tutorials/05.Tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tables\n",
    "\n",
    "A tutorial on `Tables` component that contains PVT properties of the geological model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raquired imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "\n",
    "sys.path.append('..')\n",
    "\n",
    "from deepfield import Field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load model (this is again the HDF5 model created in the first [tutorial](./01.Basics.ipynb)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:Field:===== Field summary =====\n",
      "INFO:Field:AQUIFERS attributes: \n",
      "INFO:Field:GRID attributes: MAPAXES, ACTNUM, COORD, DIMENS, ZCORN\n",
      "INFO:Field:ROCK attributes: PERMX, PERMY, PERMZ, PORO\n",
      "INFO:Field:STATES attributes: PRESSURE, RS, SGAS, SOIL, SWAT\n",
      "INFO:Field:TABLES attributes: DENSITY, PVDG, PVTO, PVTW, ROCK, SGOF, SWOF\n",
      "INFO:Field:WELLS attributes: WELSPECS, WELLTRACK, COMPDAT, WCONPROD\n",
      "INFO:Field:=========================\n",
      "INFO:Field:Grid pillars (`COORD`) are mapped to new axis with respect to `MAPAXES`.\n"
     ]
    }
   ],
   "source": [
    "model = Field(path='./model.hdf5').load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of tables the model contains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('DENSITY', 'PVDG', 'PVTO', 'PVTW', 'ROCK', 'SGOF', 'SWOF')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each table is represented by a pandas DataFrame, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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></th>\n",
       "      <th>FVF</th>\n",
       "      <th>VISC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <th>PRESSURE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20.59</th>\n",
       "      <th>50.0</th>\n",
       "      <td>1.10615</td>\n",
       "      <td>1.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.10164</td>\n",
       "      <td>1.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>1.09744</td>\n",
       "      <td>1.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125.0</th>\n",
       "      <td>1.09351</td>\n",
       "      <td>1.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>1.08984</td>\n",
       "      <td>1.453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    FVF   VISC\n",
       "RS    PRESSURE                \n",
       "20.59 50.0      1.10615  1.180\n",
       "      75.0      1.10164  1.247\n",
       "      100.0     1.09744  1.315\n",
       "      125.0     1.09351  1.384\n",
       "      150.0     1.08984  1.453"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.pvto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... but in contrast to a static table, one can query interpolated values for arbitrary point. Each table has a special interpolation method based on tNavigator/Eclipse defaults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PVDG/PVDO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PVDG-table is used to set properties of dry gas (without dissolved oil).\n",
    "Columns of the table are reference pressure (independant variable), formation volume factor and viscosity of gas (dependant ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>FVF</th>\n",
       "      <th>VISC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESSURE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.024958</td>\n",
       "      <td>0.01440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.017643</td>\n",
       "      <td>0.01490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>0.013615</td>\n",
       "      <td>0.01544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>0.011081</td>\n",
       "      <td>0.01605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130.0</th>\n",
       "      <td>0.009352</td>\n",
       "      <td>0.01671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               FVF     VISC\n",
       "PRESSURE                   \n",
       "50.0      0.024958  0.01440\n",
       "70.0      0.017643  0.01490\n",
       "90.0      0.013615  0.01544\n",
       "110.0     0.011081  0.01605\n",
       "130.0     0.009352  0.01671"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.pvdg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dependent quantities, linear interpolation of the quantities inverse of $ B_g $ and $ B_g\\mu_g $ between the given points is performed. Call-method for the table returns interpolated values ($ B_g $ and $ \\mu_g $) for the given set of pressure values. Same is relevant for PVDO-table that describes PVT properties of dissolved oil. Below we query some interpolated values from the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03148504, 0.01398137],\n",
       "       [0.01536947, 0.01520006],\n",
       "       [0.012218  , 0.01577044],\n",
       "       [0.01014335, 0.01640131]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = np.array([40, 80, 100, 120])\n",
    "\n",
    "model.tables.pvdg(points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can use `plot` to visualize interpolated table data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "model.tables.pvdg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PVTW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PVTW-table sets water properties by a single line for each region.\n",
    "Columns are reference pressure (independant variable), formation volume factor, water compressibility factor, viscosity and derivative of viscosity of water which is also called viscosibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>FVF</th>\n",
       "      <th>COMPR</th>\n",
       "      <th>VISC</th>\n",
       "      <th>VISCOSIBILITY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESSURE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277.0</th>\n",
       "      <td>1.038</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FVF     COMPR   VISC  VISCOSIBILITY\n",
       "PRESSURE                                       \n",
       "277.0     1.038  0.000047  0.318            0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.pvtw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For calculating formation volume factor and viscosity of water at different pressure values, the following equations is used:\n",
    "$$ $$\n",
    "$$ B_W(p_W) = \\frac{B_W(p_{ref})}{1+c_W(p_W-p_{ref}) + \\frac{1}{2}c^2_{W}(p_W-p_{ref})^{2}} $$\n",
    "\n",
    "$$ \\mu_W(p_W) = \\mu_{W_{ref}} \\exp{\\frac{1}{\\mu_{W_{ref}}} \\frac{\\partial \\mu_W}{\\partial P} (p_W - p_{ref})} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SWOF/SGOF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SWOF and SGOF tables are a set of points to plot relative permability curves and graphs of oil-water and gas-water capillary pressure.\n",
    "Corresponding independent variables by which interpolation is performed are water and gas saturation respectively.\n",
    "Linear interpolation is appled to these tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>KRWO</th>\n",
       "      <th>KROW</th>\n",
       "      <th>POW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0000</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.75633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0001</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.99900</td>\n",
       "      <td>3.75632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0500</th>\n",
       "      <td>0.00086</td>\n",
       "      <td>0.84782</td>\n",
       "      <td>1.86981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>0.00263</td>\n",
       "      <td>0.69746</td>\n",
       "      <td>1.23731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1500</th>\n",
       "      <td>0.00524</td>\n",
       "      <td>0.55717</td>\n",
       "      <td>0.91821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           KRWO     KROW      POW\n",
       "SW                               \n",
       "0.0000  0.00000  1.00000  3.75633\n",
       "0.0001  0.00000  0.99900  3.75632\n",
       "0.0500  0.00086  0.84782  1.86981\n",
       "0.1000  0.00263  0.69746  1.23731\n",
       "0.1500  0.00524  0.55717  0.91821"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.swof.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query some values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.63000e-03, 6.97460e-01, 1.23731e+00],\n",
       "       [8.77000e-03, 4.32860e-01, 7.24510e-01],\n",
       "       [3.60800e-02, 1.22370e-01, 3.66910e-01],\n",
       "       [1.03940e-01, 2.19900e-02, 2.13510e-01],\n",
       "       [4.92206e-01, 2.00000e-06, 4.75860e-02]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.swof([0.1, 0.2, 0.4, 0.6, 0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.tables.swof.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PVTO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PVTO is used to set PVT properties for oil with dissolved gas.\n",
    "The dataset defines the PVT properties for a given value of gas solubility, Rs. Some sets may contain data about properties of undersaturated oil for a given gas solubility. This additional data must be recorded for maximum values of Rs in each table. For the part of the table describing the unsaturated oil, gas solubility quantity is not duplicated / indicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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",
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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></th>\n",
       "      <th>FVF</th>\n",
       "      <th>VISC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <th>PRESSURE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20.59</th>\n",
       "      <th>50.0</th>\n",
       "      <td>1.10615</td>\n",
       "      <td>1.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.10164</td>\n",
       "      <td>1.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>1.09744</td>\n",
       "      <td>1.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125.0</th>\n",
       "      <td>1.09351</td>\n",
       "      <td>1.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>1.08984</td>\n",
       "      <td>1.453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    FVF   VISC\n",
       "RS    PRESSURE                \n",
       "20.59 50.0      1.10615  1.180\n",
       "      75.0      1.10164  1.247\n",
       "      100.0     1.09744  1.315\n",
       "      125.0     1.09351  1.384\n",
       "      150.0     1.08984  1.453"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.pvto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60a0a31e325048c288d716f8190c127c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectionSlider(description='RS', options=(20.59, 28.19, 36.01, 44.09, 52.46, 61.13, 70.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.tables.pvto.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PVDG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a saturated state, there is a one-to-one relationship between bubble point pressure and gas solubility. Current pressure value is equal to bubble point pressure: $ p = p_{bub} $. In this case, formation volume factor and viscosity of the oil are interpolated by Rs. An interpolation technique is defined by PVDG-table (linear interpolation of the quantities inverse of $ B_g $ and $ B_g\\mu_g $).\n",
    "If oil is in an undersaturated state, then for the current Rs value the nearest undersaturated branches are defined and further interpolation between them is performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>FVF</th>\n",
       "      <th>VISC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESSURE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.024958</td>\n",
       "      <td>0.01440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.017643</td>\n",
       "      <td>0.01490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>0.013615</td>\n",
       "      <td>0.01544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>0.011081</td>\n",
       "      <td>0.01605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130.0</th>\n",
       "      <td>0.009352</td>\n",
       "      <td>0.01671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               FVF     VISC\n",
       "PRESSURE                   \n",
       "50.0      0.024958  0.01440\n",
       "70.0      0.017643  0.01490\n",
       "90.0      0.013615  0.01544\n",
       "110.0     0.011081  0.01605\n",
       "130.0     0.009352  0.01671"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tables.pvdg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done!"
   ]
  }
 ],
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