nodal analysis for gas well.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e673b76f-6365-4332-aca7-74aa0f8c1cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c80a1b93-3f04-4395-b85d-59a5bdb12cc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Pwf</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3500.0</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>3094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>4512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>4963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>5275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>500.0</td>\n",
       "      <td>5458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.7</td>\n",
       "      <td>5519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pwf     Q\n",
       "0  4000.0     0\n",
       "1  3500.0  1999\n",
       "2  3000.0  3094\n",
       "3  2500.0  3902\n",
       "4  2000.0  4512\n",
       "5  1500.0  4963\n",
       "6  1000.0  5275\n",
       "7   500.0  5458\n",
       "8    14.7  5519"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First Let's Get the IPR dataset. (Source : PetroWiki)\n",
    "\n",
    "ipr = pd.DataFrame({'Pwf':[4000,3500,3000,2500,2000,1500,1000,500,14.7],\n",
    "                    'Q': [0,1999,3094,3902,4512,4963,5275,5458,5519]})\n",
    "\n",
    "ipr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e335a2ab-e5e6-4043-a02e-cfc231a4be1d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>q</th>\n",
       "      <th>Pwf(1.90\" tbg)</th>\n",
       "      <th>Pwf(2.375\"tbg)</th>\n",
       "      <th>Pwf(2.875\"tbg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>1334</td>\n",
       "      <td>1298</td>\n",
       "      <td>1286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>1400</td>\n",
       "      <td>1320</td>\n",
       "      <td>1294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>1487</td>\n",
       "      <td>1351</td>\n",
       "      <td>1305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2500</td>\n",
       "      <td>1592</td>\n",
       "      <td>1390</td>\n",
       "      <td>1319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>1712</td>\n",
       "      <td>1435</td>\n",
       "      <td>1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3500</td>\n",
       "      <td>1843</td>\n",
       "      <td>1487</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4000</td>\n",
       "      <td>1984</td>\n",
       "      <td>1545</td>\n",
       "      <td>1378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4500</td>\n",
       "      <td>2132</td>\n",
       "      <td>1609</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5000</td>\n",
       "      <td>2287</td>\n",
       "      <td>1677</td>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5500</td>\n",
       "      <td>2446</td>\n",
       "      <td>1749</td>\n",
       "      <td>1461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6000</td>\n",
       "      <td>2609</td>\n",
       "      <td>1824</td>\n",
       "      <td>1493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       q  Pwf(1.90\" tbg)  Pwf(2.375\"tbg)  Pwf(2.875\"tbg)\n",
       "0   1000            1334            1298            1286\n",
       "1   1500            1400            1320            1294\n",
       "2   2000            1487            1351            1305\n",
       "3   2500            1592            1390            1319\n",
       "4   3000            1712            1435            1336\n",
       "5   3500            1843            1487            1356\n",
       "6   4000            1984            1545            1378\n",
       "7   4500            2132            1609            1403\n",
       "8   5000            2287            1677            1431\n",
       "9   5500            2446            1749            1461\n",
       "10  6000            2609            1824            1493"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now TPR Data\n",
    "\n",
    "q = np.arange(1000,6500,500)\n",
    "\n",
    "p_190 = [1334,1400,1487,1592,1712,1843,1984,2132,2287,2446,2609]\n",
    "\n",
    "p_2375 = [1298,1320,1351,1390,1435,1487,1545,1609,1677,1749,1824]\n",
    "\n",
    "p_2875 = [1286,1294,1305,1319,1336,1356,1378,1403,1431,1461,1493]\n",
    "\n",
    "tpr = pd.DataFrame({'q':q, 'Pwf(1.90\" tbg)':p_190, 'Pwf(2.375\"tbg)': p_2375,'Pwf(2.875\"tbg)':p_2875})\n",
    "tpr     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f2b4a40-0705-424e-b3ff-74d744a959f1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a406d8c490>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's Plot them now. \n",
    "plt.figure(figsize=(9,5))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(ipr['Q'], ipr['Pwf'], label = 'IPR', linewidth=1.5)\n",
    "\n",
    "plt.plot(tpr['q'],tpr['Pwf(1.90\" tbg)'],label='TPR for 1.9\" tubing', lw=1.5)\n",
    "plt.plot(tpr['q'],tpr['Pwf(2.375\"tbg)'],label='TPR for 2.375\" tubing', lw=1.5)\n",
    "plt.plot(tpr['q'],tpr['Pwf(2.875\"tbg)'],label='TPR for 2.875\" tubing', lw=1.5)\n",
    "plt.xlabel('FlowRate Q(Mscf/D)')\n",
    "plt.ylabel('Flowing BHP(Psia)')\n",
    "plt.title('Nodal Analysis for a Gas Well')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a899d86-c5aa-4cea-a66b-9702c35122c5",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}