Within the oil industry, a mathematical or measurable device that’s utilized to demonstrate or portray the spatial dissemination of these properties may be called a heterogeneity work. This include might make it less demanding for geoscientists and petroleum engineers to comprehend and degree the varieties in liquid and shake characteristics all through the store. For successful store administration and oil recuperation methods, an understanding of heterogeneity is basic.
A. Dykastra-Parson Coefficient.
B. Lorentz Coefficient. (not required)
The Dykstra-Parsons method involves plotting the frequency distribution of the permeability on a log-normal probability graph paper. This is done by arranging the permeability values in descending order and then calculating for each permeability, the percent of the samples with permeability greater than that value. to avoid values of zero or 100%, the percent greater than or equal to value is normalized by n+1, where n it is the number of samples.
Let’s Import data
```r
data=read.csv(\karpur.csv\)
head(data)
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Now we will sorting permeability values in a descending order, **Then** finding samples \>= k portions
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```r
```r
data = data[order(data$k.core, decreasing=TRUE), ]
k = data$k.core
sample = c(1: length(k))
k_percent = (sample * 100 / length(k))
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Now let's **plot** the output:
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```r
```r
xlab = \Portion of Total Samples Having Larger or Equal K\
ylab = \Permeability k (md)\
plot(k_percent, k, log = 'y', xlab = xlab, ylab = ylab, pch = 10, cex = 0.5, col = \#16423C\)
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<img 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" />
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OK, Let's **get linear model** is fitted to the data:
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubG9nLmsgPSBsb2coaylcbm1vZGVsID0gbG0obG9nX2sgfiBrX3BlcmNlbnQpXG5wbG90KGtfcGVyY2VudCwgbG9nX2ssIHhsYWIgPSB4bGFiICwgeWxhYiA9IHlsYWIsIHBjaCA9IDEwLCBjZXggPSAwLjUsIGNvbCA9IFxcIzE2NDIzQ1xcKVxuYWJsaW5lKG1vZGVsLCBjb2wgPSBcXHJlZFxcLCBsd2QgPSAyKVxuYGBgXG5gYGAifQ== -->
```r
```r
log.k = log(k)
model = lm(log_k ~ k_percent)
plot(k_percent, log_k, xlab = xlab , ylab = ylab, pch = 10, cex = 0.5, col = \#16423C\)
abline(model, col = \red\, lwd = 2)
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<img 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WGDv5AykKlCy6Yp31+dacWPK4cfr07KQKbGU35a7ag6Ln6KyCz3My1IGT1Xflo2/KCZkTr59XLw7ScVn/VlCSmj70q3bOHEzfA0kQmf5AjY1HjKwVbvRF0spt4xVxEBLCrbsukKdpDyU5BygXOwKy4C6KmSLVs4BzuckUu9M6rcIoB+ajblMGF/pJ4/Y1sZsKvZlBfTrz4u5pdKHXEVEcCucudv2jnb64vl6/SRMlCyZRspW/1git2LAPqn3EWxTVOOTsOcf2X1DBFSBhbNzsr+SB2GKQ852wuwrkzLhilP1IU+pFzggkBVFwH0V4mWTU8RGbzXKXOKCFCHplKOPs2RlIG6FG7Z0qzMOdhALQpf4N74xE29rRy+2bH8AxVbBNBrDe32ml+qQ2/w+/zrglRfBIACjI8r3+hriJzbPd2LlIGSLJzt9eXhwcpQMhYBIEfjV9x0ZRGALMYr2Lc/lLikfaVFAMhnesVNr8QHzVRbBIACjK+4efQ2+qTVzI9zM1gE0HMNne1l920UOxYB9FvRc0QsXKavBqQMxJo5cXMxiT8q3TJSBmLNzMqLxaffnbOtDNSokW3lhf+GPdiAC4zfTjEo81HpVRYBoAD2YAMiWPjMqBqQMlASe7ABEUz3YE8Grx40qxfDJmWgJNMVbMUebMAFhmd7fXN9zR5swAG8XxkQwXBWfvPC8ge/bS0CQBEcjAJEML8Otr2x7FwEgCIMt5Vnl0dvORgFtI+DUYAIHIwCROBgFCCCjZStbibvXgSAbMbXwf4+/Lyo+Vd2d2STMlCS6VVERuowTNny+5ZJGSjJ+E2O+kNZF098KCvQKksflW75tC9SBkoyP3GTlAEHWJqVp94xx5WBFhlfcVNvK/sjtpWBVhmmPL9Uh97g957d8zZJGSjL+LjyjT4D+9zu25ZJGSjJwtleXx4erAwlYxEAcpil/OvtB3tD2b0IAEWYpPzJC1atB69sDmdzEQCKMUj5KdhEvn2jil/U3r85O3seTeM5x6FJGSipesr+SEc8KXwYan6p95DpC/uRMmBX9ZTjGoufHDJWx3eL2UiFtydloKhiH7BsnHLhUzaXl/S7CadxUgYKOtX/y9VcyqsbjtXFrnuppPKjAoQ6PS00LTeZcvye5nAjm1kZKMi5WVnPxvE91b+RMlBQ/dvKg7e3t7c/evqX259yd35NPXUc9RvuyyZloKAiJRulnNq6LTA3zy6XN/JvSBmwyuC48ufbpPxZOcX/R9btSRkoietgA66reQ92nUgZWCm2C5uUAccVO7BMyoDjmJUBGdhWBmSo9xSROpEykMC2MiBBoY1lUgZcV2gXNikDrmNWBmRgWxmQgYNRQF+QMiACKQMikDIgAikDHcAebEACThEBJChyjggpA84rcuYmKQPOOy2whk3KgPvCknNaJmXAfQXWsEkZcF+B/V6kDHQA28pAT5AyIAIpAyKQMiACKQMikDIgAikDncDBKEACzvYCJMg/3YuUgQ7IPwmblIEOYFYGZGBbGegHUgZEIGVABFIGRCBlQARSBkQgZUAEUgZEIGVABFIGRCBlQARSBkQgZUAEUgZEIGVABFIGRCBlQARSBkQgZUCEplP2Hx4eHutdBNBHjaZ8f6Uiz9/VtQigpxpM2R8pNXh+fX195Sl1nDk1kzJQUoMpT9TBXfzlbKgu6lgE0FvNpeyPBu9Xv5l6mdMyKQMlNZfyfHjwcfdv4odNqrYIoL9ampWfsjeWSRkoqdFt5cFyx/W9x7YyYFWTB6NugjXnw7Ozs2fBry/qWQTQV40eV569eaa3hA/P77JvSMpASZy4CXQCn68MSJD7AcukDHTB6WnOtEzKQBcwKwMysK0M9AIpAyKQMiACKQMikDIgAikDIpAyIAIpAyKQMiACKQMikDIgAikDIpAyIAIpAyKQMiACKQMikDIgAikD3cC1vQAJ8i7uRcpAF5zmXXKTlIFOYFYGZGBbGegDUgZEIGVABFIGRCBlQARSBkQgZUAEUgZEIGVABFIGRCBlQARHUwZQUoXO7KfbqeXv5erAGFc5ro7L/sDa/pu2vfy9XB0Y4yrH1XGRcmNcHRjjKsfVcZFyY1wdGOMqx9VxkXJjXB0Y4yrH1XGRcmNcHRjjKsfVcZFyY1wdGOMqx9VxkXJjXB0Y4yrH1XGRcmNcHRjjKsfVcZFyY1wdGOMqx9VxkXJjXB0Y4yrH1XGRcmNcHRjjKsfVcclLGYAVpAyIQMqACKQMiEDKgAikDIhAyoAIpAyIQMqACKQMiEDKgAikDIhAyoAIpAyIQMqACKQMiNBmyv73nhp8/djiCLbNrpQavNBjcm54T+p1+Itb4/rkKXXu3vPl33gO/jvOh/pfMDkke6NrM+Wx/si6kxZHsGXq6TEdh0+ta8ObD6OUnRpXNJiDjwu3xuWPXPx3DEYVpZwYkr3RtZjy1Du4C/4zeN/eEDYFz/UfHxezS3Xh4PCCf/TwheDUuJ7U0d1iNnLu+YrGpX/2uTOu8OeLTjkxJIujazHlsf57PTnx8zI2H+rBTL0T94b3pM70iFwalz/Sr8GpF0x/Lo1rMYkHc+HQ8/WzNzhbrlethmRxdO2l7I/0atl8eOzEZkxSOCbXhhf8lNGvT6fGFf/oCzk1rkTKzozLHx3dRcNKDMnm6NpLOR5+/JdxSvhD0rXhjQ8+6heCU+OaehfTy2i3l1PjCgYWrmBfBusM7ozrYfkTJjEkm6Mj5W16tdGx4U291wv3Ug5X+uPdXk6NK1ybDYY1eOXY8yU25WjlbOzCc5wyDjex3BqePwr+yeOUHRrXk1LHerfXiVvjWvg3+kfMC8eer2XKqyHZHB2z8iZ/rPdBuDW8Sbh7ycVZOd7Uszu/mJvEP2Iu3Hq+xM7KDj3Ha/5I6TMLnBpesE26cDNlPanYflEaC3+2LBz8ESM0ZWd2LabMhuHa9cKx4U1U7OCjU+PSR+2i58qpcSXWW50al9A92O4c8EuYD8NdJZpLw0uk7NS4gqfr/SJ+Jbo1rtWs7NS4JjKPKwfjD14HUy8+l80N4/Vo3Bve8mCpQ+OarM/2cmpc49XuOKfGFaecGJLF0XEOdlJ8CnZ08q5zw1v9THdnXG6e66zPV3fx3PCJ0HOwXXrLSuxJJVJ2bngTB98ZpQfj3DuQgsG8UQ6+Y2uZsrh3RgGwhpQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUs01U5Pnd7u/7H4L/Hx18LPp480ulTvRXY7VysvWQyd8mH/3+KhzLu6JLyx3aePBe/zr1TnZ+P+/+T+p14bGUMdnz3OySGOMkHs7UU69qGZfLSDnb6jUVv+Y3zC5PyqUcBnyx+mrXy1U/ZELy0SeFX9877rxzOCJTng/j57hXSDlb/OLwb3a/pvYlsE/wskv+SJgPjx/zHjLxSp16g3Cy+eWycECmKeepL+Xij7uVcrDm08OSSTnH8jW1p4nyKafaLZnyev2x6FL7mLI/6mXJpJxj9Zoah68X/8ZTgxdhfZOD/7lUB38PVwFfR6+l1feCXIN5c/DX9YOsvvUU3j5R7zrl9E2CZc7CreKjdztTjiRv8b83Sr1Y+G+C/z6GYw1/H27eL4cWbCB8/RgvRp2vt/vTKa8eMfrjYHT/DO6f/PvMwi8S3SdTXt07fnLeJ2+9HsLymxt/7/Sfb/xNl48T/XWiZ23fExSU/KLQP600pJwtNSuH+6zC18/H8IX3pyDKn9cpr78XvJa01dSw/tbelDdu8jr6RX+VXsFe7/FK3eJP4VevRvFix9Hvgx6joQ3VcrnRBvp6pk6lvH7E6G/9pC58nfLq7zP1wi/+sDPl9b3jJ+cxcevEEOJvbj416T9Ppbx+nETKO5+g8G7j4nsSZCHlbPFr6svfwhfIWB3fLWaj8MtJtB9MJ6BfS+vvBS/94zv/u3WyibvtW8FO3CR+yDBaf6xOtnZ7Hf5FT6qpW6iDu0XwYyV4jE/hYscq2KZe33kcTtXBtHgRLO/oLvzGqpP1vreT5CNGM2nQeZxy/PcJJ7zHcEE7Uk7cO35ykrdeD2H5zG09Nak/T+5tTDzOOuXdT1Dwr3Wjfy70ESlnS76m5kP9mtG/TKJJd5Vy4nvBq+x9ct05ebc9KSdvktxsfdpIefElXEFWg1cbt4g2EaPF6nbDwKIMw6HpBwwXPfUO0gfVkimnHjEYV1RM8u8z9fRwJ7tSTtw7fnISt04MYfnMbT01yT9PPe2Jx0muYO96gibqrNBOb5FIOVv8mhqE25fLyMK10ni2XqWc+N7m6y15tz0pJ2+y/PrL5x/+7G2mHPDv36h4VWF1i2gS1JvFehqO7hGPJVo/1evVel35PHHcemO31/IRF5Pgz4P169WPg2ioT4mfX5FUyut7L9fPl7dODCG17rzjGV0+7evfJB4n9dTueIKCf62TYF2+nl1xriPlbDkvvNpSnl2u13u3dkLreWrzFtspT6LN+OVWZdhRuGMs2DRdrcmmUl4/YvD7i3i8hVNO3Hsr5eQQLKW88wmaKL0VvfscAOlIOVtLKQfT59HL2w/p9cfVQ8ZbsOlbZMzKyTVO/4erxJ63ZMqJRwy+PtFLK55y8t47ZuX1EOykvPMJiu82VqknuS9IOVvyNZXeskunnNpWTqdcZVs5fo1PUq/UaJs4vt3WLZIpR2dKDJfbyhvT+vxy9QfJlBOPGO4E/6+woHTKWdvKyXtHT05qWzm56y7zGd11o41t5fCBdz5B8d38US83l0k5W+oFltrfGqccT12pPdgb+2bK7sEO/kzvnwqPxaZeqU/RodlZeDbT1i1SKQ/eJcaiH3zxsxdOq+FX997OWTnxiMGiBs+WG+CpqfCPj8ED7U55fe9VUatbr4eQ94zufNpP1o9zEd483oG39QSt/lX6uLlMytlSr6n46Gj4ylmf7ateJQ/e6pl3I+XE3falnLhJ/JDxxmW0E3l5+5vVny62bpFMefAsOZb4waPjOvHB2NjGCvbq0cP7HD9uprzafZVIOfY6ee91Uctbr4eQ94wun/bE1nV0o8PVoe9/GemfKjueoOVjTBKHznuDlLOlTztKnJsU//knLzqPInW210bKibvtPXFzfRP9kHr/1OD8v1fr67H7qyCPw+jErY1bpFL+Nj7vK/7DN8tzvPTZXkfr80xSu73Wj7iI5r+tlPXOpqMfvdVho3XKyXsvn5zErddDyHtG46c9kXJ0FtsvepU6eHLO/xk+iTufoPW5eT3cXCZlgcb17sJ9KnWOc7lb71f1NPHeIGWB6ko5OsNkdlns4cvdusDDkXImUhaorpSXG6jF3q5Q7ta5SDkHKQtU2wq23tQ+LHqBjnK3zkPKOUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRCBlAERSBkQgZQBEUgZEIGUARFIGRDh/wEOKFXwpMo4LAAAAABJRU5ErkJggg==" />
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Result we can calculated the heterogeneity index (`HI`) by:\
***V=(k50−k84.1)/k50***
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuTl9kYXRhID0gZGF0YS5mcmFtZShrX3BlcmNlbnQgPSBjKDUwLCA4NC4xKSlcbnByZWRpY3RfdmFsdWVzICAgID0gcHJlZGljdChtb2RlbCwgTl9kYXRhKVxuSEkgPSAocHJlZGljdF92YWx1ZXNbMV0gLSBwcmVkaWN0X3ZhbHVlc1syXSkgLyBwcmVkaWN0X3ZhbHVlc1sxXVxuSElcbmBgYFxuYGBgIn0= -->
```r
```r
N_data = data.frame(k_percent = c(50, 84.1))
predict_values = predict(model, N_data)
HI = (predict_values[1] - predict_values[2]) / predict_values[1]
HI
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1 0.2035464 ```
HI = 0.204 , which refers to slightly heterogeneous
class of permeability distribution.