PVT Analysis - Pressure Bubble Point Prediction using Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description\n",
    "\n",
    "The dataset consists of 5 Features\n",
    "\n",
    "1. Temperature:\n",
    "Oil Temperature in celsius\n",
    "\n",
    "2. Rs: \n",
    "Solution gas to Oil Ratio which is a general term for the amount of gas dissolved in the oil.\n",
    "\n",
    "3. Gas Gravity:\n",
    "Gas Gravity is the ratio of the density of the gas at standard pressure and temperature to the density of air at the same standard pressure and temperature\n",
    "\n",
    "3. Oil API: \n",
    "The grade of the crude oil\n",
    "\n",
    "4. Pbp: \n",
    "Pressure Bubble Point \"The pressure at which the first bubble of  gas is formed within the liquid oil phase. As pressure falls below the bubble point more gas is liberated from oil and single liquid phase becomes 2 phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('PVT Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Temperature</th>\n",
       "      <th>Rs</th>\n",
       "      <th>Gas Gravity</th>\n",
       "      <th>Oil API</th>\n",
       "      <th>Pbp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.796271</td>\n",
       "      <td>411.145756</td>\n",
       "      <td>1.012870</td>\n",
       "      <td>31.658760</td>\n",
       "      <td>1424.602150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.936641</td>\n",
       "      <td>291.829082</td>\n",
       "      <td>0.162192</td>\n",
       "      <td>5.036067</td>\n",
       "      <td>908.669973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>70.447234</td>\n",
       "      <td>27.832416</td>\n",
       "      <td>0.797048</td>\n",
       "      <td>17.854225</td>\n",
       "      <td>131.484967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>116.365357</td>\n",
       "      <td>183.034832</td>\n",
       "      <td>0.885612</td>\n",
       "      <td>28.469070</td>\n",
       "      <td>697.772696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>142.257643</td>\n",
       "      <td>348.735793</td>\n",
       "      <td>0.978741</td>\n",
       "      <td>32.604282</td>\n",
       "      <td>1252.802615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>176.074354</td>\n",
       "      <td>587.055416</td>\n",
       "      <td>1.113760</td>\n",
       "      <td>35.875571</td>\n",
       "      <td>1937.635034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>282.911419</td>\n",
       "      <td>1471.094081</td>\n",
       "      <td>1.632588</td>\n",
       "      <td>39.714096</td>\n",
       "      <td>4306.643567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature           Rs  Gas Gravity     Oil API          Pbp\n",
       "count   249.000000   249.000000   249.000000  249.000000   249.000000\n",
       "mean    147.796271   411.145756     1.012870   31.658760  1424.602150\n",
       "std      41.936641   291.829082     0.162192    5.036067   908.669973\n",
       "min      70.447234    27.832416     0.797048   17.854225   131.484967\n",
       "25%     116.365357   183.034832     0.885612   28.469070   697.772696\n",
       "50%     142.257643   348.735793     0.978741   32.604282  1252.802615\n",
       "75%     176.074354   587.055416     1.113760   35.875571  1937.635034\n",
       "max     282.911419  1471.094081     1.632588   39.714096  4306.643567"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objectives\n",
    "\n",
    "Statistical regression techniques are mainly used to derive empirical correlations for different PVT properties, besides statistical regression techniques, it is also possible to use neural networks to develop correlations between PVT properties. In this project I will use Neural Networks to estimate the correlations between PVT Properties and compare it to the analytical solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dividing our dataset into inputs and outputs\n",
    "\n",
    "X = pd.DataFrame(data = data.iloc[:,0:4])\n",
    "y = pd.DataFrame(data = data.iloc[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 = data.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Getting the names of the columns\n",
    "\n",
    "X_feature_columns = X.columns\n",
    "y_feature_columns = y.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Scaling our dataset to be ready for the neural networks\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler1 = MinMaxScaler()\n",
    "scaler2 = MinMaxScaler()\n",
    "Xnorm = pd.DataFrame(data = scaler1.fit_transform(X), columns=X_feature_columns)\n",
    "ynorm = pd.DataFrame(data = scaler2.fit_transform(y), columns=y_feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Feature Ranking using Random Forest and Spearmans correlation to see the \n",
    "impact of different input parameters on the output parameter \"Pbp\" \n",
    "PRESSURE BUBBLE POINT.\n",
    "'''\n",
    "\n",
    "from scipy import stats\n",
    "datanorm = scaler.fit_transform(data)\n",
    "rho, pval = stats.spearmanr(datanorm)\n",
    "corr = pd.Series(rho[:4,4], index = X.columns)\n",
    "corr.plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-6bcb04ff492e>:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(X, y)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Using Random Forest to Check the importance of input features\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(max_depth=10, random_state=0)\n",
    "model.fit(X, y)\n",
    "feat_importance = pd.Series(model.feature_importances_, index = X.columns)\n",
    "feat_importance.plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Deep Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Splitting the data into train and test sets\n",
    "\n",
    "seed = 50\n",
    "np.random.seed(seed)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(Xnorm, ynorm, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Neural Network setup using 7 input layers only and tanh, lbfgs\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "np.random.seed(seed)\n",
    "\n",
    "nn_model_1 = MLPRegressor(hidden_layer_sizes=(7),\n",
    "                       activation = 'tanh',\n",
    "                       solver = 'lbfgs',\n",
    "                       alpha = 1,\n",
    "                       learning_rate_init=0.1,\n",
    "                       max_iter=2000,\n",
    "                       random_state=None,\n",
    "                       tol = 0.01)\n",
    "\n",
    "y_train_flat = y_train.values.ravel()\n",
    "nn_model_1.fit(X_train, y_train_flat)\n",
    "\n",
    "## Predicting the Pbp values using the nn_model_1\n",
    "Pbp_test_prediction_1 =nn_model_1.predict(X_test)\n",
    "Pbp_train_prediction_1 = nn_model_1.predict(X_train)\n",
    "\n",
    "## Inverse transforming the predictions\n",
    "Pbp_test_prediction_1 = Pbp_test_prediction_1.reshape(-1,1)\n",
    "Pbp_train_prediction_1 = Pbp_train_prediction_1.reshape(-1,1)\n",
    "\n",
    "Pbp_test_prediction_1 = scaler2.inverse_transform(Pbp_test_prediction_1)\n",
    "Pbp_train_prediction_1 = scaler2.inverse_transform(Pbp_train_prediction_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Neural Network setup using 7 input layers and 5 hidden layers\n",
    "\n",
    "np.random.seed(seed)\n",
    "\n",
    "nn_model_2 = MLPRegressor(hidden_layer_sizes=(7,5),\n",
    "                       activation = 'tanh',\n",
    "                       solver = 'lbfgs',\n",
    "                       alpha = 1,\n",
    "                       learning_rate_init=0.1,\n",
    "                       max_iter=2000,\n",
    "                       random_state=None,\n",
    "                       tol = 0.01)\n",
    "\n",
    "y_train_flat = y_train.values.ravel()\n",
    "nn_model_2.fit(X_train, y_train_flat)\n",
    "\n",
    "## Predicting the Pbp values using the nn_model_2\n",
    "Pbp_test_prediction_2 =nn_model_2.predict(X_test)\n",
    "Pbp_train_prediction_2 = nn_model_2.predict(X_train)\n",
    "\n",
    "## Inverse transforming the predictions\n",
    "Pbp_test_prediction_2 = Pbp_test_prediction_2.reshape(-1,1)\n",
    "Pbp_train_prediction_2 = Pbp_train_prediction_2.reshape(-1,1)\n",
    "\n",
    "Pbp_test_prediction_2 = scaler2.inverse_transform(Pbp_test_prediction_2)\n",
    "Pbp_train_prediction_2 = scaler2.inverse_transform(Pbp_train_prediction_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Neural Network setup using 7 input layers and 5 hidden layers with logistic activation function\n",
    "\n",
    "np.random.seed(seed)\n",
    "\n",
    "nn_model_3 = MLPRegressor(hidden_layer_sizes=(7,5),\n",
    "                       activation = 'relu',\n",
    "                       solver = 'lbfgs',\n",
    "                       alpha = 1,\n",
    "                       learning_rate_init=0.1,\n",
    "                       max_iter=2000,\n",
    "                       random_state=None,\n",
    "                       tol = 0.01)\n",
    "\n",
    "y_train_flat = y_train.values.ravel()\n",
    "nn_model_3.fit(X_train, y_train_flat)\n",
    "\n",
    "## Predicting the Pbp values using the nn_model_3\n",
    "Pbp_test_prediction_3 =nn_model_3.predict(X_test)\n",
    "Pbp_train_prediction_3 = nn_model_3.predict(X_train)\n",
    "\n",
    "## Inverse transforming the predictions\n",
    "Pbp_test_prediction_3 = Pbp_test_prediction_3.reshape(-1,1)\n",
    "Pbp_train_prediction_3 = Pbp_train_prediction_3.reshape(-1,1)\n",
    "\n",
    "Pbp_test_prediction_3 = scaler2.inverse_transform(Pbp_test_prediction_3)\n",
    "Pbp_train_prediction_3 = scaler2.inverse_transform(Pbp_train_prediction_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Actual Pbp test and train values\n",
    "Pbp_test = scaler2.inverse_transform(y_test)\n",
    "Pbp_train = scaler2.inverse_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score for the Neural Network model 1 is 0.8995558148359026\n",
      "R2 Score for the Neural Network model 2 is 0.9098509823158558\n",
      "R2 Score for the Neural Network model 3 is 0.8785516249055134\n"
     ]
    }
   ],
   "source": [
    "## R2 Scores for each neural network model 1, 2 and 3\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2_test_1 = r2_score(Pbp_test, Pbp_test_prediction_1)\n",
    "r2_test_2 = r2_score(Pbp_test, Pbp_test_prediction_2)\n",
    "r2_test_3 = r2_score(Pbp_test, Pbp_test_prediction_3)\n",
    "\n",
    "print(f'R2 Score for the Neural Network model 1 is {r2_test_1}')\n",
    "print(f'R2 Score for the Neural Network model 2 is {r2_test_2}')\n",
    "print(f'R2 Score for the Neural Network model 3 is {r2_test_3}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Pbp_test_prediction_3')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize = (8,15))\n",
    "ax[0].scatter(Pbp_test, Pbp_test_prediction_1)\n",
    "ax[0].set_xlabel('Pbp_test_prediction_1')\n",
    "ax[1].scatter(Pbp_test, Pbp_test_prediction_2)\n",
    "ax[1].set_xlabel('Pbp_test_prediction_2')\n",
    "ax[2].scatter(Pbp_test, Pbp_test_prediction_3)\n",
    "ax[2].set_xlabel('Pbp_test_prediction_3')"
   ]
  },
  {
   "attachments": {
    "Screenshot%202021-10-12%20224157.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the Solutions obtained from the neural network with the \"Marhoun correlation\" \n",
    "\n",
    "![Screenshot%202021-10-12%20224157.png](attachment:Screenshot%202021-10-12%20224157.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "To use the Marhoun correlation we have to do the following:\n",
    "    1. Temperature unit converted from F to R by adding 460 to F\n",
    "    2. Oil API should be converted to oil specific gravity using the\n",
    "        following formula --> gamma = 145/(API + 135)\n",
    "'''\n",
    "\n",
    "## Converting the units of Temperature and Oil API\n",
    "DatasetTempGamma = data.copy()\n",
    "DatasetTempGamma['Temperature'] += 460\n",
    "DatasetTempGamma['Oil API']=145/(DatasetTempGamma['Oil API'] + 135)\n",
    "\n",
    "## Calculating the Pbp using the Marhoun Correlation\n",
    "Pbp_Marhoun = 0.00538088 * (pow(DatasetTempGamma['Rs'], 0.715082))*\\\n",
    "    (pow(DatasetTempGamma['Gas Gravity'], -1.877784))*\\\n",
    "    (pow(DatasetTempGamma['Oil API'], 3.1437))*\\\n",
    "    (pow(DatasetTempGamma['Temperature'], 1.32657))\n",
    "\n",
    "## Calculating the Pbp using the 2nd Neural Network model\n",
    "Pbp_NN = nn_model_2.predict(Xnorm)\n",
    "\n",
    "Pbp_NN = Pbp_NN.reshape(-1, 1)\n",
    "\n",
    "Pbp_NN = scaler2.inverse_transform(Pbp_NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score for the Neural Network is 0.9198075432299188\n",
      "The r2 score for the Marhoun correlation is 0.8668003939816404\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(2000, 4000, 'R2 = 0.8668')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparing the results from the Neural Network with the Marhoun correlation\n",
    "\n",
    "# Calculating the r2 scores for the Neural Network and the Marhoun\n",
    "r2_NN = r2_score(data['Pbp'], Pbp_NN)\n",
    "r2_Marhoun = r2_score(data['Pbp'], Pbp_Marhoun)\n",
    "\n",
    "print(f'The r2 score for the Neural Network is {r2_NN}\\nThe r2 score for the Marhoun correlation is {r2_Marhoun}\\n\\n')\n",
    "\n",
    "_, ax = plt.subplots(2, 1, figsize = (7, 14))\n",
    "ax[0].scatter(data['Pbp'], Pbp_NN)\n",
    "ax[0].set_xlabel('Pbp_Measured(Psi)')\n",
    "ax[0].set_ylabel('Pbp_Neural Network(Psi)')\n",
    "ax[0].plot([data['Pbp'].min(), data['Pbp'].max()],[data['Pbp'].min(), data['Pbp'].max()], 'k--', lw=4)\n",
    "ax[0].text(2000, 4000, f\"R2 = {round(r2_NN, 5)}\")\n",
    "\n",
    "ax[1].scatter(data['Pbp'], Pbp_Marhoun)\n",
    "ax[1].set_xlabel('Pbp_Measured(Psi)')\n",
    "ax[1].set_ylabel('Pbp_Marhoun(Psi)')\n",
    "ax[1].plot([data['Pbp'].min(), data['Pbp'].max()],[data['Pbp'].min(), data['Pbp'].max()], 'k--', lw=4)\n",
    "ax[1].text(2000, 4000, f\"R2 = {round(r2_Marhoun, 5)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key Findings\n",
    "\n",
    "We can find that the Neural Network with a hidden layer and tanh activation function obtained the best R2 Score of 0.91 and obtained better results compared to the Marhoun Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flaws and Next Steps\n",
    "\n",
    "In terms of Flaws, I think the dataset is relatively small, getting more data will indeed be beneficial and result in a more accurate model\n",
    "\n",
    "Next steps: \n",
    "Gaining more data samples can increase the accuracy as well as getting more features for example \"liquid drop out\" can be beneficial to determine more accurate correlation between the features "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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}