Q8.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q8: “Geologists say that geology is not random, however, petroleum engineers say that it is random based on their experience” Given several well logs for the same reservoir (spatial domain), can we infer that there is no relation between the well location and its quality?\n",
    "    a. Select several wells in the same reservoir\n",
    "    b. Calculate the mean and standard deviation of each well of the well lithology\n",
    "    c. Compare the mean and standard deviation of each well (hypothesis testing using chi-square test)\n",
    "\n",
    "**Purpose of the question**: test the truth of the hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lasio\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from utils import rename_columns, My_Columns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./Data/labeled_logs.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('15/9-13', '15/9-15', 8018.920418328456), ('15/9-17', '16/7-5', 13064.735973910458), ('16/1-6 A', '16/1-2', 7508.240249055258), ('16/10-2', '16/10-1', 9470.38845068875), ('16/10-5', '16/10-1', 7739.423407487112), ('16/2-11 A', '16/2-6', 2994.389226547374), ('16/2-6', '16/2-16', 3122.3630805280036), ('16/5-3', '16/2-7', 7752.661582742514), ('16/7-5', '16/7-4', 8049.546991080168), ('17/11-1', '16/11-1 ST3', 48239.341451848224), ('25/11-19 S', '25/11-5', 2344.207036041971), ('25/2-13 T4', '25/2-14', 8218.18210692661), ('25/2-7', '25/3-1', 7942.970993529315), ('25/4-5', '25/8-5 S', 15959.5895240032), ('25/5-4', '25/5-1', 14482.04166976476), ('25/6-2', '25/6-1', 4091.903411267013), ('25/7-2', '25/11-5', 12029.000143812464), ('25/8-7', '25/9-1', 8142.153506129872), ('26/4-1', '25/6-3', 11445.412301766784), ('30/3-3', '30/3-5 S', 2641.688805368078), ('30/6-5', '30/3-5 S', 11614.877433568514), ('31/2-19 S', '31/2-9', 6795.909762936333), ('31/2-8', '35/11-10', 6873.989765489002), ('31/3-1', '31/3-3', 8522.584271973617), ('31/3-3', '31/3-2', 9845.950418345456), ('31/4-10', '31/4-5', 10342.644417516609), ('31/5-4 S', '31/2-1', 5636.522311151899), ('31/6-8', '31/6-5', 9910.681639318667), ('33/5-2', '33/6-3 S', 19709.229247558662), ('33/9-1', '34/7-21', 13288.45741376948), ('34/10-19', '34/10-33', 7135.129363385049), ('34/10-33', '34/10-21', 3708.8018177406734), ('34/11-1', '34/10-35', 10016.143831151529), ('34/12-1', '34/11-2 S', 32997.89214849829), ('34/3-1 A', '34/2-4', 8388.42654715567), ('34/5-1 A', '34/5-1 S', 1335.4446030235204)]\n"
     ]
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "unique_wells = df[\"WELL\"].unique()\n",
    "\n",
    "mean_x_y = df.groupby(\"WELL\").mean()[[\"X_LOC\", \"Y_LOC\"]]\n",
    "\n",
    "unique_wells_list = list(unique_wells)\n",
    "closest_well = []\n",
    "for well in unique_wells_list:\n",
    "    min_dist = np.inf\n",
    "    closest = \"\"\n",
    "    for well2 in unique_wells_list:\n",
    "        if well != well2:\n",
    "            dist = distance.euclidean(mean_x_y.loc[well], mean_x_y.loc[well2])\n",
    "            if dist < min_dist:\n",
    "                min_dist = dist\n",
    "                closest = well2\n",
    "    closest_well.append((well, closest, min_dist))\n",
    "    unique_wells_list.remove(well2)\n",
    "    unique_wells_list.remove(well)\n",
    "print(closest_well)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for well, well2, _ in closest_well:\n",
    "    unique_color = np.random.rand(\n",
    "        3,\n",
    "    )\n",
    "    plt.scatter(\n",
    "        mean_x_y.loc[well][\"X_LOC\"], mean_x_y.loc[well][\"Y_LOC\"], color=unique_color\n",
    "    )\n",
    "    plt.scatter(\n",
    "        mean_x_y.loc[well2][\"X_LOC\"], mean_x_y.loc[well2][\"Y_LOC\"], color=unique_color\n",
    "    )\n",
    "plt.title(\"Closest Wells Based on Euclidean Distance\")\n",
    "plt.xlabel(\"X_LOC\")\n",
    "plt.ylabel(\"Y_LOC\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of wells that have different lithology distribution is 0\n"
     ]
    }
   ],
   "source": [
    "# the hypothesis is that the two wells are from the lithological distribution\n",
    "from scipy.stats import ks_2samp\n",
    "\n",
    "count_different = 0\n",
    "\n",
    "for well, well2, _ in closest_well:\n",
    "    # find the lithology of each well\n",
    "    lithology1 = df[df[\"WELL\"] == well][\"LITHOLOGY\"]\n",
    "    lithology2 = df[df[\"WELL\"] == well2][\"LITHOLOGY\"]\n",
    "    # find the distribution of lithology\n",
    "    lithology1_distribution = lithology1.value_counts(normalize=True)\n",
    "    lithology2_distribution = lithology2.value_counts(normalize=True)\n",
    "    # find the p-value\n",
    "    p_value = ks_2samp(lithology1_distribution, lithology2_distribution).pvalue\n",
    "    if p_value < 0.05:\n",
    "        print(\"The two wells have different lithology distribution\")\n",
    "        count_different += 1\n",
    "print(\n",
    "    f\"Number of wells that have different lithology distribution is {count_different}\"\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that the lithology of the close well have the same distribution\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}