deploy: 61708c6

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 87c4fbe0b5f1e22af8a4360697a7e593
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/1ce45cd6327683a313eacb9cc8351140/2_postprocessing.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plot profiles\n\nThe objective of this example is to import the output results and plot the profiles of temperature, pressure, liquid saturation and air mass fraction.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here, we assume that the simulation's output have been written in the file \"OUTPUT\". To import the results, we use the function :func:`toughio.read_output`. The variable `outputs` is a list with three :class:`toughio.Output` corresponding to the three time steps requested in the preprocessing example. In this example, we want to look at the last time step (index -1).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport toughio\n\noutputs = toughio.read_output(\"OUTPUT\")\noutput = outputs[-1]\n\nt = output.data[\"T\"]\np = output.data[\"P\"]\nsl = output.data[\"SL\"]\nxm = output.data[\"XAIRG\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "It is well known that for the stated conditions (1-D radial geometry, homogeneous medium, uniform initial conditions, and a constant-rate line source) the problem has a similarity solution: The partial differential equations for this complex two-phase flow problem can be rigorously transformed into a set of ordinary differential equations in the variable $Z = R/\\sqrt{t}$, which can be easily solved to any degree of accuracy desired by means of one-dimensional numerical integration (O'Sullivan, 1981). Comparison of TOUGH2 simulations with the semi-analytical similarity solution has shown excellent agreement (Doughty and Pruess, 1992). To define such variable, we first need to import the mesh \n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mesh = toughio.read_mesh(\"mesh.pickle\")\nR = np.log(mesh.centers[:, 0] / (output.time) ** 0.5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that the required data have been imported, we can plot the results using :mod:`matplotlib`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nplt.rc(\"font\", size=12)\n\nfig, ax1 = plt.subplots(figsize=(8, 5))\nax2 = ax1.twinx()\n\nax1.plot(R, t, color=\"black\", linestyle=\"--\", label=\"Temperature\")\nax1.set_ylim(0.0, 260.0)\nax1.set_ylabel(\"Temperature ($\\degree$C)\")\n\nax2.plot(R, sl, label=\"Liquid saturation\")\nax2.plot(R, xm, label=\"Air mass fraction\")\nax2.plot(R, p * 1.0e-5, label=\"Pressure (bar)\")\nax2.set_ylim(0.0, 1.3)\nax2.set_ylabel(\"Liquid saturation, air mass fraction, pressure\")\n\nax1.set_xlim(R.min(), -4.0)\nax1.set_xlabel(\"$ln(R/\\sqrt{t})$\")\n\nfig.legend(\n    loc=\"lower left\",\n    bbox_to_anchor=(0.0, 0.0),\n    bbox_transform=ax1.transAxes,\n    frameon=False,\n)"
+      ]
+    }
+  ],
+  "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": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/4790b1dc53fd45ecd00f717e3df347da/3_generate_model_parameters_input_file.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Generate model parameters input file\n\nNow that the mesh has been pre-processed, we can define the TOUGH simulation parameters.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As always, we first import the required modules for this example.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport toughio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A :mod:`toughio` input file is defined as a nested dictionary with meaningful keywords.\nLet's initialize our parameter dictionary by giving the simulation a title and defining the equation-of-state. This example will also be run isothermally.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters = {\n    \"title\": \"Simulation of CO2 leak along a fault\",\n    \"eos\": \"eco2n\",\n    \"isothermal\": True,\n    \"start\": True,\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also define some default values that are shared by the different materials.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters[\"default\"] = {\n    \"density\": 2260.0,\n    \"conductivity\": 1.8,\n    \"specific_heat\": 1500.0,\n    \"compressibility\": 8.33e-10,\n    \"conductivity_dry\": 1.8,\n    \"tortuosity\": 0.7,\n    \"relative_permeability\": {\n        \"id\": 3,\n        \"parameters\": [0.3, 0.05],\n    },\n    \"capillarity\": {\n        \"id\": 7,\n        \"parameters\": [0.457, 0.0, 5.03e-5, 5.0e7, 0.99],\n    },\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we define specific material properties (different than the default ones previously defined) for each material in the mesh (i.e., we write the block `ROCKS`).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters[\"rocks\"] = {\n    \"UPPAQ\": {\n        \"porosity\": 0.10,\n        \"permeability\": 1.0e-14,\n    },\n    \"CENAQ\": {\n        \"porosity\": 0.10,\n        \"permeability\": 1.0e-13,\n    },\n    \"BASAQ\": {\n        \"porosity\": 0.01,\n        \"permeability\": 1.0e-16,\n        \"relative_permeability\": {\n            \"id\": 3,\n            \"parameters\": [0.3, 0.05],\n        },\n        \"capillarity\": {\n            \"id\": 7,\n            \"parameters\": [0.457, 0.0, 1.61e-6, 5.0e7, 0.99],\n        },\n    },\n    \"CAPRO\": {\n        \"porosity\": 0.01,\n        \"permeability\": 1.0e-19,\n        \"relative_permeability\": {\n            \"id\": 3,\n            \"parameters\": [0.3, 0.05],\n        },\n        \"capillarity\": {\n            \"id\": 7,\n            \"parameters\": [0.457, 0.0, 1.61e-6, 5.0e7, 0.99],\n        },\n    },\n    \"FAULT\": {\n        \"porosity\": 0.10,\n        \"permeability\": 1.0e-13,\n    },\n    \"BOUND\": {\n        \"specific_heat\": 1.0e55,\n        \"porosity\": 0.10,\n        \"permeability\": 1.0e-13,\n    },\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can specify some simulation parameters (block `PARAM`), options (`MOP`) and selections (block `SELEC`).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters[\"options\"] = {\n    \"n_cycle\": 9999,\n    \"n_cycle_print\": 9999,\n    \"t_ini\": 0.0,\n    \"t_max\": 3.0 * 365.25 * 24.0 * 3600.0,\n    \"t_steps\": 24.0 * 3600.0,\n    \"t_step_max\": 1.0e8,\n    \"t_reduce_factor\": 4,\n    \"eps1\": 1.0e-4,\n    \"eps2\": 1.0,\n    \"gravity\": 9.81,\n}\nparameters[\"extra_options\"] = {\n    16: 4,\n    21: 8,\n}\nparameters[\"selections\"] = {\n    \"integers\": {\n        1: 1,\n        13: 2,\n        16: 2,\n    },\n    \"floats\": [0.8, 0.8],\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also have to define the generator (i.e., the source).\nHowever, we first need to know the name of the cell in which the CO2 is injected. Let's unpickle the mesh back and use the method :meth:`toughio.Mesh.near` to get the name of the injection element.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mesh = toughio.read_mesh(\"mesh.pickle\")\nlabel = mesh.labels[mesh.near((0.0, 0.0, -1500.0))]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can add the generator to the parameters by specifying the type and injection rate (block `GENER`).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters[\"generators\"] = [\n    {\n        \"label\": label,\n        \"type\": \"COM3\",\n        \"rates\": 0.02,\n    },\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's customize the outputs.\nFor this example, we want TOUGH to save the output every three months (i.e., 4 outputs per year). To reduce the size of the output file, we also want TOUGH to only save the saturation of phase 1 (gas) in addition to the cell coordinates. Note that this option is only available in TOUGH3.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "parameters[\"times\"] = np.arange(1, 13) * 90.0 * 24.0 * 3600.0\nparameters[\"output\"] = {\n    \"variables\": [\n        {\"name\": \"saturation\", \"options\": 1},\n        {\"name\": \"coordinate\"},\n    ],\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can export the model parameters input file by using the function :func:`toughio.write_input`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "toughio.write_input(\"INFILE\", parameters)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "At this stage, all the input files required to run the simulation have been generated. We can now simply call TOUGH using EOS ECO2n (e.g., :code:`tough3-eco2n` for TOUGH3).\n\n"
+      ]
+    }
+  ],
+  "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": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}