25 - Semi-Log Crossplot Regression - Poro-Perm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00d8268e",
   "metadata": {},
   "source": [
    "## Import the Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "375bc2d6-9f42-4c98-848a-d06a68554056",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98e8793f-b20e-42c5-96e3-31662ecb8004",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/15_9-19A-CORE.csv\", na_values=' ', usecols=['DEPTH', 'CPOR', 'CKHL', 'CGD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c2d1e61-58f8-4b39-8fce-607cd1a299c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 728 entries, 0 to 727\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DEPTH   728 non-null    float64\n",
      " 1   CKHL    557 non-null    float64\n",
      " 2   CPOR    593 non-null    float64\n",
      " 3   CGD     594 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 22.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd4a74d4-de44-4e26-8c7b-4a65a356c2fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 557 entries, 0 to 727\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DEPTH   557 non-null    float64\n",
      " 1   CKHL    557 non-null    float64\n",
      " 2   CPOR    557 non-null    float64\n",
      " 3   CGD     557 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 21.8 KB\n"
     ]
    }
   ],
   "source": [
    "#Drop nans\n",
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdf6529",
   "metadata": {},
   "source": [
    "## Visualise our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a59673a-1561-4a8d-a2b8-2641cacb3dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x136580f70>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.scatter(x=df['CPOR'], y=df['CKHL'], c=df['CGD'], cmap='YlOrRd', s=50)\n",
    "plt.semilogy()\n",
    "plt.ylabel('Core Permeability (mD)', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Core Porosity (%)', fontsize=12, fontweight='bold');\n",
    "plt.colorbar(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e610ebc8",
   "metadata": {},
   "source": [
    "## Build a Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02d1df19-f3eb-4057-b274-11d31f2eabaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6faae4d8-17a4-4979-bf41-16b41ad761a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['CPOR'].values\n",
    "y = np.log10(df['CKHL'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd0ef547-9af6-4d91-a9f0-988d1d232680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(557,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff4adae6-84ca-46f7-b730-3f36743aad7e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = x.reshape(-1,1)\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a20d373-1faf-4191-8b0c-04a3e25eb857",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cefbdcf6-cf01-45c0-886f-374d64b12c20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8eda687a-c57d-43bb-ab7f-7ed621eed842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7104407167237579"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = model.score(x, y)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02840831-1a80-492f-957e-7dc8af992808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.79142803])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "579de921-1655-4afc-b666-5fd0b8bc3b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18299988]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f366d608-fe53-4117-9fc3-d8b4dcdae322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10**(0.1830 * CPOR + (-1.7914))\n"
     ]
    }
   ],
   "source": [
    "regression_eq = f'10**({model.coef_[0][0]:.4f} * CPOR + ({model.intercept_[0]:.4f}))'\n",
    "print(regression_eq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc0b9e5c-e1f3-49eb-809b-2d32f787e25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_plot_vals = np.arange(0, 50)\n",
    "y_pred = model.predict(x_plot_vals.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "783e3a19-deda-431e-a32d-b10b81fee95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_log = 10**y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7df1b4d0-5a09-4647-a8b4-ea5f292fb711",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>por_vals</th>\n",
       "      <th>perm_vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.616486e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.463609e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.754670e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.722313e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>8.721104e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.329142e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2.025682e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>3.087245e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4.705123e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>7.170854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.092876e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1.665600e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2.538461e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>3.868747e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>5.896173e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>8.986076e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1.369525e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2.087228e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>3.181044e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>4.848078e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>7.388725e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>1.126080e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>1.716205e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>2.615587e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>3.986291e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>6.075316e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>9.259099e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>1.411135e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>2.150644e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>3.277694e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>4.995377e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>7.613215e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>1.160294e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>1.768348e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>2.695056e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>4.107406e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>6.259901e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>9.540417e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>1.454009e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>2.215986e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>3.377279e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>5.147150e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>7.844526e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>1.195547e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>1.822076e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>2.776939e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>4.232200e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>6.450095e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>9.830282e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>1.498186e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    por_vals     perm_vals\n",
       "0          0  1.616486e-02\n",
       "1          1  2.463609e-02\n",
       "2          2  3.754670e-02\n",
       "3          3  5.722313e-02\n",
       "4          4  8.721104e-02\n",
       "5          5  1.329142e-01\n",
       "6          6  2.025682e-01\n",
       "7          7  3.087245e-01\n",
       "8          8  4.705123e-01\n",
       "9          9  7.170854e-01\n",
       "10        10  1.092876e+00\n",
       "11        11  1.665600e+00\n",
       "12        12  2.538461e+00\n",
       "13        13  3.868747e+00\n",
       "14        14  5.896173e+00\n",
       "15        15  8.986076e+00\n",
       "16        16  1.369525e+01\n",
       "17        17  2.087228e+01\n",
       "18        18  3.181044e+01\n",
       "19        19  4.848078e+01\n",
       "20        20  7.388725e+01\n",
       "21        21  1.126080e+02\n",
       "22        22  1.716205e+02\n",
       "23        23  2.615587e+02\n",
       "24        24  3.986291e+02\n",
       "25        25  6.075316e+02\n",
       "26        26  9.259099e+02\n",
       "27        27  1.411135e+03\n",
       "28        28  2.150644e+03\n",
       "29        29  3.277694e+03\n",
       "30        30  4.995377e+03\n",
       "31        31  7.613215e+03\n",
       "32        32  1.160294e+04\n",
       "33        33  1.768348e+04\n",
       "34        34  2.695056e+04\n",
       "35        35  4.107406e+04\n",
       "36        36  6.259901e+04\n",
       "37        37  9.540417e+04\n",
       "38        38  1.454009e+05\n",
       "39        39  2.215986e+05\n",
       "40        40  3.377279e+05\n",
       "41        41  5.147150e+05\n",
       "42        42  7.844526e+05\n",
       "43        43  1.195547e+06\n",
       "44        44  1.822076e+06\n",
       "45        45  2.776939e+06\n",
       "46        46  4.232200e+06\n",
       "47        47  6.450095e+06\n",
       "48        48  9.830282e+06\n",
       "49        49  1.498186e+07"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame({'por_vals': x_plot_vals, 'perm_vals': y_pred_log.flatten()})\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca42eeb-da7a-40dc-8fb1-01b4286f9089",
   "metadata": {},
   "source": [
    "## Plot the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4737df7f-659b-4687-8c2f-fbd34beea98e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13f156e90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.scatter(x=df['CPOR'], y=df['CKHL'], c=df['CGD'], cmap='YlOrRd', s=50)\n",
    "plt.plot(results_df['por_vals'], results_df['perm_vals'], color='black')\n",
    "plt.semilogy()\n",
    "plt.ylabel('Core Permeability (mD)', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Core Porosity (%)', fontsize=12, fontweight='bold');\n",
    "plt.colorbar(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2535cc99-1393-468b-9b69-e2f4ed272e2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}