .-1,data=data)
summary(model2)
##
## Call:
## lm(formula = log10(k.core) ~ . - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5804 -0.1138 0.0322 0.1529 0.7384
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## depth 0.0007425 0.0004718 1.574 0.1160
## caliper -0.4605945 0.2693103 -1.710 0.0876 .
## ind.deep -0.0007951 0.0006222 -1.278 0.2017
## ind.med 0.0007137 0.0006833 1.044 0.2966
## gamma -0.0091269 0.0015885 -5.746 1.30e-08 ***
## phi.N -1.7628155 0.3901024 -4.519 7.16e-06 ***
## R.deep -0.0025878 0.0016620 -1.557 0.1199
## R.med 0.0044073 0.0023960 1.839 0.0662 .
## SP -0.0016935 0.0008312 -2.037 0.0419 *
## density.corr 1.4462633 1.2712045 1.138 0.2556
## density 1.6148374 0.3100921 5.208 2.44e-07 ***
## phi.core 0.0948634 0.0060329 15.724 < 2e-16 ***
## FaciesF1 -2.3461877 4.6532000 -0.504 0.6143
## FaciesF10 -2.2675417 4.6652182 -0.486 0.6271
## FaciesF2 -2.3646211 4.6544047 -0.508 0.6116
## FaciesF3 -2.3769425 4.6697172 -0.509 0.6109
## FaciesF5 -2.2367684 4.6697432 -0.479 0.6321
## FaciesF7 -2.0650257 4.6623059 -0.443 0.6579
## FaciesF8 -2.4438111 4.6776859 -0.522 0.6015
## FaciesF9 -2.7023993 4.7023453 -0.575 0.5657
## phi.core.frac NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3335 on 799 degrees of freedom
## Multiple R-squared: 0.9891, Adjusted R-squared: 0.9888
## F-statistic: 3613 on 20 and 799 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(model2)
model2.red <- lm(log10(k.core) ~ caliper+gamma+phi.N+SP+density+phi.core+Facies-1,data=data)
summary(model2.red)
##
## Call:
## lm(formula = log10(k.core) ~ caliper + gamma + phi.N + SP + density +
## phi.core + Facies - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.60108 -0.11891 0.03253 0.15792 0.70925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## caliper -0.840136 0.182354 -4.607 4.74e-06 ***
## gamma -0.010369 0.001337 -7.754 2.68e-14 ***
## phi.N -1.596764 0.359392 -4.443 1.01e-05 ***
## SP -0.001585 0.000793 -1.998 0.04600 *
## density 1.762145 0.287056 6.139 1.31e-09 ***
## phi.core 0.093920 0.005931 15.835 < 2e-16 ***
## FaciesF1 4.981486 1.783281 2.793 0.00534 **
## FaciesF10 5.101106 1.793029 2.845 0.00455 **
## FaciesF2 4.995149 1.796855 2.780 0.00556 **
## FaciesF3 4.967152 1.799799 2.760 0.00591 **
## FaciesF5 5.158638 1.760353 2.930 0.00348 **
## FaciesF7 5.272425 1.759300 2.997 0.00281 **
## FaciesF8 4.952389 1.753512 2.824 0.00486 **
## FaciesF9 4.687264 1.762588 2.659 0.00799 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3346 on 805 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9887
## F-statistic: 5128 on 14 and 805 DF, p-value: < 2.2e-16
par(mfrow=c(2,2))
plot(model2.red)
k.core.pred2 <- predict(model2.red,)
k.core.pred2 <- 10^(k.core.pred2)
Adjr.sq2 <- 1-sum((k.core.pred2 - data$k.core)^2)/sum((data$k.core-mean(data$k.core))^2)
Adjr.sq2
## [1] 0.6366977
mspe.model2 <- sqrt(sum((k.core.pred2 - data$k.core)^2)/nrow(data))
mspe.model2
## [1] 1346.681
par(mfrow=c(1,1))
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(data$k.core),type="p", col="red", lwd = 5, pch=16, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred2),type="l", col="darkblue", lwd = 5, pch=15, xlab='',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2, main='Adjr.sq1=0.6367 & RMSE=1346')
grid()
legend('topright', legend=c("Observed", "Predicted"), pch=c(16,15), col=c("red", "darkblue"))
require(MASS)
## Loading required package: MASS
library(MASS)
par(mfrow=c(1,1))
bc = boxcox(model1.red)
best.lam = bc$x[which(bc$y== max(bc$y))]
k.core.pred3 <- lm(((data$k.core^best.lam-1)/best.lam)~., data)
summary(k.core.pred3)
##
## Call:
## lm(formula = ((data$k.core^best.lam - 1)/best.lam) ~ ., data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.1372 -3.4079 0.4885 3.7256 22.5990
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.070e+02 9.396e+01 -2.203 0.027858 *
## depth 3.097e-02 9.528e-03 3.250 0.001201 **
## caliper -2.829e+00 5.438e+00 -0.520 0.603091
## ind.deep -7.161e-03 1.256e-02 -0.570 0.568883
## ind.med 7.916e-03 1.380e-02 0.574 0.566320
## gamma -2.748e-01 3.208e-02 -8.566 < 2e-16 ***
## phi.N -2.832e+01 7.877e+00 -3.595 0.000344 ***
## R.deep -9.516e-02 3.356e-02 -2.835 0.004694 **
## R.med 2.095e-01 4.838e-02 4.331 1.67e-05 ***
## SP -3.949e-02 1.679e-02 -2.353 0.018873 *
## density.corr 9.060e+00 2.567e+01 0.353 0.724213
## density 2.513e+01 6.262e+00 4.014 6.53e-05 ***
## phi.core 1.865e+00 1.218e-01 15.306 < 2e-16 ***
## FaciesF10 4.596e+00 1.916e+00 2.399 0.016679 *
## FaciesF2 3.514e+00 3.105e+00 1.132 0.258152
## FaciesF3 7.987e-01 1.785e+00 0.447 0.654666
## FaciesF5 3.426e+00 1.830e+00 1.872 0.061509 .
## FaciesF7 3.862e+00 3.065e+00 1.260 0.208038
## FaciesF8 -3.835e+00 2.096e+00 -1.829 0.067726 .
## FaciesF9 -1.342e+01 2.294e+00 -5.852 7.09e-09 ***
## phi.core.frac NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.734 on 799 degrees of freedom
## Multiple R-squared: 0.7679, Adjusted R-squared: 0.7624
## F-statistic: 139.1 on 19 and 799 DF, p-value: < 2.2e-16
k.core.pred3 <- lm(((data$k.core^best.lam-1)/best.lam)~caliper+gamma+phi.N+density+phi.core+Facies-1, data = data)
summary(k.core.pred3)
##
## Call:
## lm(formula = ((data$k.core^best.lam - 1)/best.lam) ~ caliper +
## gamma + phi.N + density + phi.core + Facies - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.1055 -3.7338 0.3807 3.8464 22.9901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## caliper -17.9711 3.7550 -4.786 2.03e-06 ***
## gamma -0.3473 0.0275 -12.627 < 2e-16 ***
## phi.N -18.4233 7.4035 -2.488 0.01303 *
## density 26.2309 5.9095 4.439 1.03e-05 ***
## phi.core 1.9057 0.1222 15.596 < 2e-16 ***
## FaciesF1 105.7366 36.6680 2.884 0.00404 **
## FaciesF10 110.9764 36.8817 3.009 0.00270 **
## FaciesF2 108.7969 36.9502 2.944 0.00333 **
## FaciesF3 106.3250 37.0225 2.872 0.00419 **
## FaciesF5 111.9329 36.2060 3.092 0.00206 **
## FaciesF7 107.6154 36.2082 2.972 0.00305 **
## FaciesF8 103.3649 36.0747 2.865 0.00427 **
## FaciesF9 95.1143 36.2597 2.623 0.00888 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.893 on 806 degrees of freedom
## Multiple R-squared: 0.965, Adjusted R-squared: 0.9645
## F-statistic: 1712 on 13 and 806 DF, p-value: < 2.2e-16
k.core.pred3<- predict(k.core.pred3, data)
k.core.pred3 <- exp(log(best.lam*k.core.pred3+1)/best.lam)
####cbind(data$k.core,k.core.pred3)
AdjR.sq3 <- 1-sum((k.core.pred3 - data$k.core)^2)/sum((data$k.core-mean(data$k.core))^2)
AdjR.sq3
## [1] 0.6823408
rmse.model3 <- sqrt(sum((k.core.pred3 - data$k.core)^2)/nrow(data))
rmse.model3
## [1] 1259.248
par(mfrow=c(1,1))
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(data$k.core),type="p", col="lightgreen", lwd = 5, pch=16, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred2),type="l", col="red", lwd = 5, pch=15, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2, main='Adjr.sq1=0.9999 & RMSE=0')
grid()
legend('topright', legend=c("Observed", "Predicted"), pch=c(16,15), col=c("lightgreen", "red"))
par(mfrow=c(1,1))
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(data$k.core),type="p", col="red", lwd = 5, pch=16, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred),type="l", col="blue", lwd = 5, pch=17, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred2),type="l", col="lightgreen", lwd = 5, pch=15, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred3),type="l", col="black", lwd = 5, pch=15, xlab='k',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2)
grid()
legend('topright', legend=c("Observed", "Predicted1", "Predicted2", "Predicted3"), pch=c(16,15,17,11), col=c("red", "blue", "lightgreen", "black"))
set.seed(42)
rows <- sample(nrow(data))
data<-data[rows, ]
head(data)
## depth caliper ind.deep ind.med gamma phi.N R.deep R.med SP
## 561 5950.0 8.489 26.833 29.266 41.036 0.284 37.268 34.169 -21.363
## 321 5827.0 8.588 8.425 10.695 24.896 0.191 118.698 93.501 -50.852
## 153 5743.0 8.816 341.817 353.069 72.168 0.248 2.925 2.832 -35.805
## 74 5703.5 8.686 32.016 36.599 56.703 0.080 31.234 27.323 -35.935
## 228 5780.5 8.686 222.486 199.552 60.909 0.177 4.495 5.011 -44.079
## 146 5739.5 8.816 316.687 306.366 68.606 0.268 3.158 3.264 -39.945
## density.corr density phi.core k.core Facies phi.core.frac
## 561 -0.004 2.054 31.2625 3293.4705 F8 0.312625
## 321 -0.021 2.073 27.6898 5338.7207 F5 0.276898
## 153 0.016 2.171 23.5965 467.5622 F3 0.235965
## 74 -0.029 1.943 31.0000 1866.9200 F1 0.310000
## 228 -0.015 2.181 23.1149 1109.5569 F3 0.231149
## 146 -0.038 2.113 29.0670 597.4316 F3 0.290670
split <- round(nrow(data)*0.70)
train <- data[1:split, ]
test <- data[(split+1):nrow(data), ]
mod <- lm(k.core.pred3 ~.-1,data=data)
summary(mod)
##
## Call:
## lm(formula = k.core.pred3 ~ . - 1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2750 -1405 -345 1103 6349
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## depth 3.079e+00 2.399e+00 1.283 0.1998
## caliper 1.492e+02 1.352e+03 0.110 0.9121
## ind.deep -2.511e+00 3.120e+00 -0.805 0.4211
## ind.med 2.886e+00 3.426e+00 0.842 0.3999
## gamma 2.009e+00 8.249e+00 0.243 0.8077
## phi.N -3.734e+03 1.958e+03 -1.907 0.0569 .
## R.deep -7.080e+00 8.405e+00 -0.842 0.3999
## R.med 1.532e+01 1.230e+01 1.246 0.2132
## SP -3.043e+00 4.181e+00 -0.728 0.4670
## density.corr -8.280e+03 6.375e+03 -1.299 0.1943
## density 3.650e+03 1.559e+03 2.342 0.0194 *
## phi.core 4.703e+01 3.156e+01 1.490 0.1366
## k.core -9.566e-02 4.688e-02 -2.040 0.0416 *
## FaciesF1 -2.532e+04 2.355e+04 -1.075 0.2826
## FaciesF10 -2.563e+04 2.360e+04 -1.086 0.2778
## FaciesF2 -2.457e+04 2.355e+04 -1.044 0.2970
## FaciesF3 -2.537e+04 2.363e+04 -1.074 0.2832
## FaciesF5 -2.564e+04 2.362e+04 -1.085 0.2782
## FaciesF7 -2.679e+04 2.359e+04 -1.136 0.2564
## FaciesF8 -2.562e+04 2.368e+04 -1.082 0.2796
## FaciesF9 -2.627e+04 2.381e+04 -1.103 0.2703
## phi.core.frac NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1672 on 798 degrees of freedom
## Multiple R-squared: 0.6197, Adjusted R-squared: 0.6097
## F-statistic: 61.93 on 21 and 798 DF, p-value: < 2.2e-16
par(mfrow=c(1,1))
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(data$k.core),type="p", col="darkgreen", lwd = 3, pch=20, xlab='Permeability, md',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2, main='RMSE=4362.062')
grid()
par(new = TRUE)
plot(y=y<-(data$depth),ylim=rev(range(data$depth)),x=x<-(k.core.pred3),type="p", col="red", lwd = 3, pch=20, xlab='',
ylab='Depth, m', xlim=c(0,16000), cex=1.5, cex.lab=1.5, cex.axis=1.2,)
grid()
legend('topright', legend=c("Observed", "Predicted"), pch=c(16,15), col=c("darkgreen", "red"))
