MRST_SPE10_optimization_python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MRST_SPE10_optimization_python.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "_vteBRb33_QD",
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        "S_buA7tw6yeA"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOQ4aPPyaFJF/rWHirlrhQD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/MRST-simulations/blob/master/MRST_SPE10_optimization_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dy3ztDIU25g6"
      },
      "source": [
        "# Well placement optimization in SPE 10 reservoir model\n",
        "\n",
        "This is my experimental notebook to conduct optimization of well placement for an inverted quarter-five (Q5) waterflooding in SPE 10 reservoir model. The MATLAB script of MRST is ported with Python in Google Colab, working in this way; an optimizer in Python optimizes a function that carries the whole MATLAB code and returns objective function.\n",
        "\n",
        "The objective function is to minimize OIP (=OOIP-cumulative production). Example of optimizer here is Bayesian optimization. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vteBRb33_QD"
      },
      "source": [
        "## Install Octave in Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cU7WGbS20Je",
        "outputId": "46fe740b-7bb1-4f8e-e3fc-df86745cbb54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!apt install octave"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
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            "  libcupsfilters1 libcupsimage2 libcxsparse3 libdata-dump-perl libemf1\n",
            "  libencode-locale-perl libfftw3-single3 libfile-listing-perl libfltk-gl1.3\n",
            "  libfltk1.3 libfont-afm-perl libgail-common libgail18 libglpk40\n",
            "  libgraphicsmagick++-q16-12 libgraphicsmagick-q16-3 libgs9 libgs9-common\n",
            "  libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libhtml-form-perl\n",
            "  libhtml-format-perl libhtml-parser-perl libhtml-tagset-perl\n",
            "  libhtml-tree-perl libhttp-cookies-perl libhttp-daemon-perl libhttp-date-perl\n",
            "  libhttp-message-perl libhttp-negotiate-perl libijs-0.35 libio-html-perl\n",
            "  libio-socket-ssl-perl libjbig2dec0 liblqr-1-0 liblua5.3-0\n",
            "  liblwp-mediatypes-perl liblwp-protocol-https-perl libmagick++-6.q16-7\n",
            "  libmagickcore-6.q16-3 libmagickwand-6.q16-3 libmailtools-perl libmetis5\n",
            "  libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl libnotify4\n",
            "  liboctave4 libosmesa6 libplot2c2 libportaudio2 libpstoedit0c2a libqrupdate1\n",
            "  libqscintilla2-qt5-13 libqscintilla2-qt5-l10n libqt5opengl5 libqt5svg5\n",
            "  libsuitesparseconfig5 libtext-unidecode-perl libtimedate-perl\n",
            "  libtry-tiny-perl libumfpack5 liburi-perl libwmf0.2-7 libwww-perl\n",
            "  libwww-robotrules-perl libwxbase3.0-0v5 libwxgtk3.0-0v5 libxml-libxml-perl\n",
            "  libxml-namespacesupport-perl libxml-parser-perl libxml-sax-base-perl\n",
            "  libxml-sax-expat-perl libxml-sax-perl libzip4 netbase notification-daemon\n",
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            "  | ivtools-bin | tgif | transfig debhelper texlive-base texlive-latex-base\n",
            "  texlive-generic-recommended texlive-fonts-recommended\n",
            "The following NEW packages will be installed:\n",
            "  aglfn epstool fonts-droid-fallback fonts-noto-mono ghostscript gnuplot-data\n",
            "  gnuplot-qt gsfonts imagemagick-6-common info install-info libamd2\n",
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            "  libcupsfilters1 libcupsimage2 libcxsparse3 libdata-dump-perl libemf1\n",
            "  libencode-locale-perl libfftw3-single3 libfile-listing-perl libfltk-gl1.3\n",
            "  libfltk1.3 libfont-afm-perl libgail-common libgail18 libglpk40\n",
            "  libgraphicsmagick++-q16-12 libgraphicsmagick-q16-3 libgs9 libgs9-common\n",
            "  libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libhtml-form-perl\n",
            "  libhtml-format-perl libhtml-parser-perl libhtml-tagset-perl\n",
            "  libhtml-tree-perl libhttp-cookies-perl libhttp-daemon-perl libhttp-date-perl\n",
            "  libhttp-message-perl libhttp-negotiate-perl libijs-0.35 libio-html-perl\n",
            "  libio-socket-ssl-perl libjbig2dec0 liblqr-1-0 liblua5.3-0\n",
            "  liblwp-mediatypes-perl liblwp-protocol-https-perl libmagick++-6.q16-7\n",
            "  libmagickcore-6.q16-3 libmagickwand-6.q16-3 libmailtools-perl libmetis5\n",
            "  libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl libnotify4\n",
            "  liboctave4 libosmesa6 libplot2c2 libportaudio2 libpstoedit0c2a libqrupdate1\n",
            "  libqscintilla2-qt5-13 libqscintilla2-qt5-l10n libqt5opengl5 libqt5svg5\n",
            "  libsuitesparseconfig5 libtext-unidecode-perl libtimedate-perl\n",
            "  libtry-tiny-perl libumfpack5 liburi-perl libwmf0.2-7 libwww-perl\n",
            "  libwww-robotrules-perl libwxbase3.0-0v5 libwxgtk3.0-0v5 libxml-libxml-perl\n",
            "  libxml-namespacesupport-perl libxml-parser-perl libxml-sax-base-perl\n",
            "  libxml-sax-expat-perl libxml-sax-perl libzip4 netbase notification-daemon\n",
            "  octave octave-common octave-doc perl-openssl-defaults poppler-data pstoedit\n",
            "  tex-common texinfo\n",
            "0 upgraded, 103 newly installed, 0 to remove and 14 not upgraded.\n",
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            "Selecting previously unselected package libmagick++-6.q16-7:amd64.\n",
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            "Selecting previously unselected package libtext-unidecode-perl.\n",
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            "\n",
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            "update-alternatives: using /usr/bin/gnuplot-qt to provide /usr/bin/gnuplot (gnuplot) in auto mode\n",
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            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LfNk8N0kEId"
      },
      "source": [
        "## Cloning repositories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axdabnci5WCV",
        "outputId": "68a3f2cf-1259-4b12-8e35-0d316d9c907c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/yohanesnuwara/MRST-simulations"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'MRST-simulations'...\n",
            "remote: Enumerating objects: 260, done.\u001b[K\n",
            "remote: Total 260 (delta 0), reused 0 (delta 0), pack-reused 260\u001b[K\n",
            "Receiving objects: 100% (260/260), 27.90 MiB | 9.64 MiB/s, done.\n",
            "Resolving deltas: 100% (100/100), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EL81aobrJbYa",
        "outputId": "77a264ac-bfac-4760-b214-ba9585ff51fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/yohanesnuwara/reservoir_datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'reservoir_datasets'...\n",
            "remote: Enumerating objects: 69, done.\u001b[K\n",
            "remote: Counting objects: 100% (69/69), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 189 (delta 22), reused 67 (delta 20), pack-reused 120\u001b[K\n",
            "Receiving objects: 100% (189/189), 97.03 MiB | 14.51 MiB/s, done.\n",
            "Resolving deltas: 100% (44/44), done.\n",
            "Checking out files: 100% (138/138), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhdQzMnsKwVW",
        "outputId": "5d5339a9-35e8-406c-81e7-7d8dd53d5e81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://bitbucket.org/mrst/mrst-core.git"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'mrst-core'...\n",
            "remote: Counting objects: 8934, done.\u001b[K\n",
            "remote: Compressing objects: 100% (4594/4594), done.\u001b[K\n",
            "remote: Total 8934 (delta 6766), reused 6245 (delta 4254)\u001b[K\n",
            "Receiving objects: 100% (8934/8934), 5.71 MiB | 4.53 MiB/s, done.\n",
            "Resolving deltas: 100% (6766/6766), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiT3dGFZQuHL",
        "outputId": "3c5588de-a983-4481-8565-3016255dd60c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://yohanesnuwara@bitbucket.org/mrst/mrst-autodiff.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'mrst-autodiff'...\n",
            "remote: Counting objects: 40863, done.\u001b[K\n",
            "remote: Compressing objects: 100% (12732/12732), done.\u001b[K\n",
            "remote: Total 40863 (delta 29924), reused 37400 (delta 27804)\u001b[K\n",
            "Receiving objects: 100% (40863/40863), 15.26 MiB | 8.31 MiB/s, done.\n",
            "Resolving deltas: 100% (29924/29924), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooM1l42m40nl"
      },
      "source": [
        "## Main MATLAB program"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5perAWYJKVS",
        "outputId": "4fea486f-260b-4df1-8323-9ce814a7eece",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%writefile addpaths.m\n",
        "addpath '/content/mrst-core/utils/units'\n",
        "addpath '/content/mrst-core/gridprocessing'\n",
        "addpath '/content/mrst-core/utils'\n",
        "addpath '/content/mrst-core/modules'\n",
        "addpath '/content/mrst-core/solvers'\n",
        "addpath '/content/mrst-core/utils/gridtools'\n",
        "addpath '/content/mrst-core/params/rock'\n",
        "addpath '/content/mrst-core/params/wells_and_bc'\n",
        "addpath '/content/mrst-core/params'\n",
        "addpath '/content/MRST-simulations/modules/incomp/fluid/incompressible'\n",
        "addpath '/content/MRST-simulations/modules/incomp'\n",
        "addpath '/content/MRST-simulations/modules/incomp/fluid/utils'\n",
        "addpath '/content/MRST-simulations/modules/incomp/transport'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing addpaths.m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCOl8A5Q43FZ",
        "outputId": "142b4848-4d57-4e62-f19a-cfd7d5bdee96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%writefile SPE10_waterflooding.m\n",
        "addpaths\n",
        "\n",
        "%% Set up model and parameters\n",
        "T           = 5*year;\n",
        "nlayer      = 1; % number of layers of SPE 10 model to be simulated\n",
        "startlayer  = 4; % the SPE 10 model starts from layer i-th defined\n",
        "dims        = [60 220 nlayer];\n",
        "domain      = dims.*[20 10 2]*ft;\n",
        "G           = computeGeometry(cartGrid(dims,domain));\n",
        "\n",
        "rock        = getSPE10rock((1:dims(1)), (1:dims(2)), ... \n",
        "              startlayer:startlayer+(nlayer-1));\n",
        "            \n",
        "rock.poro = max(rock.poro,.0005);\n",
        "\n",
        "hT = computeTrans(G, rock); % compute transmissibility \n",
        "%x0 = initState(G,W,100*barsa, [0 1]); % initialize\n",
        "\n",
        "%% Set fluid model\n",
        "fluid = initSimpleFluid('mu' , [   1,    1] .* centi*poise     , ...\n",
        "                        'rho', [1000,  850] .* kilogram/meter^3, ...\n",
        "                        'n'  , [   2,    2]);\n",
        "\n",
        "%% Set parameters describing the well\n",
        "wtype    = {'bhp', 'bhp', 'bhp', 'bhp', 'bhp'};\n",
        "wtarget  = [200,   200,   200,   200,   500] .* barsa();\n",
        "wrad     = [0.125, 0.125, 0.125, 0.125, 0.125] .* meter;\n",
        "wloc     = [  1,   60,     1,   60,  30;\n",
        "              1,    1,   220,  220, 111];\n",
        "wname    = {'P1', 'P2', 'P3', 'P4', 'I'};\n",
        "\n",
        "W = [];\n",
        "for w = 1 : numel(wtype)\n",
        "  if w < 5\n",
        "    % production well is oil\n",
        "    phase = [0 1];\n",
        "  else\n",
        "    % production well is water\n",
        "    phase = [1 0];\n",
        "  endif\n",
        "  W = verticalWell(W, G, rock, wloc(1,w), wloc(2,w), 1, ...\n",
        "      'Type', wtype{w}, 'Val', wtarget(w), ...\n",
        "      'Radius', wrad(w), 'Name', wname{w}, ...\n",
        "      'InnerProduct', 'ip_tpf', 'Comp_i', phase);\n",
        "end\n",
        "\n",
        "x0 = initState(G,W,100*barsa, [0 1]); % initialize\n",
        "\n",
        "%% Simulation\n",
        "M = 50;\n",
        "x = x0;\n",
        "[dt,dT] = deal(zeros(M,1), T/M);\n",
        "wellSol = cell(M,3);\n",
        "oip     = zeros(M,3);\n",
        "n = 1;\n",
        "\n",
        "for i=1:M\n",
        "    x  = incompTPFA(x, G, hT, fluid, 'wells', W);\n",
        "    x  = implicitTransport(x, G, dT, rock, fluid, 'wells', W);    \n",
        "        \n",
        "    dt(i) = dT;\n",
        "    oip(i,n) = sum(x.s(:,2).*poreVolume(G,rock));\n",
        "    wellSol{i,n} = getWellSol(W,x, fluid);\n",
        "end\n",
        "\n",
        "%% Print the Oil in Place\n",
        "display(oip(50));"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting SPE10_waterflooding.m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSjsYhEF6CX4"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tTJjcYY6EfF",
        "outputId": "0a0b83b9-b392-47f5-a4e0-dd959b46309b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%writefile /content/mrst-core/modules/getSPE10rock.m\n",
        "function rock = getSPE10rock(varargin)\n",
        "%Define rock properties for Model 2 of tenth SPE CSP\n",
        "%\n",
        "% SYNOPSIS:\n",
        "%   rock = getSPE10rock\n",
        "%   rock = getSPE10rock(layers)\n",
        "%   rock = getSPE10rock(I, J, K)\n",
        "%\n",
        "% PARAMETERS:\n",
        "%   layers - Which of the 85 model layers to include in a specific test.\n",
        "%            OPTIONAL.  If unspecified, all 85 layers (for a total of\n",
        "%            60-by-220-by-85 == 1,122,000 cells) are included.\n",
        "%\n",
        "%            Some possible values are\n",
        "%               layers = ( 1 : 35).';  %  Tarbert formation\n",
        "%               layers = (36 : 85).';  %  Upper Ness formation\n",
        "%\n",
        "%   I,J,K  - Global Cartesian indices identifying subset of rock data.\n",
        "%            Useful, e.g., in extracting lateral sections.  Arrays must\n",
        "%            satisfy\n",
        "%\n",
        "%               ALL((0 < I) & (I <=  60)) && ...\n",
        "%               ALL((0 < J) & (J <= 220)) && ...\n",
        "%               ALL((0 < K) & (K <=  85))\n",
        "%\n",
        "%            OPTIONAL.  If unspecified, treated as I=1:60,J=1:220,K=1:85\n",
        "%            (i.e., the entire model; 1,122,000 cells).  In other words,\n",
        "%            equivalently to an unspecified 'layers' input.\n",
        "%\n",
        "% RETURNS:\n",
        "%   rock - Rock structure having fields 'perm' and 'poro' pertaining to\n",
        "%          the specified layers or cell subset.\n",
        "%\n",
        "% NOTE:\n",
        "%   The permeability data is returned in strict SI units (metres squared).\n",
        "%\n",
        "% EXAMPLE:\n",
        "%   rock = getSPE10rock(85)\n",
        "%\n",
        "% SEE ALSO:\n",
        "%   `getSPE10setup`, `make_spe10_data`.\n",
        "\n",
        "%{\n",
        "Copyright 2009-2019 SINTEF Digital, Mathematics & Cybernetics.\n",
        "\n",
        "This file is part of The MATLAB Reservoir Simulation Toolbox (MRST).\n",
        "\n",
        "MRST is free software: you can redistribute it and/or modify\n",
        "it under the terms of the GNU General Public License as published by\n",
        "the Free Software Foundation, either version 3 of the License, or\n",
        "(at your option) any later version.\n",
        "\n",
        "MRST is distributed in the hope that it will be useful,\n",
        "but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
        "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
        "GNU General Public License for more details.\n",
        "\n",
        "You should have received a copy of the GNU General Public License\n",
        "along with MRST.  If not, see <http://www.gnu.org/licenses/>.\n",
        "%}\n",
        "\n",
        "   assert (all(cellfun(@isnumeric, varargin)), ...\n",
        "           'Input arrays must be numeric.');\n",
        "\n",
        "   % Load data in pre-processed form.\n",
        "   rock = load_mat_file();\n",
        "\n",
        "   % Extract caller's requested subset from 'rock' data\n",
        "   %\n",
        "   % Return only PERM and PORO data and exclude any other information that\n",
        "   % might be stored in on-disk representation of rock data.\n",
        "   %\n",
        "   ix   = define_subset(varargin{:});\n",
        "\n",
        "   rock = struct('perm', rock.perm(ix, :), ...\n",
        "                 'poro', rock.poro(ix   ));\n",
        "end\n",
        "\n",
        "%--------------------------------------------------------------------------\n",
        "\n",
        "function ix = define_subset(varargin)\n",
        "   [Nx, Ny, Nz] = deal(60, 220, 85);\n",
        "                                          % ()\n",
        "   [I, J, K]    = deal(1:Nx, 1:Ny, 1:Nz); % Default (entire dataset)\n",
        "\n",
        "   if nargin == 1\n",
        "                                          % (layers)\n",
        "      K = varargin{1};                    % Caller specified ind. layers\n",
        "\n",
        "   elseif nargin == 3\n",
        "                                          % (I, J, K)\n",
        "      [I, J, K] = deal(varargin{:});      % Caller specified box\n",
        "\n",
        "   elseif nargin ~= 0\n",
        "      file = mfilename();\n",
        "      error(['Syntax is\\n\\t'              , ...\n",
        "             'rock = %s         %% or\\n\\t', ...\n",
        "             'rock = %s(layers) %% or\\n\\t', ...\n",
        "             'rock = %s(I, J, K)'], file, file, file);\n",
        "   end\n",
        "\n",
        "   validate_range(I, Nx);\n",
        "   validate_range(J, Ny);\n",
        "   validate_range(K, Nz);\n",
        "\n",
        "   [I, J, K] = ndgrid(I, J, K);\n",
        "\n",
        "   ix = sub2ind([Nx, Ny, Nz], I(:), J(:), K(:));\n",
        "end\n",
        "\n",
        "%--------------------------------------------------------------------------\n",
        "\n",
        "function rock = load_mat_file()\n",
        "   %rdir = getDatasetPath('spe10', 'skipAvailableCheck', true);\n",
        "   rdir = '/content/mrst-core/data/SPE10';\n",
        "   rock_file = fullfile(rdir, 'spe10_rock');\n",
        "\n",
        "   if ~exist([rock_file, '.mat'], 'file')\n",
        "      ok = makeSPE10DataAvailable();\n",
        "\n",
        "      if ~ok\n",
        "         error('SPE10Download:Fail', 'Failed to download SPE 10 dataset');\n",
        "      end\n",
        "   end\n",
        "\n",
        "   data = load(rock_file);\n",
        "   rock = data.rock; % Fa-Fa-Fa\n",
        "end\n",
        "\n",
        "%--------------------------------------------------------------------------\n",
        "\n",
        "function validate_range(i, n)\n",
        "   assert (all((0 < i) & (i <= n)), ...\n",
        "           '%s outside valid range 1:%d', inputname(1), n);\n",
        "end"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing /content/mrst-core/modules/getSPE10rock.m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxHLVKKFFOBn",
        "outputId": "e6b97857-7766-47a2-e52d-7fe73e217f7b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%writefile /content/mrst-core/modules/getWellSol.m\n",
        "function wellSol = getWellSol(W, x, fluid)\n",
        "\n",
        "mu = fluid.properties();\n",
        "wellSol(numel(W))=struct;\n",
        "for i=1:numel(W)\n",
        "    out = min(x.wellSol(i).flux,0); iout = out<0;\n",
        "    in  = max(x.wellSol(i).flux,0); iin  = in>0;\n",
        "    \n",
        "    swc  = x.s(W(i).cells,:);\n",
        "    lamc = bsxfun(@rdivide,fluid.relperm(swc), mu); \n",
        "    fc   = lamc(:,1)./sum(lamc,2);\n",
        "    \n",
        "    sww  = W(i).compi;\n",
        "    lamw = bsxfun(@rdivide,fluid.relperm(sww), mu);\n",
        "    fw   = lamw(:,1)./sum(lamw,2);\n",
        "    \n",
        "    wellSol(i).name = W(i).name;\n",
        "    wellSol(i).bhp  = x.wellSol(i).pressure;\n",
        "    wellSol(i).wcut = iout.*fc + iin.*fw;\n",
        "    wellSol(i).Sw   = iout.*swc(:,1) + iin.*sww(:,1);\n",
        "    wellSol(i).qW   = out.*fc + in.*fw;\n",
        "    wellSol(i).qWs  = sum(wellSol(i).qW);\n",
        "    wellSol(i).qO   = out.*(1-fc) + in.*(1-fw);\n",
        "    wellSol(i).qOs  = sum(wellSol(i).qO);\n",
        "end"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing /content/mrst-core/modules/getWellSol.m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_buA7tw6yeA"
      },
      "source": [
        "## Access SPE 10 model data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx3AuuSO6doX"
      },
      "source": [
        "!mkdir /content/mrst-core/data"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wT1b8ddC6etg"
      },
      "source": [
        "!mv /content/reservoir_datasets/MRST/SPE10 /content/mrst-core/data"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_p4tGvyG6bKW"
      },
      "source": [
        "## Execute main program"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eoaUE2EOG_Yn"
      },
      "source": [
        "The result is OIP after 5 years of production"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lwpvb0F65MIb",
        "outputId": "bd15c84c-1a6c-440a-f4f4-a42446e50500",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!octave -W SPE10_waterflooding.m"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   1.0147e+04\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4w33sLQJLDA-"
      },
      "source": [
        "out = !octave -W SPE10_waterflooding.m"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ciMBOdX2LHSP",
        "outputId": "0f855114-56a0-4e5e-e7de-2e5ba3509906",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.float64(out)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10147.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8A2NYmQGjyr"
      },
      "source": [
        "## Well placement optimization with Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fv6capj-LMip"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIuB8soLLjym",
        "outputId": "cc806f7d-0dd6-4938-9942-75827564c514",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install bayesian-optimization\n",
        "from bayes_opt import BayesianOptimization"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting bayesian-optimization\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/7a/fd8059a3881d3ab37ac8f72f56b73937a14e8bb14a9733e68cc8b17dbe3c/bayesian-optimization-1.2.0.tar.gz\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (1.18.5)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (0.17.0)\n",
            "Building wheels for collected packages: bayesian-optimization\n",
            "  Building wheel for bayesian-optimization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bayesian-optimization: filename=bayesian_optimization-1.2.0-cp36-none-any.whl size=11685 sha256=38453a95c6d781d0c4f11f56daebaac438d89d52c7a579bed71d8d28f9d4a2dc\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/56/ae/e0e3c1fc1954dc3ec712e2df547235ed072b448094d8f94aec\n",
            "Successfully built bayesian-optimization\n",
            "Installing collected packages: bayesian-optimization\n",
            "Successfully installed bayesian-optimization-1.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdLKR53RGsgu",
        "outputId": "bed5952e-05ef-447b-9868-e224f07cca92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%%writefile calculate_oip_spe10.m\n",
        "%% Script that reads the well location from input text file, and then calculates\n",
        "%% OIP after several years of production from \"SPE10_waterflooding.m\" program\n",
        "\n",
        "%% Read well location from input text file\n",
        "fileID = fopen('input.txt','r');\n",
        "formatSpec = '%f';\n",
        "P = fscanf(fileID,formatSpec);\n",
        "\n",
        "%% Output is 1D array. Reshape to x and y coords\n",
        "wloc = reshape(P,2,[]);\n",
        "\n",
        "%% The whole SPE10_waterflooding.m program\n",
        "addpaths\n",
        "\n",
        "%% Set up model and parameters\n",
        "T           = 5*year;\n",
        "nlayer      = 1; % number of layers of SPE 10 model to be simulated\n",
        "startlayer  = 4; % the SPE 10 model starts from layer i-th defined\n",
        "dims        = [60 220 nlayer];\n",
        "domain      = dims.*[20 10 2]*ft;\n",
        "G           = computeGeometry(cartGrid(dims,domain));\n",
        "\n",
        "rock        = getSPE10rock((1:dims(1)), (1:dims(2)), ... \n",
        "              startlayer:startlayer+(nlayer-1));\n",
        "            \n",
        "rock.poro = max(rock.poro,.0005);\n",
        "\n",
        "hT = computeTrans(G, rock); % compute transmissibility \n",
        "%x0 = initState(G,W,100*barsa, [0 1]); % initialize\n",
        "\n",
        "%% Set fluid model\n",
        "fluid = initSimpleFluid('mu' , [   1,    1] .* centi*poise     , ...\n",
        "                        'rho', [1000,  850] .* kilogram/meter^3, ...\n",
        "                        'n'  , [   2,    2]);\n",
        "\n",
        "%% Set parameters describing the well. wloc has been updated above.\n",
        "wtype    = {'bhp', 'bhp', 'bhp', 'bhp', 'bhp'};\n",
        "wtarget  = [200,   200,   200,   200,   500] .* barsa();\n",
        "wrad     = [0.125, 0.125, 0.125, 0.125, 0.125] .* meter;\n",
        "wname    = {'P1', 'P2', 'P3', 'P4', 'I'};\n",
        "\n",
        "W = [];\n",
        "for w = 1 : numel(wtype)\n",
        "  if w < 5\n",
        "    % production well is oil\n",
        "    phase = [0 1];\n",
        "  else\n",
        "    % production well is water\n",
        "    phase = [1 0];\n",
        "  endif\n",
        "  W = verticalWell(W, G, rock, wloc(1,w), wloc(2,w), 1, ...\n",
        "      'Type', wtype{w}, 'Val', wtarget(w), ...\n",
        "      'Radius', wrad(w), 'Name', wname{w}, ...\n",
        "      'InnerProduct', 'ip_tpf', 'Comp_i', phase);\n",
        "end\n",
        "\n",
        "x0 = initState(G,W,100*barsa, [0 1]); % initialize\n",
        "\n",
        "%% Simulation\n",
        "M = 50;\n",
        "x = x0;\n",
        "[dt,dT] = deal(zeros(M,1), T/M);\n",
        "wellSol = cell(M,3);\n",
        "oip     = zeros(M,3);\n",
        "n = 1;\n",
        "\n",
        "for i=1:M\n",
        "    x  = incompTPFA(x, G, hT, fluid, 'wells', W);\n",
        "    x  = implicitTransport(x, G, dT, rock, fluid, 'wells', W);    \n",
        "        \n",
        "    dt(i) = dT;\n",
        "    oip(i,n) = sum(x.s(:,2).*poreVolume(G,rock));\n",
        "    wellSol{i,n} = getWellSol(W,x, fluid);\n",
        "end\n",
        "\n",
        "%% Print the Oil in Place\n",
        "display(oip(50));"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting calculate_oip_spe10.m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGwbgFzDKYhJ"
      },
      "source": [
        "def objective(x, y):\n",
        "  \"\"\"\n",
        "  The objective function returns the OIP (calculated from the MATLAB program\n",
        "  calculate_oip_spe10.m) that will be minimized\n",
        "\n",
        "  An optimizer will optimize the injector locations ONE BY ONE. This function\n",
        "  should be put in a loop.\n",
        "\n",
        "  Input:\n",
        "\n",
        "  x, y coordinates of one of the injector wells\n",
        "  \"\"\"\n",
        "  # change location of one of the injectors\n",
        "  wloc[i] = np.array([x, y])\n",
        "\n",
        "  # save the locations into input text file\n",
        "  np.savetxt('input.txt', wloc)\n",
        "\n",
        "  # execute calculate_oip_spe10.m MATLAB program\n",
        "  oip = !octave -W calculate_oip_spe10.m\n",
        "\n",
        "  return np.float64(oip)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQfeSeJTL7fQ"
      },
      "source": [
        "# Initial injector well locations\n",
        "wloc = np.array([[1,1],[60,1],[1,220],[60,220],[30,111]])\n",
        "x = wloc[:,0]\n",
        "y = wloc[:,1]"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8sfmRwrMHGi",
        "outputId": "4a8add09-8978-47b8-d21b-d151371d6d83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "objective(11.67,100.76)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11520.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWrQObX0LgD6",
        "outputId": "8234e86c-2c6d-4699-e48f-04ff131ecd4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "for i in range(2):\n",
        "  # Define search bound for each injector\n",
        "  if i == 0:\n",
        "    # First well (SW)\n",
        "    pbounds = {\"x\": (0, 30), \"y\": (0, 111)}\n",
        "  if i == 1:\n",
        "    # Second well (SE)\n",
        "    pbounds = {\"x\": (30, 60), \"y\": (0, 111)}\n",
        "  if i == 2:\n",
        "    # Third well (NW)\n",
        "    pbounds = {\"x\": (0, 30), \"y\": (111, 220)}\n",
        "  if i == 3:\n",
        "    # Fourth well (NE)\n",
        "    pbounds = {\"x\": (30, 60), \"y\": (111, 220)}\n",
        "  \n",
        "  # Optimization\n",
        "  optimizer = BayesianOptimization(\n",
        "      f=objective,\n",
        "      pbounds=pbounds,\n",
        "      verbose=2,  # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
        "      random_state=42,\n",
        "  )\n",
        "\n",
        "  print('Optimization at injector well:', i+1)\n",
        "  optimizer.maximize(init_points=5, n_iter=20)\n",
        "\n",
        "  x_reloc = optimizer.max['params']['x']\n",
        "  y_reloc = optimizer.max['params']['y']  "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization at injector well: 1\n",
            "|   iter    |  target   |     x     |     y     |\n",
            "-------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bayes_opt/target_space.py\u001b[0m in \u001b[0;36mprobe\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m             \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: (11.236203565420874, 105.5292880115007)",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-77396c3fce50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Optimization at injector well:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m   \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m   \u001b[0mx_reloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mmaximize\u001b[0;34m(self, init_points, n_iter, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0miteration\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_probe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlazy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bounds_transformer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bayes_opt/bayesian_optimization.py\u001b[0m in \u001b[0;36mprobe\u001b[0;34m(self, params, lazy)\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOPTIMIZATION_STEP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bayes_opt/target_space.py\u001b[0m in \u001b[0;36mprobe\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    193\u001b[0m             \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m             \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bayes_opt/target_space.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, params, target)\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprobe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: all the input arrays must have same number of dimensions, but the array at index 0 has 1 dimension(s) and the array at index 1 has 2 dimension(s)"
          ]
        }
      ]
    }
  ]
}