Reservoir Simulator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This block of code sets the properties of the reservoir.  The commented code is for user input.  \n",
    "The first block is where the user inputs the reservoir information.  Exchange the function with what's included here below in the triple-quotes to ask the user for their input.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import sparse\n",
    "\n",
    "'''\n",
    "def Res_properties():\n",
    "    NX = int(input(\"Number of X grid blocks: \"))\n",
    "    NY = int(input(\"Number of Y grid blocks: \"))\n",
    "    L = int(input(\"Length of Reservoir: \"))\n",
    "    W = int(input(\"Width of Reservoir: \"))\n",
    "    h = int(input(\"Height of Reservoir: \"))\n",
    "    rw = float(input(\"Wellbore radius: \"))\n",
    "    dt = float(input(\"Change in time: \"))\n",
    "    mu = float(input(\"Fluid viscosity: \"))\n",
    "    Bw = float(input(\"Formation volume factor: \"))\n",
    "    ct = float(input(\"Rock compressibility: \"))\n",
    "    Pi = int(input(\"Initial pressure: \"))\n",
    "    Porosity = (np.ones([NX, NY])*0.3).T\n",
    "    Permeability = (np.ones([NX, NY])*75).T\n",
    "    t = int(input(\"Total time: \"))\n",
    "    n = int(input(\"Number of wells: \"))\n",
    "    \n",
    "    return NX, NY, L, W, h, rw, dt, mu, Bw, ct, Pi, Porosity, Permeability, t, n'''\n",
    "\n",
    "def Res_properties():\n",
    "    NX = 99\n",
    "    NY = 99\n",
    "\n",
    "    L = 20000\n",
    "    W = 20000\n",
    "    h = 300\n",
    "    rw = float(.25)\n",
    "    dt = float(1)\n",
    "    mu = float(1)\n",
    "    Bw = float(1.36)\n",
    "    ct = float(1e-06)\n",
    "    Pi = int(3000)\n",
    "    Porosity = (np.ones([NX, NY])*.3).T\n",
    "    Permeability = (np.ones([NX, NY])*75).T\n",
    "    t = int(100)\n",
    "    n = 5\n",
    "\n",
    "    return NX, NY, L, W, h, rw, dt, mu, Bw, ct, Pi, Porosity, Permeability, t, n\n",
    "\n",
    "NX, NY, L, W, h, rw, dt, mu, Bw, ct, Pi, Porosity, Permeability, t, n = Res_properties()\n",
    "\n",
    "dx = L/NX\n",
    "dy = W/NY\n",
    "\n",
    "Area_x = h*dx\n",
    "Area_y = h*dy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below T_inter function creates the intertransmisciblity of the reservoir between blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def T_inter(Permeability, Permeability_2, mu, Bw, dx, Area):\n",
    "    if Permeability == 0 or Permeability_2 == 0:\n",
    "        t_inter = 0\n",
    "    else:\n",
    "        k_inter = 2*(1/float(Permeability) + 1/float(Permeability_2))**(-1)\n",
    "        t_inter = k_inter*Area/(mu*Bw*dx)*(6.33e-03)\n",
    "    return t_inter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, T_array creates the matrix to describe intertransmiscibility.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "def T_array(NX, NY, Permeability, mu, Bw, dx, dy, Area_x, Area_y):\n",
    "    N = NX*NY\n",
    "    T = sparse.lil_matrix(np.zeros([N, N]))\n",
    "\n",
    "    for i in range(N):\n",
    "        if (i+1) - NX > 0:  # shows blocks above bottom edge\n",
    "            T[i, i-NX] = -T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                  Permeability[3-m.floor((i)/NX),i-m.floor(i/NX)*NX],\n",
    "                                mu, Bw, dy, Area_x)\n",
    "            T[i, i] = T[i, i] + T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                        Permeability[3-m.floor((i)/NX),i-m.floor(i/NX)*NX],\n",
    "                                mu, Bw, dy, Area_x)\n",
    "\n",
    "            \n",
    "        \n",
    "        if (i+1) + NX <= N:    # shows blocks below top edge \n",
    "\n",
    "            T[i, i+NX] = -T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                  Permeability[1-m.floor((i)/NX),i-m.floor(i/NX)*NX],\n",
    "                                mu, Bw, dy, Area_x)\n",
    "            T[i, i] = T[i, i] + T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                        Permeability[1-m.floor((i)/NX),i-m.floor(i/NX)*NX],\n",
    "                                mu, Bw, dy, Area_x)\n",
    "\n",
    "            \n",
    "            \n",
    "        if (i+1)%NX != 0:     # Shows blocks except right edge\n",
    "            T[i, i+1] = -T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX+1],\n",
    "                                mu, Bw, dx, Area_y)\n",
    "            T[i, i] = T[i, i] + T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                        Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX+1],\n",
    "                                mu, Bw, dx, Area_y)\n",
    "\n",
    "            \n",
    "            \n",
    "        if (i+1)%NX != 1:      # shows blcoks except left edge\n",
    "            T[i, i-1] = -T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                 Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX-1],\n",
    "                                mu, Bw, dx, Area_y)\n",
    "            T[i, i] = T[i, i] + T_inter(Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX], \n",
    "                                        Permeability[2-m.floor((i)/NX),i-m.floor(i/NX)*NX-1],\n",
    "                                mu, Bw, dx, Area_y)      \n",
    "            \n",
    "    return sparse.lil_matrix(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inter_transm function creates the pressure points for each block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy.sparse.linalg import spsolve\n",
    "\n",
    "def inter_transm(Permeability, Porosity, NX, NY, t, W, L, h, mu, Bw, dt, ct, Pi, dx, dy, Q, J, B):\n",
    "    T = T_array(NX,NY,Permeability,mu,Bw,dx,dy,Area_x,Area_y);\n",
    "    T = sparse.lil_matrix(T);\n",
    "    \n",
    "    Permeability = Permeability.reshape(1, NX*NY)\n",
    "    Porosity = Porosity.reshape(1, NX*NY)\n",
    "    \n",
    "    P_0 = np.ones([NX*NY,1])*Pi\n",
    "    P_n = sparse.lil_matrix(P_0)\n",
    "    \n",
    "    P = np.zeros([NX*NY,t+1])\n",
    "    P[:,0] = P_0.transpose()\n",
    "    \n",
    "    for i in range(t):\n",
    "        a = T + J + B/dt\n",
    "        b = np.dot((B/dt), P_n) + Q\n",
    "        P_nplus1 = spsolve(a,b)\n",
    "        P_n = (sparse.lil_matrix(P_nplus1)).transpose()\n",
    "        P[:,i+1] = P_nplus1\n",
    "    \n",
    "    x = [i*L/(NX-1) for i in range(NX)]\n",
    "    y = [i*W/(NY-1) for i in range(NY)]\n",
    "    \n",
    "    return P, x, y, T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the main script to create the matrices used at the final plot. \n",
    "Ex: P_time_3 data is Pressure at time = 3 days.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import math as m\n",
    "from scipy import sparse\n",
    "import random\n",
    "\n",
    "NX, NY, L, W, h, rw, dt, mu, Bw, ct, Pi, Porosity, Permeability, t, n = Res_properties()\n",
    "\n",
    "wells = np.zeros([n, 4])\n",
    "Q = sparse.lil_matrix(np.zeros([NX*NY, 1]))\n",
    "J = sparse.lil_matrix(np.zeros([NX*NY, NX*NY]))\n",
    "\n",
    "x_loc = []\n",
    "y_loc = []\n",
    "welltype = []\n",
    "oper_con = []\n",
    "\n",
    "for i in range(n):\n",
    "    x_loc = [5000, 15000, 5000, 15000, 10000]\n",
    "    y_loc = [5000, 5000, 15000, 15000, 10000]\n",
    "    welltype = [1, 1, 1, 1, 2]\n",
    "    oper_con = [1000, 1000, 1000, 1000, 8000]\n",
    "    \n",
    "    # x_loc.append(int(input('X location of Well: ')))\n",
    "    # y_loc.append(int(input('Y location of Well: ')))\n",
    "    # welltype.append(int(input('BHP (1) or Injector (2)? ')))\n",
    "    # oper_con.append(int(input('Operating condition (ft^3/day or psi): ')))\n",
    "    \n",
    "    wells[i,:] = [x_loc[i], y_loc[i], welltype[i], oper_con[i]]\n",
    "    l = (m.ceil(y_loc[i]/dy)-1)*NX+m.ceil(x_loc[i]/dx)-1 \n",
    "    if welltype[i] == 1:\n",
    "        J[l,l] = (6.33e-3)*(2*m.pi*Permeability[2-m.floor((i)/3),i-m.floor(i/3)*3]*h)/(mu*Bw*m.log(.28/2*(dx**2+dy**2)**(1/2)/rw))\n",
    "        Q[l] = J[l,l]*oper_con[i]\n",
    "        Q = sparse.lil_matrix(Q)\n",
    "    else:\n",
    "        Q[l] = oper_con[i]\n",
    "        Q = sparse.lil_matrix(Q)\n",
    "    J = sparse.lil_matrix(J)\n",
    "Q = sparse.lil_matrix(Q)\n",
    "B = np.diagflat(Porosity.reshape(1,NX*NY)*dx*dy*h*ct/Bw)\n",
    "B = sparse.lil_matrix(B)\n",
    "P, x, y, T = inter_transm(Permeability, Porosity, NX, NY, t, W, L, h, mu, Bw, dt, ct, Pi, dx, dy, Q, J, B)\n",
    "\n",
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "P_time_3 = P[:,3].reshape([NX,NY])\n",
    "P_time_20 = P[:,20].reshape([NX,NY])\n",
    "P_time_50 = P[:,50].reshape([NX,NY])\n",
    "\n",
    "Q_time = np.zeros([t,welltype.count(1)])\n",
    "P_time = np.zeros([t,n])\n",
    "\n",
    "for i in range(n):\n",
    "    l=(m.ceil(y_loc[i]/dy)-1)*NX+m.ceil(x_loc[i]/dx)-1\n",
    "    \n",
    "    if welltype[i] == 1:\n",
    "        for t_p in range(t):\n",
    "            Q_time[t_p,i-welltype[:i].count(2)] = J[l,l]*(P[l,t_p]-oper_con[i])\n",
    "            \n",
    "    elif welltype[i] == 2:\n",
    "        for t_p in range(t):\n",
    "            J[l,l] = 6.33e-3*(2*m.pi*Permeability[2-m.floor((i)/3),i-m.floor(i/3)*3]*h)/(mu*Bw*m.log(.28/2*(dx**2+dy**2)**(1/2)/rw))\n",
    "            P_time[t_p,i] = P[l,t_p] + (oper_con[i]/J[l,l])\n",
    "            \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the script to create the 3D plot in Matplotlib.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmnkRreWAABOBHOC7OC19um8TIsTX4v8jU/WGbD8U8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151dfd8e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm, colorbar\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.plot_surface(X.T, Y.T, P_time_50, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
    "    linewidth=0, antialiased=False)\n",
    "\n",
    "ax.set_xlabel('Width (ft)')\n",
    "ax.set_ylabel('Length (ft)')\n",
    "ax.set_zlabel('Pressure (psi)')\n",
    "plt.tick_params(axis='both', labelsize=14)\n",
    "plt.xticks(rotation=8)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next section is to recreate a more smooth graph in plotly.  \n",
    "The 3D interactive graph is available at https://plot.ly/~keedanbarrera/2.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99, 99)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~keedanbarrera/2.embed\" height=\"500px\" width=\"600px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import pandas as pd\n",
    "print(P_time_50.shape)\n",
    "data = [\n",
    "    go.Surface(\n",
    "        z=P_time_50\n",
    "    )\n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Nichelik Field',\n",
    "    autosize=False,\n",
    "    width=600,\n",
    "    height=500,\n",
    "    margin=dict(\n",
    "        l=65,\n",
    "        r=50,\n",
    "        b=65,\n",
    "        t=90\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='elevations-3d-surface')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}