easy_kraken.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from PyQt5.QtWidgets import (\n",
    "    QFileDialog,\n",
    "    QMessageBox,\n",
    ")\n",
    "\n",
    "plt.style.use([\"bmh\"])\n",
    "\n",
    "SELECTED_FILE = \"data/Grad_1-BRSA_.xlsx\"\n",
    "PAGINA = \"1-BRSA-1221-MA\" \n",
    "UNIDADE_PRESSAO = \"kgf/cm2/m\" # \"psi/ft\" ou \"psi/m\" ou \"kgf/cm2/m\" ou \"bar/m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_file():\n",
    "    selected_file = SELECTED_FILE # Insira aqui o nome do arquivo com a extensão\n",
    "    sheetTab = PAGINA # Insira aqui o nome da página na qual quer calcular\n",
    "    dataframe = pd.read_excel(\n",
    "        selected_file,\n",
    "        skiprows=1,\n",
    "        decimal=\",\",\n",
    "        sheet_name=sheetTab,\n",
    "    )\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fluid_color(fluid_name):\n",
    "    \"\"\"\n",
    "    Returns a color based on the type of fluid.\n",
    "    \"\"\"\n",
    "    if \"gas\" in fluid_name.lower():\n",
    "        return \"#A60628\"  # Gas - Red\n",
    "    elif \"oil\" in fluid_name.lower():\n",
    "        return \"#467821\"  # Oil - Green\n",
    "    elif \"water\" in fluid_name.lower():\n",
    "        return \"#0072B2\"  # Water - Blue\n",
    "    else:\n",
    "        return \"gray\"  # Default color\n",
    "\n",
    "\n",
    "def pressure_gradient_classification(\n",
    "    data, kmeans_number, pressure_unit\n",
    "):\n",
    "    # GAS - RED\n",
    "    # OIL - GREEN\n",
    "    # WATER - BLUE\n",
    "    pressao = data.iloc[:, 0]\n",
    "    prof = data.iloc[:, 1]\n",
    "\n",
    "    def calculate_slope(ps_a, ps_b):\n",
    "        if len(ps_a) != len(ps_b):\n",
    "            raise ValueError(\"ps_a and ps_b must have the same length\")\n",
    "        coefficients = np.polyfit(ps_a, ps_b, 1)\n",
    "        return coefficients[0]\n",
    "\n",
    "    slopes = []\n",
    "    slope_indices = {}\n",
    "    for i in range(len(prof) - 1):\n",
    "        x_values = np.array([prof[i], prof[i + 1]])\n",
    "        y_values = np.array([pressao[i], pressao[i + 1]])\n",
    "        slope = calculate_slope(x_values, y_values) * -1\n",
    "        slopes.append(slope)\n",
    "        slope_indices[slope] = [i, i + 1]\n",
    "\n",
    "    # Convert the list of slopes to a numpy array\n",
    "    slopes_array = np.array([slopes, slopes])\n",
    "\n",
    "    # Perform KMeans clustering on the slopes\n",
    "    kmeans = KMeans(n_clusters=kmeans_number, random_state=0).fit(slopes_array.T)\n",
    "\n",
    "    # Classify the data based on the clusters\n",
    "    classified_data = [list(kmeans.labels_)[0]] + list(kmeans.labels_)\n",
    "\n",
    "    def convert_classification(class_data):\n",
    "        if class_data[0] == 1:\n",
    "            return [1 - label for label in class_data]\n",
    "        return class_data\n",
    "\n",
    "    # Convert the classification labels if necessary\n",
    "    converted_data = convert_classification(classified_data)\n",
    "\n",
    "    # Separate the data into two groups based on the classification\n",
    "    top_values = [\n",
    "        (prof[i], pressao[i]) for i, label in enumerate(converted_data) if label == 0\n",
    "    ]\n",
    "    bottom_values = [\n",
    "        (prof[i], pressao[i]) for i, label in enumerate(converted_data) if label == 1\n",
    "    ]\n",
    "\n",
    "    # Calculate the line of best fit for each group\n",
    "    top_prof, top_pressao = zip(*top_values)\n",
    "    bottom_prof, bottom_pressao = zip(*bottom_values)\n",
    "\n",
    "    slope_top, intercept_top = np.polyfit(top_prof, top_pressao, 1)\n",
    "    slope_bottom, intercept_bottom = np.polyfit(bottom_prof, bottom_pressao, 1)\n",
    "\n",
    "    pressure_unit = pressure_unit\n",
    "    # four pressure units to choose from (psi/ft, psi/m, Kgf/cm2/m, bar/m)\n",
    "\n",
    "    fluid_pressure = {\n",
    "        \"dry_gas_zero\": {\n",
    "            \"name\": \"Dry gas zero\",\n",
    "            \"gradient\": {\"psi/ft\": 0.0, \"psi/m\": 0.0, \"kgf/cm2/m\": 0.0, \"bar/m\": 0.0},\n",
    "        },\n",
    "        \"dry_gas\": {\n",
    "            \"name\": \"Dry gas\",\n",
    "            \"gradient\": {\"psi/ft\": 0.0, \"psi/m\": 0.0, \"kgf/cm2/m\": 0.0, \"bar/m\": 0.0},\n",
    "        },\n",
    "        \"wet_gas\": {\n",
    "            \"name\": \"Wet gas\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.140,\n",
    "                \"psi/m\": 0.459,\n",
    "                \"kgf/cm2/m\": 0.030,\n",
    "                \"bar/m\": 0.032,\n",
    "            },\n",
    "        },\n",
    "        \"oil_limit\": {\n",
    "            \"name\": \"Oil limit\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.300,\n",
    "                \"psi/m\": 0.984,\n",
    "                \"kgf/cm2/m\": 0.069,\n",
    "                \"bar/m\": 0.069,\n",
    "            },\n",
    "        },\n",
    "        \"oil_60\": {\n",
    "            \"name\": \"Oil 60°\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.387,\n",
    "                \"psi/m\": 1.270,\n",
    "                \"kgf/cm2/m\": 0.089,\n",
    "                \"bar/m\": 0.087,\n",
    "            },\n",
    "        },\n",
    "        \"oil_20\": {\n",
    "            \"name\": \"Oil 20° (heavy)\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.404,\n",
    "                \"psi/m\": 1.325,\n",
    "                \"kgf/cm2/m\": 0.093,\n",
    "                \"bar/m\": 0.091,\n",
    "            },\n",
    "        },\n",
    "        \"fresh_water\": {\n",
    "            \"name\": \"Fresh water\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.433,\n",
    "                \"psi/m\": 1.421,\n",
    "                \"kgf/cm2/m\": 0.100,\n",
    "                \"bar/m\": 0.098,\n",
    "            },\n",
    "        },\n",
    "        \"sea_water\": {\n",
    "            \"name\": \"Sea Water\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.444,\n",
    "                \"psi/m\": 1.457,\n",
    "                \"kgf/cm2/m\": 0.102,\n",
    "                \"bar/m\": 0.101,\n",
    "            },\n",
    "        },\n",
    "        \"salt_sat_water\": {\n",
    "            \"name\": \"Salt sat. Water\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 0.520,\n",
    "                \"psi/m\": 1.706,\n",
    "                \"kgf/cm2/m\": 0.120,\n",
    "                \"bar/m\": 0.118,\n",
    "            },\n",
    "        },\n",
    "        \"salt_max\": {\n",
    "            \"name\": \"Salt sat. Water Max\",\n",
    "            \"gradient\": {\n",
    "                \"psi/ft\": 100.000,\n",
    "                \"psi/m\": 100.000,\n",
    "                \"kgf/cm2/m\": 100.000,\n",
    "                \"bar/m\": 100.000,\n",
    "            },\n",
    "        },\n",
    "    }\n",
    "\n",
    "    # Get a list of all fluid keys\n",
    "    fluid_keys = list(fluid_pressure.keys())\n",
    "\n",
    "    # Initialize an empty dictionary for pressure values\n",
    "    pressure_values = {}\n",
    "\n",
    "    # Iterate over each pair of consecutive fluids\n",
    "    for i in range(len(fluid_keys) - 1):\n",
    "        # Get the top and bottom fluids\n",
    "        top_fluid = fluid_keys[i]\n",
    "        bottom_fluid = fluid_keys[i + 1]\n",
    "\n",
    "        # Get the pressure gradient values for the top and bottom fluids\n",
    "        top_pressure = fluid_pressure[top_fluid][\"gradient\"][pressure_unit]\n",
    "        bottom_pressure = fluid_pressure[bottom_fluid][\"gradient\"][pressure_unit]\n",
    "\n",
    "        # Store the pressure values in the dictionary\n",
    "        pressure_values[top_fluid] = [top_pressure, bottom_pressure]\n",
    "\n",
    "    # Remove the first key-value pair from the dictionary\n",
    "    first_key = next(iter(pressure_values))\n",
    "    pressure_values.pop(first_key)\n",
    "\n",
    "    # Initialize the top fluid\n",
    "    top_fluid = 0\n",
    "\n",
    "    # Find the fluid that matches the top slope\n",
    "    for fluid, pressures in pressure_values.items():\n",
    "        if (\n",
    "            -1.0 * round(slope_top, 4) >= pressures[0]\n",
    "            and -1.0 * round(slope_top, 4) < pressures[1]\n",
    "        ):\n",
    "            top_fluid = fluid\n",
    "\n",
    "    # Find the fluid that matches the bottom slope\n",
    "    for fluid, pressures in pressure_values.items():\n",
    "        if (\n",
    "            -1.0 * round(slope_bottom, 4) >= pressures[0]\n",
    "            and -1.0 * round(slope_bottom, 4) < pressures[1]\n",
    "        ):\n",
    "            bottom_fluid = fluid\n",
    "\n",
    "    # Get the names of the top and bottom fluids\n",
    "    top_fluid_name = fluid_pressure[top_fluid][\"name\"]\n",
    "    bottom_fluid_name = fluid_pressure[bottom_fluid][\"name\"]\n",
    "\n",
    "    # Print the names of the top and bottom fluids\n",
    "    # print(top_fluid_name, \"|\", bottom_fluid_name)\n",
    "\n",
    "    x_intercept = (intercept_bottom - intercept_top) / (slope_top - slope_bottom)\n",
    "    y_intercept = slope_top * x_intercept + intercept_top\n",
    "    # print(\"O ponto de interseção das retas é\", x_intercept, y_intercept)\n",
    "\n",
    "    diff = np.diff(top_prof)\n",
    "    # print(diff)\n",
    "\n",
    "    # Extended top curve\n",
    "    mean_cota_top = np.mean(np.diff(top_prof))\n",
    "    extended_cota_top = list(top_prof) + [(np.min(top_prof) - np.abs(mean_cota_top))]\n",
    "    extended_pressure_top = np.array(extended_cota_top) * slope_top + intercept_top\n",
    "\n",
    "    # Extended bot curve\n",
    "    mean_cota_bot = np.mean(np.diff(bottom_prof))\n",
    "    extended_cota_bot = [(np.max(bottom_prof) - mean_cota_bot)] + list(bottom_prof)\n",
    "    extended_pressure_bot = (\n",
    "        np.array(extended_cota_bot) * slope_bottom + intercept_bottom\n",
    "    )\n",
    "\n",
    "    # Calculate the line of best fit for the top and bottom fluids\n",
    "    line_top = slope_top * np.array(top_prof) + intercept_top\n",
    "    line_bottom = slope_bottom * np.array(bottom_prof) + intercept_bottom\n",
    "\n",
    "    messages = []\n",
    "    messages.append(\n",
    "        f\"Fluido do topo: {top_fluid_name} | Fluido da base: {bottom_fluid_name}\"\n",
    "    )\n",
    "    messages.append(\n",
    "        f\"O ponto de interseção das retas é {x_intercept:.2f} {y_intercept:.2f}\"\n",
    "    )\n",
    "\n",
    "    top_fluid_color = get_fluid_color(top_fluid_name)\n",
    "    bottom_fluid_color = get_fluid_color(bottom_fluid_name)\n",
    "\n",
    "    return (\n",
    "        top_pressao,\n",
    "        top_prof,\n",
    "        bottom_pressao,\n",
    "        bottom_prof,\n",
    "        extended_pressure_top,\n",
    "        extended_cota_top,\n",
    "        extended_pressure_bot,\n",
    "        extended_cota_bot,\n",
    "        y_intercept,\n",
    "        x_intercept,\n",
    "        top_fluid_color,\n",
    "        top_fluid_name,\n",
    "        bottom_fluid_name,\n",
    "        bottom_fluid_color,\n",
    "        slope_top,\n",
    "        slope_bottom,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_data_for_trends():\n",
    "        dataframe = read_file()\n",
    "        kmeans_clusters = 2\n",
    "\n",
    "        pressure_unit = UNIDADE_PRESSAO # Insira aqui a unidade de pressão\n",
    "        # \"psi/ft\" ou \"psi/m\" ou \"kgf/cm2/m\" ou \"bar/m\"\n",
    "\n",
    "        return pressure_gradient_classification(\n",
    "            dataframe,\n",
    "            kmeans_clusters,\n",
    "            pressure_unit,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_trends():\n",
    "        (\n",
    "            top_pressao,\n",
    "            top_prof,\n",
    "            bottom_pressao,\n",
    "            bottom_prof,\n",
    "            extended_pressure_top,\n",
    "            extended_cota_top,\n",
    "            extended_pressure_bot,\n",
    "            extended_cota_bot,\n",
    "            y_intercept,\n",
    "            x_intercept,\n",
    "            top_fluid_color,\n",
    "            top_fluid_name,\n",
    "            bottom_fluid_name,\n",
    "            bottom_fluid_color,\n",
    "            slope_top,\n",
    "            slope_bottom,\n",
    "        ) = process_data_for_trends()\n",
    "\n",
    "        plt.figure(figsize=(10, 6))\n",
    "\n",
    "        plt.plot(top_pressao, top_prof, \"o\", c=top_fluid_color, label=top_fluid_name)\n",
    "        plt.plot(\n",
    "            bottom_pressao,\n",
    "            bottom_prof,\n",
    "            \"o\",\n",
    "            c=bottom_fluid_color,\n",
    "            label=bottom_fluid_name,\n",
    "        )\n",
    "        plt.plot(\n",
    "            extended_pressure_top,\n",
    "            extended_cota_top,\n",
    "            c=top_fluid_color,\n",
    "            label=top_fluid_name + \" \" + str(round(slope_top, 4)),\n",
    "        )\n",
    "        plt.plot(\n",
    "            extended_pressure_bot,\n",
    "            extended_cota_bot,\n",
    "            \"-\",\n",
    "            c=bottom_fluid_color,\n",
    "            label=bottom_fluid_name + \" \" + str(round(slope_bottom, 4)),\n",
    "        )\n",
    "        plt.plot(\n",
    "            y_intercept,\n",
    "            x_intercept,\n",
    "            \"s\",\n",
    "            c=\"k\",\n",
    "            label=\"Intersection \" + str(round(x_intercept, 2)),\n",
    "        )\n",
    "\n",
    "        plt.title(\"Gradiente de Pressão\")\n",
    "        plt.legend(fontsize=\"small\")\n",
    "        plt.xlabel(\"Pressão\")\n",
    "        plt.ylabel(\"Cota\")\n",
    "        plt.grid(True)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "plot_trends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}