Generate_Conditional_Sampling-45.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smith/miniconda3/envs/tensorflowGPU/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/smith/miniconda3/envs/tensorflowGPU/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/smith/miniconda3/envs/tensorflowGPU/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/smith/miniconda3/envs/tensorflowGPU/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from HistoryMatching.ReadFiles import ReadObsFile\n",
    "from HistoryMatching.Utils import CVAE_function,GetFaciesData,Plot_Result,Contitional_ES_MDA\n",
    "from HistoryMatching.Utils import Load_Ensemble,GerenateObsFile,CreateStateFacies,UpdateStateFacies\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Ensemble Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Facies=Load_Ensemble(pathFile='DataSet/MPS-45.mat',variable='Dato')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0, 1, 1, 1, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "GenerateMeasurement=True\n",
    "import scipy.io as sio\n",
    "True45 = sio.loadmat('DataSet/True45.mat')\n",
    "True45 = True45['True']\n",
    "if GenerateMeasurement:\n",
    "    \"\"\n",
    "    \"\"\n",
    "    xp = [2,37,7,33]      # produtor well\n",
    "    yp = [22,4,42,41]     # produtor well\n",
    "    xi = [7,24,42]        # injetor well\n",
    "    yi = [4,14,26]        # injetor well\n",
    "    position=np.array([yp+yi] + [xp+xi]).T\n",
    "    obs=GerenateObsFile(position,True45)\n",
    "    print(obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test #1\n",
    "<ul>\n",
    "    <li> File utilizado: 'Obs.dat' </li>\n",
    "        <ul>\n",
    "            <li> xp = [2,37,7,33] - Poço produtor </li>\n",
    "            <li> yp = [22,4,42,41] - Poço produtor </li>\n",
    "            <li> xi = [7,24,42] - Poço injetor </li>\n",
    "            <li> yi = [4,14,26] - Poço injetor </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li> position = np.array([yp+yi] + [xp+xi]).T </li>\n",
    "</ul>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Observation File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs,R,t_acu,act,produccion = ReadObsFile('Obs.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate parameterized Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/smith/miniconda3/envs/tensorflowGPU/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "m_x = CreateStateFacies(Facies,45,45,redeVAE='Model/TrainModel/CVAE45'); # facies em DL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstucted Initial Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_f = UpdateStateFacies(m_x,45,45,redeVAE='Model/TrainModel/CVAE45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute ES-MDA for generate Contidional Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro ite_ 0  :  818.0\n",
      "Erro ite_ 1  :  360.0\n",
      "Erro ite_ 2  :  115.0\n",
      "Erro ite_ 3  :  56.0\n",
      "Erro ite_ 4  :  28.0\n",
      "Erro ite_ 5  :  15.0\n",
      "Erro End:  11.0\n"
     ]
    }
   ],
   "source": [
    "m_f_a=Contitional_ES_MDA(6,[],position,obs,R,m_x,m_f,[45,45],redeVAE='Model/TrainModel/CVAE45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot True Model and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "plt.imshow(True45.reshape((45,45)),cmap='jet')\n",
    "plt.plot(xp,yp,'^',color='yellow')\n",
    "plt.plot(xi,yi,'o',color='green')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_Result(Facies,m_f_a,xp,yp,xi,yi,initial_data=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste 2\n",
    "\n",
    "<ul>\n",
    "    <li> Arquivo utilizado: 'Obs2.dat' </li>\n",
    "    <li> Pontos utilizados:\n",
    "        <ul>\n",
    "            <li> xe = [2,8,18,33,41,44,2,10,26,38,44] </li>\n",
    "            <li> ye = [40,34,27,23,19,7,15,10,1,40,36] </li>\n",
    "            <li> xi = [] </li>\n",
    "            <li> yi = [] </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li> position = np.array([ye] + [xe]).T </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "GenerateMeasurement=True\n",
    "import scipy.io as sio\n",
    "True45 = sio.loadmat('DataSet/True45.mat')\n",
    "True45 = True45['True']\n",
    "if GenerateMeasurement:\n",
    "    \"\"\n",
    "    \"\"\n",
    "    xe = [2, 8, 18,33,41,44,2 ,10,26,38,44]\n",
    "    ye = [40,34,30,28,25,7 ,15,10,1 ,40,36]\n",
    "    position=np.array([ye] + [xe]).T\n",
    "    obs=GerenateObsFile(position,True45,filePath='Obs2.dat')\n",
    "    print(obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Observation File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs,R,t_acu,act,produccion = ReadObsFile('Obs2.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate parameterized Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_x = CreateStateFacies(Facies,45,45,redeVAE='Model/TrainModel/CVAE45'); # facies em DL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstucted Initial Facies data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_f = UpdateStateFacies(m_x,45,45,redeVAE='Model/TrainModel/CVAE45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute ES-MDA for generate Contidional Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro ite_ 0  :  1172.0\n",
      "Erro ite_ 1  :  384.0\n",
      "Erro ite_ 2  :  109.0\n",
      "Erro ite_ 3  :  38.0\n",
      "Erro ite_ 4  :  19.0\n",
      "Erro ite_ 5  :  10.0\n",
      "Erro End:  7.0\n"
     ]
    }
   ],
   "source": [
    "m_f_a=Contitional_ES_MDA(6,[],position,obs,R,m_x,m_f,[45,45],redeVAE='Model/TrainModel/CVAE45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot True Model and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "plt.imshow(True45.reshape((45,45)),cmap='jet')\n",
    "plt.plot(xe,ye,'o',color='green')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_Result(Facies,m_f_a,xe,ye,[],[],initial_data=0) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}