Lorenz_coefficient_function.ipynb

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    "### Python Functions to Calculate and Plot the Lorenz Curve and Coefficient to Quantify Spatial Heterogeneity\n",
    "\n",
    "#### The University of Texas at Austin, PGE 2020 SURI, Undergraduation Research\n",
    "\n",
    "#### Alan Scherman, Undergraduate Student, Rice University\n",
    "\n",
    "\n",
    "#### Supervised by: \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "#### Introduction\n",
    "\n",
    "This is a demonstration of how to calculate the Lorenz Coefficient of a subsurface sample from well log porosity and permeability measurements. The Lorenz Coefficient is an important heuristic imported form the sphere of macroeconomics used to quantify the spatial heterogeneity of a subsurface sample. The Lorenz Coefficient is obtained by doubling the area between the Lorenz Curve and the Homogeneous Line. It ranges from 0 to 1 where, by convention, a coefficient of less than 0.3 suggests low heterogeneity and a coefficient of more than 0.6 indicates high heterogeneity. In practical terms, a low spatial heterogeneity allows for simple displacement of subsurface fluids and a high recovery factor.\n",
    "***\n",
    "#### Objective\n",
    "\n",
    "To understand and apply the methodology to calculate the Lorenz Curve and Coefficient from porosity and permeability measurements through Python functionalities.\n",
    "\n",
    "***\n",
    "#### Calculation procedure\n",
    "\n",
    "The following list contains the necessary steps to determine the Lorenz Coefficient of a subsurface sample:\n",
    "\n",
    "**(1)** Sort porosity ($\\phi$) and permeability (**_k_**) in descending order of ratio **_k_**/$\\phi$;\n",
    "\n",
    "**(2)** Calculate storage (**_S.C._**) and flow capacity (**_F.C._**) of each layer (region between depth measurements) with:\n",
    "\n",
    "<br>\n",
    "\\begin{equation}\n",
    "S.C. = \\phi*h\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "F.C. = k*h\n",
    "\\end{equation}\n",
    "\n",
    "where **_h_** is the layer thickness;\n",
    "\n",
    "**(3)** Calculate the _cumulative_ storage (**_C.S.C._**) and flow capacities (**_C.F.C._**) of each layer with:\n",
    "\n",
    "<br>\n",
    "\\begin{equation}\n",
    "C.S.C. = \\sum^{current layer}_{i = 1} \\phi*h\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "C.F.C. = \\sum^{current layer}_{i = 1} k*h\n",
    "\\end{equation}\n",
    "\n",
    "**(4)** Normalize the cumulative storage and flow capacities by dividing them by the largest cumulative storage and flow capacities (i.e. the last cumulative capacities calculated), respectively;\n",
    "\n",
    "**(5)** _(Optional)_ Plot the normalized cumulative flow capacities against the normalized cumulative storage capacities (i.e. the Lorenz Curve);\n",
    "\n",
    "**(6)** Find a curve fit for the normalized capacities in the Cartesian plane (usually a 3rd degree polynomial is sufficient);\n",
    "\n",
    "**(7)** Integrate to find the area between the Lorenz Curve and the Homogenous Line;\n",
    "\n",
    "**(8)** Divide the result found in **(7)** by 0.5 to obtain the Lorenz Coefficient:\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "LorenzCoefficient = \\frac{\\int^{1}_{0} (LorenzCurve - HomogeneousLine)}{0.5}\n",
    "\\end{equation}\n",
    "***\n",
    "#### Load the required libraries\n",
    "\n",
    "The program below utilizes some standard Python packages. These should be previously installed if you have Anaconda or other similar software.\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "There are examples below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "Let's import the GeostatsPy package."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Declare function\n",
    "\n",
    "These are the functions to:\n",
    "\n",
    "1. calculate the Lorenz coefficent and the Lorenz curve\n",
    "\n",
    "2. plot the Lorenz curve and display the Lorenz coeficient"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorenz_coefficient(poros, perme, layer_thick = 0.25, depth = None, curve_plot = False):\n",
    "   \n",
    "    \"\"\"Calculates Lorenz coefficient and curve of subsurface sample given porosity and permeability data.\n",
    "    :param poros: porosities in %\n",
    "    :type: 1D numpy.ndarray\n",
    "    :param perme: permeabilities in mD\n",
    "    :type: 1D numpy.ndarray\n",
    "    :param layer_thick: uniform layer thickness; default 0.25 m;\n",
    "                        if depth is not None, layer_thick is recalculated\n",
    "    :type: int or float\n",
    "    :param depth: default None for constant thickness; \n",
    "                  for variable layer thickness, enter 1D numpy.ndarray of depths in m\n",
    "    :param curve_plot: default False; set to True to display the Lorenz Curve\n",
    "    :type: boolean\n",
    "    :return:lorenz_coeff: Lorenz Coefficient rounded to 3 decimal places\n",
    "           :type: float\n",
    "           :lorenz_plot: (optional) figure with plot of Lorenz Curve   \n",
    "    \"\"\"\n",
    "        \n",
    "    # Converting to row-oriented numpy.ndarrays (if necessary)\n",
    "    \n",
    "    if np.ndim(depth) == 2:\n",
    "        depth = np.transpose(depth)[0,:]\n",
    "    if np.ndim(poros) == 2:\n",
    "        poros = np.transpose(poros)[0,:]\n",
    "    if np.ndim(perme) == 2:\n",
    "        perme = np.transpose(perme)[0,:]\n",
    "   \n",
    "    # Determining layer thickness array\n",
    "    \n",
    "    if depth is not None:\n",
    "        layer_thick = depth - np.concatenate(([0],depth[:-1]))\n",
    "    else:\n",
    "        layer_thick = np.ones(len(poros))*layer_thick    \n",
    "        \n",
    "    # Exception handling\n",
    "    \n",
    "    if (all(isinstance(x, (int, float)) for x in layer_thick) or all(isinstance(x, (int, float)) for x in poros) or all(isinstance(x, (int, float)) for x in perme)) == False:\n",
    "        raise TypeError('Not all array entries are integers or floats')\n",
    "    \n",
    "    elif len(layer_thick) != len(poros) or len(layer_thick) != len(perme):\n",
    "        raise TypeError('There are not sufficient porosity/permeability measurements for all layers')\n",
    "        \n",
    "    elif (np.ndim(layer_thick) or np.ndim(poros) or np.ndim(perme)) != 1:\n",
    "        raise TypeError('Arguments should be 1D numpy.ndarrays')\n",
    "    \n",
    "    # Sorting data in descending order of perme/poros\n",
    "    \n",
    "    ratio = perme/poros\n",
    "    layer_thick = np.flip(layer_thick[np.argsort(ratio)], axis = 0)\n",
    "    poros = np.flip(poros[np.argsort(ratio)], axis = 0)\n",
    "    perme = np.flip(perme[np.argsort(ratio)], axis = 0)\n",
    "    \n",
    "    # Flow and storage capacities\n",
    "    \n",
    "    storage_cap = poros*layer_thick\n",
    "    flow_cap = perme*layer_thick\n",
    "    \n",
    "    # Cumulative flow and storage capacities\n",
    "    \n",
    "    cumul_storage_cap = np.zeros(len(flow_cap)+1)\n",
    "    cumul_flow_cap = np.zeros(len(flow_cap)+1)\n",
    "    \n",
    "    cumul_storage_cap[1] = storage_cap[0]\n",
    "    cumul_flow_cap[1] = flow_cap[0]\n",
    "        \n",
    "    for i in range(2,len(flow_cap)+1):\n",
    "        cumul_storage_cap[i] = cumul_storage_cap[i-1] + storage_cap[i-1]\n",
    "        cumul_flow_cap[i] = cumul_flow_cap[i-1] + flow_cap[i-1]\n",
    "                     \n",
    "    # Fractional flow and storage capacities\n",
    "    \n",
    "    frac_storage_cap = cumul_storage_cap/cumul_storage_cap[-1]\n",
    "    frac_flow_cap = cumul_flow_cap/cumul_flow_cap[-1]\n",
    "       \n",
    "    # Curve fitting\n",
    "    \n",
    "    weights = np.ones(len(frac_storage_cap)) # To ensure the fit converges to (0,0) and (1,1)\n",
    "    weights[0] = 1000; weights[-1] = 1000 \n",
    "    \n",
    "    poly_fit = np.polyfit(frac_storage_cap, frac_flow_cap, deg=3, w=weights)\n",
    "    \n",
    "    poly_fit = np.poly1d(poly_fit)\n",
    "    integral = np.polyint(poly_fit) \n",
    "    lorenz_coeff = (integral(1) - 0.5)/0.5\n",
    "    \n",
    "    if curve_plot == True:                                                    # To output Lorenz Curve plot                           \n",
    "         \n",
    "        # Plotting instructions \n",
    "        \n",
    "        lorenz_plot = plt.figure(figsize = (16,12))\n",
    "\n",
    "        plt.style.use('fivethirtyeight')\n",
    "\n",
    "        plt.plot([0,1],[0,1], label='Homogeneous Curve', color = 'red', linewidth = '3')\n",
    "        plt.plot(np.linspace(0,1,100), poly_fit(np.linspace(0,1,100)), linestyle = 'dashed', \\\n",
    "                 linewidth = '3', label = 'Polynomial Fit', color = 'black')\n",
    "        plt.fill_between(np.linspace(0,1,100), poly_fit(np.linspace(0,1,100)), np.linspace(0,1,100), alpha = 0.75)\n",
    "        plt.fill_between(np.linspace(0,1,100), poly_fit(np.linspace(0,1,100)), 1, alpha = 0.75, color = 'orange')\n",
    "    \n",
    "        plt.title('Lorenz Curve and Coefficient', size = 50)\n",
    "        plt.xlabel('Fraction of Total Storage Capacity (m)', size = 36)\n",
    "        plt.ylabel('Fraction of Total Flow Capacity (mD*m)', size = 36)\n",
    "        plt.legend(loc = 4, facecolor = '#C0C0C0', fontsize = 25)\n",
    "        plt.text(0.3, 0.7, 'lorenz_coeff = \\n %.3f' %lorenz_coeff, size = 40, horizontalalignment = 'center', \\\n",
    "                 bbox=dict(facecolor='none', edgecolor='black'))\n",
    "        plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.7, wspace=0.3, hspace=0.3)\n",
    "        plt.show()\n",
    "           \n",
    "    else:                                                                   # To output only Lorenz Coefficient\n",
    "        return 'lorenz_coeff = %.3f' %lorenz_coeff"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Docstrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculates Lorenz coefficient and curve of subsurface sample given porosity and permeability data.\n",
      "    :param poros: porosities in %\n",
      "    :type: 1D numpy.ndarray\n",
      "    :param perme: permeabilities in mD\n",
      "    :type: 1D numpy.ndarray\n",
      "    :param layer_thick: uniform layer thickness; default 0.25 m;\n",
      "                        if depth is not None, layer_thick is recalculated\n",
      "    :type: int or float\n",
      "    :param depth: default None for constant thickness; \n",
      "                  for variable layer thickness, enter 1D numpy.ndarray of depths in m\n",
      "    :param curve_plot: default False; set to True to display the Lorenz Curve\n",
      "    :type: boolean\n",
      "    :return:lorenz_coeff: Lorenz Coefficient rounded to 3 decimal places\n",
      "           :type: float\n",
      "           :lorenz_plot: (optional) figure with plot of Lorenz Curve   \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(lorenz_coefficient.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### E.g. 1: Lorenz coefficient with default parameters (Data set 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lorenz_coeff = 0.171'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('WellPorPermSample_1.xlsx')\n",
    "poros1 = pd.DataFrame(df, columns = ['Por (%)']); poros1 = poros1.to_numpy()\n",
    "perme1 = pd.DataFrame(df, columns = ['Perm (mD)']); perme1 = perme1.to_numpy()\n",
    "\n",
    "lorenz_coefficient(poros1, perme1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### E.g. 2: Lorenz curve and coefficient with irregular thicknesses (Data set 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mpyrc\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:49: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('WellPorPermSample_2.xlsx')\n",
    "depth2 = pd.DataFrame(df, columns = ['Depth (m)']); depth2 = depth2.to_numpy()\n",
    "poros2 = pd.DataFrame(df, columns = ['Por (%)']); poros2 = poros2.to_numpy()\n",
    "perme2 = pd.DataFrame(df, columns = ['Perm (mD)']); perme2 = perme2.to_numpy()\n",
    "\n",
    "lorenz_coefficient(poros2, perme2, depth = depth2, curve_plot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### E.g. 3: Lorenz coefficient with customized uniform thickness (Data set 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lorenz_coeff = 0.192'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('WellPorPermSample_3.xlsx')\n",
    "depth3 = pd.DataFrame(df, columns = ['Depth (m)']); depth3 = depth3.to_numpy()\n",
    "poros3 = pd.DataFrame(df, columns = ['Por (%)']); poros3 = poros3.to_numpy()\n",
    "perme3 = pd.DataFrame(df, columns = ['Perm (mD)']); perme3 = perme3.to_numpy()\n",
    "\n",
    "lorenz_coefficient(poros3, perme3, layer_thick = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This is a set of functions to calculate and plot the Lorenz coefficient. Appreciation to **Alan Scherman** for writing this code and providing this documentation as part of the SURI program at The University of Texas at Austin.  \n",
    "\n",
    "If you want to learn more about data analytics and machine learning in Python, The Texas Center for Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "We hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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