Perdict & Measure permeability in multiple scenarios
Karar Methaq Mousa
2024-11-29
In this R Markdown we usin MLR to predict permeability in multiple scenarios, as follows: 1. MLR of Permeability given all well logs without Facies. 2. MLR of Permeability given all well logs with Facies. 3. MLR of Log10(Permeability) given all well logs with Facies. 4. Random Subsampling Cross-Validation applied on MLR of Log10(Permeability) given all well logs with Facies.
with Apply Stepwise Elimination on the all previous model.
data=read.csv("C:/Users/DELL/OneDrive/Desktop/karar2/karpur.csv")
library(caret)## Warning: package 'caret' was built under R version 4.4.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.4.2## Loading required package: latticemodel_1<- lm(k.core~ .-Facies,data=data)
summary(model_1)##
## Call:
## lm(formula = k.core ~ . - Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5549.5 -755.5 -178.1 578.0 11260.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60762.728 16605.360 3.659 0.000269 ***
## depth -7.398 1.446 -5.115 3.92e-07 ***
## caliper -3955.952 1055.105 -3.749 0.000190 ***
## ind.deep -14.183 2.345 -6.048 2.24e-09 ***
## ind.med 17.300 2.509 6.896 1.08e-11 ***
## gamma -77.487 5.475 -14.153 < 2e-16 ***
## phi.N -1784.704 1301.772 -1.371 0.170763
## R.deep -26.007 6.974 -3.729 0.000206 ***
## R.med 63.525 9.841 6.455 1.86e-10 ***
## SP -8.784 3.460 -2.539 0.011313 *
## density.corr -523.060 5358.876 -0.098 0.922269
## density 8011.106 1120.554 7.149 1.96e-12 ***
## phi.core 183.203 23.802 7.697 4.07e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1442 on 806 degrees of freedom
## Multiple R-squared: 0.5903, Adjusted R-squared: 0.5842
## F-statistic: 96.77 on 12 and 806 DF, p-value: < 2.2e-16k.predicted_1 <-predict(model_1,data=data)
plot(k.predicted_1,data$k.core)
rmse_1<- RMSE(k.predicted_1,data$k.core )
rmse_1## [1] 1430.118model_2<-step(model_1 , direction = "backward")## Start: AIC=11926.91
## k.core ~ (depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density.corr + density + phi.core +
## Facies) - Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 19799 1675068713 11925
## - phi.N 1 3906205 1678955118 11927
## <none> 1675048914 11927
## - SP 1 13394190 1688443104 11931
## - R.deep 1 28897686 1703946599 11939
## - caliper 1 29214826 1704263740 11939
## - depth 1 54372650 1729421563 11951
## - ind.deep 1 76022788 1751071701 11961
## - R.med 1 86603706 1761652619 11966
## - ind.med 1 98823752 1773872666 11972
## - density 1 106221406 1781270319 11975
## - phi.core 1 123125117 1798174031 11983
## - gamma 1 416312526 2091361440 12107
##
## Step: AIC=11924.92
## k.core ~ depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density + phi.core
##
## Df Sum of Sq RSS AIC
## <none> 1675068713 11925
## - phi.N 1 4564880 1679633593 11925
## - SP 1 13491079 1688559792 11930
## - R.deep 1 28896144 1703964857 11937
## - caliper 1 29253869 1704322581 11937
## - depth 1 54825159 1729893872 11949
## - ind.deep 1 77573926 1752642639 11960
## - R.med 1 86772220 1761840933 11964
## - ind.med 1 100740701 1775809413 11971
## - density 1 114209586 1789278299 11977
## - phi.core 1 124694278 1799762991 11982
## - gamma 1 417015194 2092083907 12105summary(model_2)##
## Call:
## lm(formula = k.core ~ depth + caliper + ind.deep + ind.med +
## gamma + phi.N + R.deep + R.med + SP + density + phi.core,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5545.3 -753.4 -177.1 576.8 11260.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60910.619 16525.937 3.686 0.000243 ***
## depth -7.409 1.442 -5.139 3.46e-07 ***
## caliper -3957.892 1054.270 -3.754 0.000186 ***
## ind.deep -14.146 2.314 -6.113 1.52e-09 ***
## ind.med 17.263 2.478 6.967 6.74e-12 ***
## gamma -77.461 5.465 -14.174 < 2e-16 ***
## phi.N -1825.771 1231.150 -1.483 0.138470
## R.deep -25.972 6.961 -3.731 0.000204 ***
## R.med 63.466 9.816 6.466 1.75e-10 ***
## SP -8.803 3.453 -2.549 0.010974 *
## density 7980.761 1075.902 7.418 3.02e-13 ***
## phi.core 183.436 23.667 7.751 2.75e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1441 on 807 degrees of freedom
## Multiple R-squared: 0.5903, Adjusted R-squared: 0.5847
## F-statistic: 105.7 on 11 and 807 DF, p-value: < 2.2e-16k.predicted_2 <-predict(model_2,data=data)
plot(k.predicted_2,data$k.core)
rmse_2<- RMSE(k.predicted_2,data$k.core )
rmse_2## [1] 1430.126model_3<- lm(k.core~ .,data=data)
summary(model_3)##
## Call:
## lm(formula = k.core ~ ., data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5585.6 -568.9 49.2 476.5 8928.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.783e+04 1.760e+04 -3.853 0.000126 ***
## depth 8.544e+00 1.785e+00 4.786 2.02e-06 ***
## caliper 1.413e+03 1.019e+03 1.387 0.165789
## ind.deep -2.418e-01 2.354e+00 -0.103 0.918220
## ind.med 1.224e+00 2.585e+00 0.473 0.636062
## gamma -4.583e+01 6.010e+00 -7.626 6.88e-14 ***
## phi.N -2.010e+03 1.476e+03 -1.362 0.173540
## R.deep -2.344e+01 6.288e+00 -3.727 0.000207 ***
## R.med 5.643e+01 9.065e+00 6.225 7.76e-10 ***
## SP -7.125e+00 3.145e+00 -2.266 0.023736 *
## density.corr -2.567e+03 4.809e+03 -0.534 0.593602
## density 2.319e+03 1.173e+03 1.976 0.048458 *
## phi.core 1.921e+02 2.282e+01 8.418 < 2e-16 ***
## FaciesF10 8.921e+02 3.590e+02 2.485 0.013157 *
## FaciesF2 9.243e+02 5.818e+02 1.589 0.112514
## FaciesF3 4.393e+02 3.344e+02 1.313 0.189394
## FaciesF5 7.411e+02 3.428e+02 2.162 0.030908 *
## FaciesF7 -4.152e+01 5.742e+02 -0.072 0.942377
## FaciesF8 -1.179e+03 3.927e+02 -3.002 0.002770 **
## FaciesF9 -2.969e+03 4.298e+02 -6.908 1.00e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1262 on 799 degrees of freedom
## Multiple R-squared: 0.6889, Adjusted R-squared: 0.6815
## F-statistic: 93.12 on 19 and 799 DF, p-value: < 2.2e-16k.predicted_3 <-predict(model_3,data=data)
plot(k.predicted_3,data$k.core)
rmse_3<- RMSE(k.predicted_3,data$k.core )
rmse_3## [1] 1246.201model_4<-step(model_3 , direction = "backward")## Start: AIC=11715.43
## k.core ~ depth + caliper + ind.deep + ind.med + gamma + phi.N +
## R.deep + R.med + SP + density.corr + density + phi.core +
## Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 16793 1271937992 11713
## - ind.med 1 356746 1272277945 11714
## - density.corr 1 453661 1272374861 11714
## - phi.N 1 2953609 1274874809 11715
## - caliper 1 3063007 1274984206 11715
## <none> 1271921199 11715
## - density 1 6217927 1278139127 11717
## - SP 1 8171834 1280093033 11719
## - R.deep 1 22117394 1294038593 11728
## - depth 1 36466976 1308388176 11737
## - R.med 1 61690461 1333611660 11752
## - gamma 1 92579723 1364500923 11771
## - phi.core 1 112793101 1384714301 11783
## - Facies 7 403127714 1675048914 11927
##
## Step: AIC=11713.44
## k.core ~ depth + caliper + ind.med + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 437546 1272375538 11712
## - phi.N 1 2938766 1274876758 11713
## - caliper 1 3074396 1275012389 11713
## <none> 1271937992 11713
## - density 1 6228928 1278166920 11715
## - ind.med 1 6905855 1278843848 11716
## - SP 1 8191802 1280129794 11717
## - R.deep 1 22125695 1294063687 11726
## - depth 1 39139470 1311077462 11736
## - R.med 1 61773953 1333711946 11750
## - gamma 1 92865220 1364803212 11769
## - phi.core 1 112960440 1384898432 11781
## - Facies 7 479133709 1751071701 11961
##
## Step: AIC=11711.72
## k.core ~ depth + caliper + ind.med + gamma + phi.N + R.deep +
## R.med + SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - caliper 1 2980713 1275356252 11712
## <none> 1272375538 11712
## - phi.N 1 3279032 1275654571 11712
## - density 1 5792837 1278168375 11713
## - ind.med 1 6813959 1279189497 11714
## - SP 1 8391302 1280766840 11715
## - R.deep 1 22009402 1294384940 11724
## - depth 1 38705776 1311081314 11734
## - R.med 1 61436819 1333812357 11748
## - gamma 1 93974329 1366349868 11768
## - phi.core 1 115336515 1387712053 11781
## - Facies 7 480267100 1752642639 11960
##
## Step: AIC=11711.64
## k.core ~ depth + ind.med + gamma + phi.N + R.deep + R.med + SP +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - phi.N 1 2534906 1277891157 11711
## <none> 1275356252 11712
## - density 1 7270311 1282626562 11714
## - SP 1 8733336 1284089587 11715
## - ind.med 1 12924050 1288280301 11718
## - R.deep 1 22449117 1297805369 11724
## - depth 1 51507476 1326863728 11742
## - R.med 1 60137982 1335494234 11747
## - phi.core 1 112564835 1387921086 11779
## - gamma 1 141535555 1416891807 11796
## - Facies 7 520094756 1795451008 11978
##
## Step: AIC=11711.26
## k.core ~ depth + ind.med + gamma + R.deep + R.med + SP + density +
## phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 1277891157 11711
## - density 1 5155969 1283047127 11713
## - SP 1 8515796 1286406953 11715
## - ind.med 1 10944937 1288836095 11716
## - R.deep 1 23273312 1301164469 11724
## - depth 1 49725248 1327616405 11740
## - R.med 1 59454645 1337345802 11746
## - phi.core 1 110154394 1388045551 11777
## - gamma 1 219059092 1496950249 11839
## - Facies 7 526383446 1804274603 11980summary(model_4)##
## Call:
## lm(formula = k.core ~ depth + ind.med + gamma + R.deep + R.med +
## SP + density + phi.core + Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5608.3 -567.8 35.9 500.7 8989.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.322e+04 6.625e+03 -6.523 1.22e-10 ***
## depth 6.648e+00 1.189e+00 5.590 3.11e-08 ***
## ind.med 1.078e+00 4.111e-01 2.623 0.008894 **
## gamma -5.324e+01 4.537e+00 -11.733 < 2e-16 ***
## R.deep -2.395e+01 6.264e+00 -3.824 0.000141 ***
## R.med 5.515e+01 9.022e+00 6.112 1.53e-09 ***
## SP -7.214e+00 3.118e+00 -2.313 0.020960 *
## density 1.880e+03 1.044e+03 1.800 0.072240 .
## phi.core 1.817e+02 2.184e+01 8.320 3.77e-16 ***
## FaciesF10 8.266e+02 3.533e+02 2.340 0.019553 *
## FaciesF2 7.035e+02 5.567e+02 1.264 0.206697
## FaciesF3 4.100e+02 3.228e+02 1.270 0.204443
## FaciesF5 5.913e+02 3.211e+02 1.841 0.065924 .
## FaciesF7 -3.159e+02 5.402e+02 -0.585 0.558866
## FaciesF8 -1.455e+03 3.122e+02 -4.661 3.69e-06 ***
## FaciesF9 -3.017e+03 3.764e+02 -8.017 3.82e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1262 on 803 degrees of freedom
## Multiple R-squared: 0.6874, Adjusted R-squared: 0.6816
## F-statistic: 117.7 on 15 and 803 DF, p-value: < 2.2e-16k.predicted_4 <-predict(model_4,data=data)
plot(k.predicted_4,data$k.core)
rmse_4<- RMSE(k.predicted_4,data$k.core )
rmse_4## [1] 1249.122data$log10_k.core<-log10(data$k.core)
model_5<- lm(log10_k.core~.-k.core,data=data)
summary(model_5)##
## Call:
## lm(formula = log10_k.core ~ . - k.core, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5804 -0.1138 0.0322 0.1529 0.7384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.3461877 4.6532000 -0.504 0.61425
## depth 0.0007425 0.0004718 1.574 0.11596
## caliper -0.4605945 0.2693103 -1.710 0.08760 .
## ind.deep -0.0007951 0.0006222 -1.278 0.20168
## ind.med 0.0007137 0.0006833 1.044 0.29659
## 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 0.11987
## R.med 0.0044073 0.0023960 1.839 0.06622 .
## SP -0.0016935 0.0008312 -2.037 0.04194 *
## density.corr 1.4462633 1.2712045 1.138 0.25558
## density 1.6148374 0.3100921 5.208 2.44e-07 ***
## phi.core 0.0948634 0.0060329 15.724 < 2e-16 ***
## FaciesF10 0.0786460 0.0948909 0.829 0.40746
## FaciesF2 -0.0184334 0.1537793 -0.120 0.90462
## FaciesF3 -0.0307548 0.0883957 -0.348 0.72799
## FaciesF5 0.1094193 0.0906034 1.208 0.22753
## FaciesF7 0.2811620 0.1517797 1.852 0.06433 .
## FaciesF8 -0.0976234 0.1038054 -0.940 0.34727
## FaciesF9 -0.3562116 0.1135966 -3.136 0.00178 **
## ---
## 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: 0.6806, Adjusted R-squared: 0.673
## F-statistic: 89.6 on 19 and 799 DF, p-value: < 2.2e-16log_k.predicted_5 <-predict(model_5,data=data)
k.predicted_5<-10^log_k.predicted_5
plot(k.predicted_5,data$k.core)
rmse_5<- RMSE(k.predicted_5,data$k.core )
rmse_5## [1] 1333.017model_6<-step(model_5, direction = "backward")## Start: AIC=-1779.02
## log10_k.core ~ (depth + caliper + ind.deep + ind.med + gamma +
## phi.N + R.deep + R.med + SP + density.corr + density + phi.core +
## k.core + Facies) - k.core
##
## Df Sum of Sq RSS AIC
## - ind.med 1 0.1213 88.981 -1779.9
## - density.corr 1 0.1440 89.004 -1779.7
## - ind.deep 1 0.1816 89.042 -1779.3
## <none> 88.860 -1779.0
## - R.deep 1 0.2696 89.130 -1778.5
## - depth 1 0.2754 89.135 -1778.5
## - caliper 1 0.3253 89.185 -1778.0
## - R.med 1 0.3763 89.236 -1777.6
## - SP 1 0.4617 89.322 -1776.8
## - phi.N 1 2.2710 91.131 -1760.3
## - density 1 3.0160 91.876 -1753.7
## - gamma 1 3.6713 92.531 -1747.9
## - Facies 7 7.0758 95.936 -1730.3
## - phi.core 1 27.4982 116.358 -1560.2
##
## Step: AIC=-1779.9
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - density.corr 1 0.1931 89.174 -1780.1
## <none> 88.981 -1779.9
## - ind.deep 1 0.2179 89.199 -1779.9
## - R.deep 1 0.2447 89.226 -1779.7
## - caliper 1 0.2921 89.273 -1779.2
## - R.med 1 0.3397 89.321 -1778.8
## - SP 1 0.4101 89.391 -1778.1
## - depth 1 0.4622 89.444 -1777.7
## - phi.N 1 2.2035 91.185 -1761.9
## - density 1 3.0113 91.993 -1754.6
## - gamma 1 3.5761 92.557 -1749.6
## - Facies 7 9.1242 98.106 -1714.0
## - phi.core 1 27.4190 116.400 -1561.9
##
## Step: AIC=-1780.12
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 0.2180 89.392 -1780.1
## <none> 89.174 -1780.1
## - R.deep 1 0.2526 89.427 -1779.8
## - caliper 1 0.2676 89.442 -1779.7
## - R.med 1 0.3598 89.534 -1778.8
## - SP 1 0.3832 89.558 -1778.6
## - depth 1 0.5404 89.715 -1777.2
## - phi.N 1 2.0726 91.247 -1763.3
## - gamma 1 3.4838 92.658 -1750.7
## - density 1 3.6220 92.796 -1749.5
## - Facies 7 9.3567 98.531 -1712.4
## - phi.core 1 27.2273 116.402 -1563.9
##
## Step: AIC=-1780.12
## log10_k.core ~ depth + caliper + gamma + phi.N + R.deep + R.med +
## SP + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 89.392 -1780.1
## - R.deep 1 0.2869 89.679 -1779.5
## - depth 1 0.3332 89.726 -1779.1
## - SP 1 0.4296 89.822 -1778.2
## - R.med 1 0.5085 89.901 -1777.5
## - caliper 1 0.5746 89.967 -1776.9
## - phi.N 1 2.3337 91.726 -1761.0
## - gamma 1 3.8214 93.214 -1747.8
## - density 1 3.8626 93.255 -1747.5
## - Facies 7 9.2100 98.602 -1713.8
## - phi.core 1 27.0935 116.486 -1565.3summary(model_6)##
## Call:
## lm(formula = log10_k.core ~ depth + caliper + gamma + phi.N +
## R.deep + R.med + SP + density + phi.core + Facies, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.58182 -0.12001 0.03437 0.15230 0.70317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2671250 3.9316796 -0.322 0.74732
## depth 0.0006562 0.0003796 1.729 0.08420 .
## caliper -0.5608681 0.2470292 -2.270 0.02344 *
## gamma -0.0091497 0.0015626 -5.855 6.94e-09 ***
## phi.N -1.7463527 0.3816550 -4.576 5.50e-06 ***
## R.deep -0.0026554 0.0016551 -1.604 0.10903
## R.med 0.0049837 0.0023334 2.136 0.03300 *
## SP -0.0016140 0.0008221 -1.963 0.04996 *
## density 1.7602255 0.2990153 5.887 5.79e-09 ***
## phi.core 0.0927539 0.0059493 15.591 < 2e-16 ***
## FaciesF10 0.0896953 0.0945929 0.948 0.34330
## FaciesF2 0.0152576 0.1523676 0.100 0.92026
## FaciesF3 -0.0292379 0.0869197 -0.336 0.73667
## FaciesF5 0.1022238 0.0879087 1.163 0.24524
## FaciesF7 0.2794793 0.1462763 1.911 0.05641 .
## FaciesF8 -0.0932936 0.0927473 -1.006 0.31477
## FaciesF9 -0.3877078 0.1030388 -3.763 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3339 on 802 degrees of freedom
## Multiple R-squared: 0.6787, Adjusted R-squared: 0.6722
## F-statistic: 105.9 on 16 and 802 DF, p-value: < 2.2e-16log_k.predicted_6 <-predict(model_6,data=data)
k.predicted_6<-10^log_k.predicted_6
plot(k.predicted_6,data$k.core)
rmse_6<- RMSE(k.predicted_6,data$k.core )
rmse_6## [1] 1330.932library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionset.seed(12345)
training<-sample_frac(data, .70)
testing<-anti_join(data,training)## Joining with `by = join_by(depth, caliper, ind.deep, ind.med, gamma, phi.N,
## R.deep, R.med, SP, density.corr, density, phi.core, k.core, Facies,
## log10_k.core)`model_7<- lm(log10_k.core~.-k.core,data=training)
summary(model_7)##
## Call:
## lm(formula = log10_k.core ~ . - k.core, data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.51913 -0.11889 0.02699 0.14920 0.74362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5280201 5.7011736 -0.093 0.926242
## depth 0.0006252 0.0005855 1.068 0.286112
## caliper -0.6495236 0.3324223 -1.954 0.051215 .
## ind.deep -0.0007610 0.0007845 -0.970 0.332465
## ind.med 0.0007045 0.0008609 0.818 0.413522
## gamma -0.0089894 0.0020495 -4.386 1.38e-05 ***
## phi.N -1.9657737 0.5087312 -3.864 0.000125 ***
## R.deep -0.0030975 0.0022302 -1.389 0.165427
## R.med 0.0050063 0.0031376 1.596 0.111149
## SP -0.0017209 0.0010366 -1.660 0.097434 .
## density.corr 1.9813037 1.6025476 1.236 0.216855
## density 1.8343569 0.3866623 4.744 2.67e-06 ***
## phi.core 0.0980669 0.0073769 13.294 < 2e-16 ***
## FaciesF10 0.0571707 0.1159350 0.493 0.622119
## FaciesF2 -0.0058529 0.1737794 -0.034 0.973144
## FaciesF3 -0.0899993 0.1104451 -0.815 0.415493
## FaciesF5 0.1011056 0.1172564 0.862 0.388918
## FaciesF7 0.2716534 0.1743201 1.558 0.119720
## FaciesF8 -0.1212214 0.1333494 -0.909 0.363719
## FaciesF9 -0.3658161 0.1448505 -2.525 0.011833 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3468 on 553 degrees of freedom
## Multiple R-squared: 0.6674, Adjusted R-squared: 0.6559
## F-statistic: 58.39 on 19 and 553 DF, p-value: < 2.2e-16log_k.predicted_7 <-predict(model_7,newdata=testing)
k.predicted_7<-10^log_k.predicted_7
plot(k.predicted_7,testing$k.core)
rmse_7<- RMSE(k.predicted_7,testing$k.core )
rmse_7## [1] 1438.566model_8<-step(model_7, direction = "backward")## Start: AIC=-1193.86
## log10_k.core ~ (depth + caliper + ind.deep + ind.med + gamma +
## phi.N + R.deep + R.med + SP + density.corr + density + phi.core +
## k.core + Facies) - k.core
##
## Df Sum of Sq RSS AIC
## - ind.med 1 0.0806 66.604 -1195.2
## - ind.deep 1 0.1132 66.636 -1194.9
## - depth 1 0.1371 66.660 -1194.7
## - density.corr 1 0.1839 66.707 -1194.3
## - R.deep 1 0.2320 66.755 -1193.9
## <none> 66.523 -1193.9
## - R.med 1 0.3063 66.829 -1193.2
## - SP 1 0.3316 66.855 -1193.0
## - caliper 1 0.4593 66.982 -1191.9
## - phi.N 1 1.7961 68.319 -1180.6
## - gamma 1 2.3142 68.837 -1176.3
## - density 1 2.7074 69.230 -1173.0
## - Facies 7 5.0032 71.526 -1166.3
## - phi.core 1 21.2588 87.782 -1037.0
##
## Step: AIC=-1195.17
## log10_k.core ~ depth + caliper + ind.deep + gamma + phi.N + R.deep +
## R.med + SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - ind.deep 1 0.1082 66.712 -1196.2
## - R.deep 1 0.2109 66.815 -1195.4
## - density.corr 1 0.2172 66.821 -1195.3
## <none> 66.604 -1195.2
## - depth 1 0.2471 66.851 -1195.0
## - R.med 1 0.2786 66.882 -1194.8
## - SP 1 0.2947 66.898 -1194.6
## - caliper 1 0.4181 67.022 -1193.6
## - phi.N 1 1.7655 68.369 -1182.2
## - gamma 1 2.2398 68.843 -1178.2
## - density 1 2.7565 69.360 -1173.9
## - Facies 7 6.4262 73.030 -1156.4
## - phi.core 1 21.2026 87.806 -1038.8
##
## Step: AIC=-1196.24
## log10_k.core ~ depth + caliper + gamma + phi.N + R.deep + R.med +
## SP + density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - depth 1 0.1500 66.862 -1197.0
## - density.corr 1 0.2125 66.924 -1196.4
## - R.deep 1 0.2297 66.942 -1196.3
## <none> 66.712 -1196.2
## - SP 1 0.3316 67.043 -1195.4
## - R.med 1 0.3646 67.076 -1195.1
## - caliper 1 0.6584 67.370 -1192.6
## - phi.N 1 1.9869 68.699 -1181.4
## - gamma 1 2.3870 69.099 -1178.1
## - density 1 2.9356 69.647 -1173.6
## - Facies 7 6.3728 73.085 -1158.0
## - phi.core 1 21.1606 87.872 -1040.4
##
## Step: AIC=-1196.95
## log10_k.core ~ caliper + gamma + phi.N + R.deep + R.med + SP +
## density.corr + density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - R.deep 1 0.1759 67.038 -1197.5
## <none> 66.862 -1197.0
## - R.med 1 0.2540 67.116 -1196.8
## - density.corr 1 0.2619 67.124 -1196.7
## - SP 1 0.3184 67.180 -1196.2
## - phi.N 1 1.8370 68.699 -1183.4
## - caliper 1 1.9149 68.777 -1182.8
## - density 1 3.3795 70.241 -1170.7
## - gamma 1 3.6755 70.537 -1168.3
## - Facies 7 8.9870 75.849 -1138.7
## - phi.core 1 21.7187 88.580 -1037.8
##
## Step: AIC=-1197.45
## log10_k.core ~ caliper + gamma + phi.N + R.med + SP + density.corr +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## - R.med 1 0.1324 67.170 -1198.3
## <none> 67.038 -1197.5
## - density.corr 1 0.2593 67.297 -1197.2
## - SP 1 0.2599 67.298 -1197.2
## - caliper 1 1.7731 68.811 -1184.5
## - phi.N 1 2.0040 69.042 -1182.6
## - density 1 3.2337 70.271 -1172.5
## - gamma 1 3.5085 70.546 -1170.2
## - Facies 7 9.1381 76.176 -1138.2
## - phi.core 1 22.1867 89.224 -1035.6
##
## Step: AIC=-1198.32
## log10_k.core ~ caliper + gamma + phi.N + SP + density.corr +
## density + phi.core + Facies
##
## Df Sum of Sq RSS AIC
## <none> 67.170 -1198.3
## - density.corr 1 0.2594 67.429 -1198.1
## - SP 1 0.3343 67.504 -1197.5
## - caliper 1 1.9581 69.128 -1183.8
## - phi.N 1 1.9810 69.151 -1183.7
## - density 1 3.1498 70.320 -1174.1
## - gamma 1 3.8279 70.998 -1168.6
## - Facies 7 13.3735 80.544 -1108.3
## - phi.core 1 22.2762 89.446 -1036.2summary(model_8)##
## Call:
## lm(formula = log10_k.core ~ caliper + gamma + phi.N + SP + density.corr +
## density + phi.core + Facies, data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.52008 -0.11669 0.02277 0.14991 0.72044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4633388 2.2347879 2.445 0.01481 *
## caliper -0.9314084 0.2309360 -4.033 6.27e-05 ***
## gamma -0.0099123 0.0017578 -5.639 2.72e-08 ***
## phi.N -1.9340484 0.4767572 -4.057 5.69e-05 ***
## SP -0.0016636 0.0009983 -1.666 0.09619 .
## density.corr 2.3185618 1.5795902 1.468 0.14272
## density 1.8928532 0.3700391 5.115 4.31e-07 ***
## phi.core 0.0987031 0.0072557 13.603 < 2e-16 ***
## FaciesF10 0.0772945 0.1144729 0.675 0.49982
## FaciesF2 -0.0060105 0.1716877 -0.035 0.97209
## FaciesF3 -0.0932764 0.1075241 -0.867 0.38604
## FaciesF5 0.1511626 0.1090306 1.386 0.16617
## FaciesF7 0.2790714 0.1647283 1.694 0.09080 .
## FaciesF8 -0.0712826 0.1147451 -0.621 0.53470
## FaciesF9 -0.3250663 0.1080541 -3.008 0.00275 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.347 on 558 degrees of freedom
## Multiple R-squared: 0.6641, Adjusted R-squared: 0.6557
## F-statistic: 78.81 on 14 and 558 DF, p-value: < 2.2e-16log_k.predicted_8 <-predict(model_8,newdata=testing)
k.predicted_8<-10^log_k.predicted_8
plot(k.predicted_8,testing$k.core)
rmse_8<- RMSE(k.predicted_8,testing$k.core )
rmse_8## [1] 1496.65