module2_lesson6.ipynb

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    "colab": {
      "name": "module2_lesson6.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMb1mwVv9hBZTejxT7Nwxi8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexeynick/msu_oil_gas/blob/main/module2_lesson6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTbQUBdRl2s7"
      },
      "source": [
        "## Задачи модуля №2. Характеристики вытеснения"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPj-MO-omENw"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import io\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.graph_objs import *\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "from plotly.subplots import make_subplots\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6lPcN_Kap5x"
      },
      "source": [
        "anno_font = dict(\n",
        "        family=\"arial bold\",\n",
        "        size=36,\n",
        "        color=\"Black\"\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "3VTTXxwPlwXN",
        "outputId": "c38f41b2-21a7-4268-93e2-38a94bc35ee8"
      },
      "source": [
        "df_object = pd.read_csv('https://raw.githubusercontent.com/alexeynick/msu_oil_gas/main/datasets/dynamic_object_5.csv')\n",
        "df_object.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>year</th>\n",
              "      <th>oil_year</th>\n",
              "      <th>production_frac</th>\n",
              "      <th>production_frac_rem</th>\n",
              "      <th>oil_total</th>\n",
              "      <th>production_frac_total</th>\n",
              "      <th>kin</th>\n",
              "      <th>water_year</th>\n",
              "      <th>water_total</th>\n",
              "      <th>water_cut</th>\n",
              "      <th>inject_year</th>\n",
              "      <th>inject_total</th>\n",
              "      <th>inject_comp_year</th>\n",
              "      <th>inject_comp_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2005</td>\n",
              "      <td>55.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>6661</td>\n",
              "      <td>74.06</td>\n",
              "      <td>0.307</td>\n",
              "      <td>429.7</td>\n",
              "      <td>21933</td>\n",
              "      <td>87.0</td>\n",
              "      <td>410.6</td>\n",
              "      <td>22481.0</td>\n",
              "      <td>101.1</td>\n",
              "      <td>103.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2006</td>\n",
              "      <td>57.7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>6719</td>\n",
              "      <td>74.70</td>\n",
              "      <td>0.309</td>\n",
              "      <td>388.5</td>\n",
              "      <td>22322</td>\n",
              "      <td>85.2</td>\n",
              "      <td>361.6</td>\n",
              "      <td>22842.6</td>\n",
              "      <td>98.0</td>\n",
              "      <td>103.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2007</td>\n",
              "      <td>56.7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.6</td>\n",
              "      <td>6776</td>\n",
              "      <td>75.33</td>\n",
              "      <td>0.312</td>\n",
              "      <td>215.6</td>\n",
              "      <td>22538</td>\n",
              "      <td>73.7</td>\n",
              "      <td>117.5</td>\n",
              "      <td>22960.1</td>\n",
              "      <td>55.5</td>\n",
              "      <td>102.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2008</td>\n",
              "      <td>61.1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6837</td>\n",
              "      <td>76.01</td>\n",
              "      <td>0.315</td>\n",
              "      <td>208.9</td>\n",
              "      <td>22747</td>\n",
              "      <td>70.7</td>\n",
              "      <td>156.2</td>\n",
              "      <td>23116.4</td>\n",
              "      <td>75.5</td>\n",
              "      <td>102.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2009</td>\n",
              "      <td>62.1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6899</td>\n",
              "      <td>76.70</td>\n",
              "      <td>0.318</td>\n",
              "      <td>202.4</td>\n",
              "      <td>22949</td>\n",
              "      <td>69.3</td>\n",
              "      <td>137.2</td>\n",
              "      <td>23253.5</td>\n",
              "      <td>68.2</td>\n",
              "      <td>102.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2010</td>\n",
              "      <td>60.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6959</td>\n",
              "      <td>77.37</td>\n",
              "      <td>0.320</td>\n",
              "      <td>205.3</td>\n",
              "      <td>23154</td>\n",
              "      <td>70.6</td>\n",
              "      <td>141.2</td>\n",
              "      <td>23394.8</td>\n",
              "      <td>69.5</td>\n",
              "      <td>101.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011</td>\n",
              "      <td>51.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.6</td>\n",
              "      <td>7011</td>\n",
              "      <td>77.94</td>\n",
              "      <td>0.323</td>\n",
              "      <td>192.5</td>\n",
              "      <td>23347</td>\n",
              "      <td>73.2</td>\n",
              "      <td>138.9</td>\n",
              "      <td>23533.6</td>\n",
              "      <td>73.4</td>\n",
              "      <td>101.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2012</td>\n",
              "      <td>49.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.6</td>\n",
              "      <td>7061</td>\n",
              "      <td>78.49</td>\n",
              "      <td>0.325</td>\n",
              "      <td>201.8</td>\n",
              "      <td>23548</td>\n",
              "      <td>75.4</td>\n",
              "      <td>144.5</td>\n",
              "      <td>23678.1</td>\n",
              "      <td>73.3</td>\n",
              "      <td>101.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2013</td>\n",
              "      <td>44.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>7105</td>\n",
              "      <td>78.99</td>\n",
              "      <td>0.327</td>\n",
              "      <td>213.5</td>\n",
              "      <td>23762</td>\n",
              "      <td>79.1</td>\n",
              "      <td>146.7</td>\n",
              "      <td>23824.9</td>\n",
              "      <td>71.1</td>\n",
              "      <td>101.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2014</td>\n",
              "      <td>47.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>7153</td>\n",
              "      <td>79.52</td>\n",
              "      <td>0.329</td>\n",
              "      <td>231.3</td>\n",
              "      <td>23993</td>\n",
              "      <td>79.5</td>\n",
              "      <td>107.7</td>\n",
              "      <td>23932.6</td>\n",
              "      <td>48.2</td>\n",
              "      <td>100.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    year  oil_year  ...  inject_comp_year  inject_comp_total\n",
              "35  2005      55.9  ...             101.1              103.2\n",
              "36  2006      57.7  ...              98.0              103.1\n",
              "37  2007      56.7  ...              55.5              102.7\n",
              "38  2008      61.1  ...              75.5              102.4\n",
              "39  2009      62.1  ...              68.2              102.1\n",
              "40  2010      60.3  ...              69.5              101.8\n",
              "41  2011      51.5  ...              73.4              101.6\n",
              "42  2012      49.6  ...              73.3              101.4\n",
              "43  2013      44.6  ...              71.1              101.1\n",
              "44  2014      47.3  ...              48.2              100.6\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "DDNzeubrl98m",
        "outputId": "06322d88-f36f-4106-a823-1d61c8c87323"
      },
      "source": [
        "df_graph = df_object[['year', 'oil_total', 'water_total']].copy()\n",
        "df_graph['oil_total'] = df_graph['oil_total'].astype(float)\n",
        "df_graph['water_total'] = df_graph['water_total'].astype(float)\n",
        "df_graph['water_total_deg_1'] = np.power(df_graph['water_total'].astype(float), -1)\n",
        "df_graph['water_total_ln'] = np.log(df_graph['water_total'].astype(float))\n",
        "df_graph['oil_total_ln'] = np.log(df_graph['oil_total'].astype(float))\n",
        "df_graph['water_to_oil_total'] = df_graph['water_total'].astype(float) / df_graph['oil_total'].astype(float)\n",
        "df_graph.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        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>year</th>\n",
              "      <th>oil_total</th>\n",
              "      <th>water_total</th>\n",
              "      <th>water_total_deg_1</th>\n",
              "      <th>water_total_ln</th>\n",
              "      <th>oil_total_ln</th>\n",
              "      <th>water_to_oil_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>90.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.010870</td>\n",
              "      <td>4.521789</td>\n",
              "      <td>4.499810</td>\n",
              "      <td>1.022222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1971</td>\n",
              "      <td>181.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>0.005263</td>\n",
              "      <td>5.247024</td>\n",
              "      <td>5.198497</td>\n",
              "      <td>1.049724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1972</td>\n",
              "      <td>359.0</td>\n",
              "      <td>378.0</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>5.934894</td>\n",
              "      <td>5.883322</td>\n",
              "      <td>1.052925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1973</td>\n",
              "      <td>754.0</td>\n",
              "      <td>796.0</td>\n",
              "      <td>0.001256</td>\n",
              "      <td>6.679599</td>\n",
              "      <td>6.625392</td>\n",
              "      <td>1.055703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1974</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>1324.0</td>\n",
              "      <td>0.000755</td>\n",
              "      <td>7.188413</td>\n",
              "      <td>7.069023</td>\n",
              "      <td>1.126809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  oil_total  ...  oil_total_ln  water_to_oil_total\n",
              "0  1970       90.0  ...      4.499810            1.022222\n",
              "1  1971      181.0  ...      5.198497            1.049724\n",
              "2  1972      359.0  ...      5.883322            1.052925\n",
              "3  1973      754.0  ...      6.625392            1.055703\n",
              "4  1974     1175.0  ...      7.069023            1.126809\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgEL1yD6OqHt"
      },
      "source": [
        "niz_total = 8995"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VfhRL6_w6Eh"
      },
      "source": [
        "def kambarov_func(x, a, b):\n",
        "    return a - b * (x ** -1)\n",
        "\n",
        "def kambarov_niz(a, b):\n",
        "    return a - (b * 0.02) ** 0.5\n",
        "\n",
        "def sazonov_func(x, a, b):\n",
        "    return a + b * np.log(x)\n",
        "\n",
        "def sazonov_niz(a, b):\n",
        "    return a + b * np.log(b / 0.02)\n",
        "\n",
        "def french_func(x, a, b):\n",
        "    return (a + 1) * x + b * (x ** 2)\n",
        "\n",
        "def french_niz(a, b):\n",
        "    fvk = 0.98\n",
        "    return (1 / (2 * b))  * (fvk / (1 - fvk) - a)\n",
        "\n",
        "def naz_sip_func(x, a, b):\n",
        "    return (a * x - b * (x ** 2)) / (1 - b * x)\n",
        "\n",
        "def naz_sip_niz(vvk, a, b):\n",
        "    return vvk / (a - 1 + b * vvk)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCq5Jb5Szwu7",
        "outputId": "0feee196-b4e3-4b8c-b6cb-fb6a8ad8ce44"
      },
      "source": [
        "xdata = df_graph[-25:]['water_total']\n",
        "ydata = df_graph[-25:]['oil_total']\n",
        "\n",
        "popt, pcov = curve_fit(kambarov_func, xdata, ydata)\n",
        "popt\n",
        "\n",
        "#print(popt)\n",
        "#print(kambarov_niz(*popt))\n",
        "\n",
        "#ydata_fit = func_kambarov(xdata, *popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.18132561e+03, 5.33728279e+07])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "nh11cNUV956w",
        "outputId": "26f52d9e-c88e-4c2d-adf0-a542c7993261"
      },
      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['water_total_deg_1'], y=df_graph['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['water_total_deg_1'], y=df_graph[-25:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['water_total_deg_1'], y=kambarov_func(xdata, *popt),\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Камбарова)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "\n",
        "fig['layout']['xaxis1']['title']='1 / Накопленный объем жидкости, 10^-6'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "fig['layout']['yaxis1']['title']='Накопленный объем нефти, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='1 / Накопленный объем жидкости, 10^-6'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленной добычи нефти от объема жидкости',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "metadata": {
        "id": "FldyUYG-P8Yr"
      },
      "source": [
        "frame_range = [10, 25]\n",
        "shift_range = [0, 15]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe3or7O0P8hx"
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      "source": [
        "data = []\n",
        "\n",
        "for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "  for frame in range(frame_range[0], frame_range[1] + 1):\n",
        "    if shift == 0:\n",
        "      xdata = df_graph[-frame:]['water_total']\n",
        "      ydata = df_graph[-frame:]['oil_total']\n",
        "    else:\n",
        "      xdata = df_graph[-(frame + shift):-shift]['water_total']\n",
        "      ydata = df_graph[-(frame + shift):-shift]['oil_total']      \n",
        "\n",
        "    popt, pcov = curve_fit(kambarov_func, xdata, ydata)\n",
        "    popt\n",
        "\n",
        "    data.append([shift, frame, kambarov_niz(*popt)])\n",
        "\n",
        "    #print(popt[0], popt[1], np.abs(kambarov_niz(popt[0], popt[1]) - niz_total))\n",
        "\n",
        "df_approx = pd.DataFrame(data, columns=['shift', 'frame', 'niz'])\n",
        "df_approx['err'] = np.abs(df_approx['niz'] - niz_total)"
      ],
      "execution_count": null,
      "outputs": []
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        "outputId": "ffb8693b-2d60-4a3a-a817-062fb24c0aa5"
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      "source": [
        "df_approx.head(5)"
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              "   shift  frame           niz          err\n",
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        "def print_approx_graph(df_approx):\n",
        "  fig = go.Figure()\n",
        "\n",
        "  for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "    df_shift_data = df_approx.query('shift == @shift')\n",
        "\n",
        "    fig.add_trace(go.Scatter(x=df_shift_data['frame'], y=df_shift_data['err'],\n",
        "                        mode='lines',\n",
        "                        name='График при сдвиге: {0}'.format(shift)))\n",
        "\n",
        "  fig.update_layout( \n",
        "      xaxis=dict(\n",
        "          domain=[0.2, 0.9],\n",
        "          title='Размер окна',\n",
        "      ),\n",
        "      yaxis=dict(\n",
        "          title='Ошибка предсказания',\n",
        "      ),\n",
        "      title={\n",
        "          'text': \"Зависимость ошибки предсказания НИЗ от параметров аппроксимации\",\n",
        "          'y':0.95,\n",
        "          'x':0.50,\n",
        "          'xanchor': 'center',\n",
        "          'yanchor': 'top'},\n",
        "      autosize=False,\n",
        "      width=1400,\n",
        "      height=700\n",
        "  )\n",
        "\n",
        "  fig.show()"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "0gSS3FnKXy8j"
      },
      "source": [
        "def df_to_plotly(df):\n",
        "    return {'z': df.values.tolist(),\n",
        "            'x': df.columns.tolist(),\n",
        "            'y': df.index.tolist()}"
      ],
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "metadata": {
        "id": "eGGH5dENMDtw"
      },
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        "def plot_approx_heatmap(df_approx_pvt):\n",
        "  fig = go.Figure(data=go.Heatmap(\n",
        "          df_to_plotly(df_approx_pvt),\n",
        "          colorscale='blues'))\n",
        "\n",
        "  fig.update_layout( \n",
        "      xaxis=dict(\n",
        "          title='Размер окна',\n",
        "      ),\n",
        "      yaxis=dict(\n",
        "          title='Сдвиг',\n",
        "      ),\n",
        "      title={\n",
        "          'text': \"Зависимость ошибки предсказания НИЗ от параметров аппроксимации\",\n",
        "          'y':0.95,\n",
        "          'x':0.50,\n",
        "          'xanchor': 'center',\n",
        "          'yanchor': 'top'},\n",
        "      autosize=False,\n",
        "      width=1400,\n",
        "      height=700\n",
        "  )\n",
        "\n",
        "  fig.show()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "D8F34E8OMMvi",
        "outputId": "f950aa22-880b-4195-a6ca-41d1b736dda3"
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      "source": [
        "df_approx_pvt = df_approx.pivot(index='shift', columns='frame', values='err')\n",
        "\n",
        "plot_approx_heatmap(df_approx_pvt)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWGpcWzYZHXa",
        "outputId": "7508569b-b0a2-40b1-f40a-892a3a002cde"
      },
      "source": [
        "xdata = df_graph[-17:-1]['water_total']\n",
        "ydata = df_graph[-17:-1]['oil_total']\n",
        "\n",
        "popt, pcov = curve_fit(kambarov_func, xdata, ydata)\n",
        "\n",
        "kambarov_niz(*popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8993.317215475843"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "hqgdN1yaZbSP",
        "outputId": "fbc06f60-b94a-43ce-ed49-3974a2474ea2"
      },
      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['water_total_deg_1'], y=df_graph['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-17:-1]['water_total_deg_1'], y=df_graph[-17:-1]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-17:-1]['water_total_deg_1'], y=kambarov_func(xdata, *popt),\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Камбарова)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_annotation(x=10**(-4), y=7000, text=\"НИЗ={0}\".format(int(kambarov_niz(*popt))), showarrow=False, yshift=10, font=anno_font,\n",
        "    row=1, col=2)\n",
        "\n",
        "fig['layout']['xaxis1']['title']='1 / Накопленный объем жидкости, 10^-6'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "fig['layout']['yaxis1']['title']='Накопленный объем нефти, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='1 / Накопленный объем жидкости, 10^-6'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленной добычи нефти от объема жидкости',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('32acd7c4-ab33-4ea9-ba43-54b0a7422ee3');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WI2dzBF3ZdQl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0T1bCGVQZdts",
        "outputId": "c789aa9c-af1d-4f5b-b29b-f746c99dff3a"
      },
      "source": [
        "xdata = df_graph[-20:]['oil_total']\n",
        "ydata = df_graph[-20:]['water_total']\n",
        "\n",
        "popt, pcov = curve_fit(french_func, xdata, ydata)\n",
        "popt\n",
        "\n",
        "#print(popt)\n",
        "#print(kambarov_niz(*popt))\n",
        "\n",
        "ydata_fit = french_func(xdata, *popt)\n",
        "ydata_fit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25    18242.958836\n",
              "26    18516.481317\n",
              "27    18803.318116\n",
              "28    19126.495760\n",
              "29    19429.452170\n",
              "30    19734.777102\n",
              "31    20059.957993\n",
              "32    20417.181816\n",
              "33    20736.030848\n",
              "34    21081.237008\n",
              "35    21411.207890\n",
              "36    21761.940318\n",
              "37    22109.389191\n",
              "38    22484.255083\n",
              "39    22868.481401\n",
              "40    23243.399398\n",
              "41    23570.783699\n",
              "42    23887.726430\n",
              "43    24168.379751\n",
              "44    24476.408775\n",
              "Name: oil_total, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wY2KPcx9d44u",
        "outputId": "3a957043-2c94-4cc3-a32f-8458f98e81da"
      },
      "source": [
        "french_niz(*popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58816.89903729069"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qzgui7wdeCMW",
        "outputId": "35ca0d03-f677-489b-873d-a2e68a63f324"
      },
      "source": [
        "popt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-5.93831446e-01,  4.21595088e-04])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "vflaqIhfZdwn",
        "outputId": "3f09f325-242e-470a-9e8c-b610a882d9cf"
      },
      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['oil_total'], y=df_graph['water_to_oil_total'] - 1,\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_to_oil_total'] - 1,\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_to_oil_total'] - 1,\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['oil_total'], y=df_graph[-25:]['water_to_oil_total'] - 1,\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-20:]['oil_total'], y= (french_func(xdata, *popt) / df_graph[-20:]['oil_total']) - 1,\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Камбарова)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "\n",
        "fig['layout']['xaxis1']['title']='1 / Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "fig['layout']['yaxis1']['title']='Соотношение вода/нефти, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='1 / Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленной добычи нефти от объема жидкости',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQ2kfn_AZdzH"
      },
      "source": [
        "data = []\n",
        "\n",
        "for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "  for frame in range(frame_range[0], frame_range[1] + 1):\n",
        "    if shift == 0:\n",
        "      xdata = df_graph[-frame:]['oil_total']\n",
        "      ydata = df_graph[-frame:]['water_total']\n",
        "    else:\n",
        "      xdata = df_graph[-(frame + shift):-shift]['oil_total']\n",
        "      ydata = df_graph[-(frame + shift):-shift]['water_total']      \n",
        "\n",
        "    popt, pcov = curve_fit(french_func, xdata, ydata)\n",
        "    popt\n",
        "\n",
        "    data.append([shift, frame, french_niz(*popt)])\n",
        "\n",
        "    #print(popt[0], popt[1], np.abs(kambarov_niz(popt[0], popt[1]) - niz_total))\n",
        "\n",
        "df_approx = pd.DataFrame(data, columns=['shift', 'frame', 'niz'])\n",
        "df_approx['err'] = np.abs(df_approx['niz'] - niz_total)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "KtrAg4d6and8",
        "outputId": "81e3a234-2b47-4605-ceed-5555128e7380"
      },
      "source": [
        "df_approx.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>shift</th>\n",
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            "text/plain": [
              "   shift  frame            niz            err\n",
              "0      0     10  286341.797723  277346.797723\n",
              "1      0     11  197690.696842  188695.696842\n",
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    {
      "cell_type": "code",
      "metadata": {
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          "height": 717
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        "id": "eqcSeIIYZd1z",
        "outputId": "aeafc571-c1ad-41c6-b0da-aa40f53be7a7"
      },
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "  df_shift_data = df_approx.query('shift == @shift')\n",
        "\n",
        "  fig.add_trace(go.Scatter(x=df_shift_data['frame'], y=df_shift_data['err'],\n",
        "                      mode='lines',\n",
        "                      name='График при сдвиге: {0}'.format(shift)))\n",
        "\n",
        "fig.update_layout( \n",
        "    xaxis=dict(\n",
        "        domain=[0.2, 0.9],\n",
        "        title='Размер окна',\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Ошибка предсказания',\n",
        "    ),\n",
        "    title={\n",
        "        'text': \"Зависимость ошибки предсказания НИЗ от параметров аппроксимации\",\n",
        "        'y':0.95,\n",
        "        'x':0.50,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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              "</body>\n",
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          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNLVBEm8Zd4p"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgdX0OqBZd9h",
        "outputId": "e52435ac-d491-4d58-be8f-6db79838592f"
      },
      "source": [
        "xdata = df_graph[-25:]['oil_total']\n",
        "ydata = df_graph[-25:]['water_total']\n",
        "\n",
        "popt, pcov = curve_fit(naz_sip_func, xdata, ydata, p0 = [2, 0])\n",
        "popt\n",
        "\n",
        "print(popt)\n",
        "print(xdata[-1:])\n",
        "print(naz_sip_niz(np.float(ydata[-1:] - xdata[-1:]), *popt))\n",
        "\n",
        "ydata_fit = naz_sip_func(xdata, *popt)\n",
        "ydata_fit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.82438511e+00 9.32447872e-05]\n",
            "44    7153.0\n",
            "Name: oil_total, dtype: float64\n",
            "7032.4095138507655\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20    16027.540090\n",
              "21    16500.453903\n",
              "22    16854.798547\n",
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              "24    17556.931328\n",
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              "30    19345.399759\n",
              "31    19685.995474\n",
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              "42    24101.421102\n",
              "43    24460.049872\n",
              "44    24860.122819\n",
              "Name: oil_total, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "9qY2tC5sFBv7",
        "outputId": "18ec04ba-1e1b-44fe-c77e-3a37320a59dc"
      },
      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['oil_total'], y=df_graph['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['oil_total'], y=df_graph[-25:]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['oil_total'], y=naz_sip_func(xdata, *popt),\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Назаров-Сипачев)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "\n",
        "fig['layout']['xaxis1']['title']='Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "fig['layout']['yaxis1']['title']='Накопленный объем жидкости, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленного объема жидкости от добычи нефти',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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      "metadata": {
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      "source": [
        "ydata_fit[-1:]"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44    24860.122819\n",
              "Name: oil_total, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
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      "metadata": {
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        "id": "dw612DS1FB1H",
        "outputId": "d2cfff94-c0fc-4164-9b27-ad9a9961f058"
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        "xdata[-1:]"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44    7153.0\n",
              "Name: oil_total, dtype: float64"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 29
        }
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    },
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMspGS6JFB3t",
        "outputId": "6ac8b633-0eb6-4a85-97bf-42906f065649"
      },
      "source": [
        "naz_sip_niz(np.float(ydata[-1:] - xdata[-1:]), *popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {
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        "outputId": "aa939354-1433-43fa-c2be-77c673c7953c"
      },
      "source": [
        "vvk = np.float(ydata[-1:] - xdata[-1:])\n",
        "vvk = 23993\n",
        "vvk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23993"
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          "metadata": {
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        "outputId": "1a937fee-b279-4a83-ecbf-ac4cd8aacb24"
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        "popt[1]"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.324478716534906e-05"
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          "metadata": {
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      "metadata": {
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        "outputId": "a9b3ca17-2c47-453a-ab49-f8ad37a222a7"
      },
      "source": [
        "vvk / (popt[0] - 1 + popt[1] * vvk)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "7836.733364750956"
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          "metadata": {
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    },
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      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "frame_range = [3, 20]\n",
        "shift_range = [0, 12]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "data = []\n",
        "\n",
        "for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "  for frame in range(frame_range[0], frame_range[1] + 1):\n",
        "    if shift == 0:\n",
        "      xdata = df_graph[-frame:]['oil_total']\n",
        "      ydata = df_graph[-frame:]['water_total']\n",
        "    else:\n",
        "      xdata = df_graph[-(frame + shift):-shift]['oil_total']\n",
        "      ydata = df_graph[-(frame + shift):-shift]['water_total']      \n",
        "\n",
        "    popt, pcov = curve_fit(naz_sip_func, xdata, ydata, p0 = [2, 0])\n",
        "    popt\n",
        "\n",
        "    #print(naz_sip_niz(np.float(ydata[-1:] - xdata[-1:]), *popt))\n",
        "    \n",
        "    #data.append([shift, frame, naz_sip_niz(np.float(ydata[-1:] - xdata[-1:]), *popt)])\n",
        "    data.append([shift, frame, naz_sip_niz(vvk, *popt)])\n",
        "\n",
        "    \n",
        "    #if (shift == 1 and frame == 6):\n",
        "      #print(naz_sip_niz(np.float(ydata[-1:] - xdata[-1:]), *popt))\n",
        "\n",
        "    #print(popt[0], popt[1], np.abs(kambarov_niz(popt[0], popt[1]) - niz_total))\n",
        "\n",
        "df_approx = pd.DataFrame(data, columns=['shift', 'frame', 'niz'])\n",
        "df_approx['err'] = np.abs(df_approx['niz'] - niz_total)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "outputId": "fe631114-754a-438a-97db-4a555e9d806a"
      },
      "source": [
        "df_approx.head(5)"
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      "outputs": [
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              "      <th></th>\n",
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              "   shift  frame          niz         err\n",
              "0      0      3  8738.655338  256.344662\n",
              "1      0      4  8877.380673  117.619327\n",
              "2      0      5  9032.180154   37.180154\n",
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      "source": [
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        "plot_approx_heatmap(df_approx.pivot(index='shift', columns='frame', values='err'))"
      ],
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "xZM6KKcgM-4H",
        "outputId": "11d72b50-1db5-498a-a808-5a170e7694e9"
      },
      "source": [
        "xdata = df_graph[-11:-3]['oil_total']\n",
        "ydata = df_graph[-11:-3]['water_total']\n",
        "\n",
        "popt, pcov = curve_fit(naz_sip_func, xdata, ydata, p0 = [2, 0])\n",
        "popt\n",
        "naz_sip_niz(vvk, *popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9009.48401815127"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "OOo-FnXvFCI5",
        "outputId": "6eb70816-3d0b-4699-b55b-f0d8a054e5c5"
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      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['oil_total'], y=df_graph['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['oil_total'], y=df_graph[-35:]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-11:-3]['oil_total'], y=df_graph[-11:-3]['water_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-11:-3]['oil_total'], y=naz_sip_func(xdata, *popt),\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Камбарова)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_annotation(x=5500, y=23000, text=\"НИЗ={0}\".format(int(naz_sip_niz(vvk, *popt))), showarrow=False, yshift=10, font=anno_font,\n",
        "    row=1, col=2)\n",
        "\n",
        "fig['layout']['xaxis1']['title']='Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "fig['layout']['yaxis1']['title']='Накопленный объем жидкости, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='Накопленный объем нефти, 10^-6'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленного объема жидкости от добычи нефти',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "bUaUn_W_Ao40",
        "outputId": "90c41fa8-3d31-4e4f-fab9-75a5ef857fb4"
      },
      "source": [
        "xdata = df_graph[-25:-5]['water_total']\n",
        "ydata = df_graph[-25:-5]['oil_total']\n",
        "\n",
        "popt, pcov = curve_fit(sazonov_func, xdata, ydata)\n",
        "popt\n",
        "\n",
        "ydata_fit = func_sazonov(xdata, *popt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-b1877a5b21e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpopt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mydata_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_sazonov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpopt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'func_sazonov' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYIbuzPxZGM0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4kz9zZjAo78"
      },
      "source": [
        "fig = make_subplots(\n",
        "    subplot_titles=[\n",
        "                    'Весь период',\n",
        "                    'Период аппроксимации'],\n",
        "    rows=1, \n",
        "    cols=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['water_total_ln'], y=df_graph['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'),\n",
        "    row=1, col=1)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_ln'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Период аппроксимации'),\n",
        "    row=1, col=1)\n",
        "\n",
        "#####\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_ln'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:-5]['water_total_ln'], y=df_graph[-25:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Участок аппроксимации'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:-5]['water_total_ln'], y=ydata_fit,\n",
        "                    mode='lines',\n",
        "                    name='Функция аппроксимации (Сазонова)'),\n",
        "    row=1, col=2)\n",
        "\n",
        "\n",
        "fig['layout']['xaxis1']['title']='ln(накопленный объем жидкости)'\n",
        "fig['layout']['xaxis1']['tickangle']=-45\n",
        "fig['layout']['xaxis1']['tickmode']='array'\n",
        "#fig['layout']['xaxis1']['tickvals']=df_data['year']\n",
        "fig['layout']['yaxis1']['title']='Накопленный объем нефти, 10^6'\n",
        "\n",
        "fig['layout']['xaxis2']['title']='ln(накопленный объем жидкости)'\n",
        "fig['layout']['xaxis2']['tickangle']=-45\n",
        "#fig['layout']['yaxis2']['title']='Обводненность, д.ед.'\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout( \n",
        "    title={\n",
        "        'text': 'Зависимость накопленной добычи нефти от объема жидкости',\n",
        "        'y':0.95,\n",
        "        'x':0.45,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=700)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOHNp5-OAo_B"
      },
      "source": [
        "frame_range = [10, 25]\n",
        "shift_range = [0, 15]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MlZGzqpApGl"
      },
      "source": [
        "for shift in range(shift_range[0], shift_range[1] + 1):\n",
        "  for frame in range(frame_range[0], frame_range[1] + 1):\n",
        "    if shift == 0:\n",
        "      xdata = df_graph[-frame:]['water_total']\n",
        "      ydata = df_graph[-frame:]['oil_total']\n",
        "    else:\n",
        "      xdata = df_graph[-(frame + shift):-shift]['water_total']\n",
        "      ydata = df_graph[-(frame + shift):-shift]['oil_total']      \n",
        "\n",
        "    #print(shift, frame)\n",
        "    #popt, pcov = curve_fit(func_kambarov, xdata, ydata)\n",
        "    popt, pcov = curve_fit(func_sazonov, xdata, ydata)\n",
        "    popt\n",
        "\n",
        "    #print(popt[0], popt[1], kambarov_niz(popt[0], np.abs(popt[1])))\n",
        "    print(popt[0], popt[1], sazonov_niz(popt[0], np.abs(popt[1])))\n",
        "\n",
        "\n",
        "    #ydata_fit = func_kambarov(xdata, *popt)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GG_aLm1yZmi"
      },
      "source": [
        "df_graph[-27:-2]['oil_total']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6i-Mcdgln0vB"
      },
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph['water_total_deg_1'], y=df_graph['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Накопленный объем нефти, 10^6'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines',\n",
        "                    name='Накопленный объем нефти <br>(предполагаемый период масштабирования)'))\n",
        "\n",
        "fig.update_layout( \n",
        "    xaxis=dict(\n",
        "        domain=[0.2, 0.9],\n",
        "        title='1 / Накопленный объем жидкости',\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Накопленный объем нефти',\n",
        "    ),\n",
        "    title={\n",
        "        'text': \"Зависимость накопленной добычи нефти от объема жидкости\",\n",
        "        'y':0.95,\n",
        "        'x':0.50,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=900\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj1FMXaM6xHR"
      },
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-35:]['water_total_deg_1'], y=df_graph[-35:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=df_graph[-25:]['water_total_deg_1'], y=df_graph[-25:]['oil_total'],\n",
        "                    mode='lines+markers',\n",
        "                    name='Годовой отбор жидкости, 10^6'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=xdata, y=ydata_fit,\n",
        "                    mode='lines',\n",
        "                    name='Годовой отбор жидкости, 10^6'))\n",
        "\n",
        "fig.update_layout( \n",
        "    xaxis=dict(\n",
        "        domain=[0.2, 0.9],\n",
        "        title='Накопленный объем жидкости в степени -1',\n",
        "    ),\n",
        "    title={\n",
        "        'text': \"Зависимость накопленной добычи нефти от объема жидкости\",\n",
        "        'y':0.95,\n",
        "        'x':0.55,\n",
        "        'xanchor': 'center',\n",
        "        'yanchor': 'top'},\n",
        "    autosize=False,\n",
        "    width=1400,\n",
        "    height=900\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hgl0tl8vw6Hf"
      },
      "source": [
        "popt, pcov = curve_fit(func_kambarov, xdata, ydata)\n",
        "popt\n",
        "\n",
        "plt.plot(xdata, func_kambarov(xdata, *popt), 'r-',\n",
        "         label='fit: a=%5.3f, b=%5.3f' % tuple(popt))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zm8ZEVtVw6KO"
      },
      "source": [
        "popt\n",
        "b = popt[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kit5FeTbw6Ps"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtn5yNCftEZU"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "def func(x, a, b, c):\n",
        "    return a * np.exp(-b * x) + c\n",
        "\n",
        "xdata = np.linspace(0, 4, 50)\n",
        "y = func(xdata, 2.5, 1.3, 0.5)\n",
        "np.random.seed(1729)\n",
        "y_noise = 0.2 * np.random.normal(size=xdata.size)\n",
        "ydata = y + y_noise\n",
        "plt.plot(xdata, ydata, 'b-', label='data')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KS34jtDGwQB6"
      },
      "source": [
        "def func2(x, a, b):\n",
        "    return a - b * x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0za17ktptEi-"
      },
      "source": [
        "popt, pcov = curve_fit(func2, xdata, ydata)\n",
        "popt\n",
        "\n",
        "plt.plot(xdata, func2(xdata, *popt), 'r-',\n",
        "         label='fit: a=%5.3f, b=%5.3f' % tuple(popt))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HN2MKMOZoSQW"
      },
      "source": [
        "popt, pcov = curve_fit(func, xdata, ydata, bounds=(0, [3., 1., 0.5]))\n",
        "popt\n",
        "\n",
        "plt.plot(xdata, func(xdata, *popt), 'g--',\n",
        "         label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))\n",
        "\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UU8iOXrrtkRf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}