commit e814b8ff9fc2a6c9a969790712283a800eafda89 Author: GMG Contributors Date: Mon Jul 29 18:39:03 2024 +0000 initialize new template with Groningen reservoir model as example diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f83192d --- /dev/null +++ b/.gitignore @@ -0,0 +1,190 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..73968d7 --- /dev/null +++ b/environment.yml @@ -0,0 +1,438 @@ +name: flow2quake +channels: + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_kmp_llvm + - _sysroot_linux-64_curr_repodata_hack=3=h69a702a_16 + - aiohttp=3.9.5=py310h2372a71_0 + - aiosignal=1.3.1=pyhd8ed1ab_0 + - alsa-lib=1.2.12=h4ab18f5_0 + - anyio=4.4.0=pyhd8ed1ab_0 + - 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x264=1!164.3095=h166bdaf_2 + - x265=3.5=h924138e_3 + - xarray=2023.7.0=pyhd8ed1ab_0 + - xarray-einstats=0.6.0=pyhd8ed1ab_0 + - xcb-util=0.4.0=hd590300_1 + - xcb-util-image=0.4.0=h8ee46fc_1 + - xcb-util-keysyms=0.4.0=h8ee46fc_1 + - xcb-util-renderutil=0.3.9=hd590300_1 + - xcb-util-wm=0.4.1=h8ee46fc_1 + - xkeyboard-config=2.42=h4ab18f5_0 + - xorg-fixesproto=5.0=h7f98852_1002 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.1.1=hd590300_0 + - xorg-libsm=1.2.4=h7391055_0 + - xorg-libx11=1.8.9=h8ee46fc_0 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxfixes=5.0.3=h7f98852_1004 + - xorg-libxrender=0.9.11=hd590300_0 + - xorg-libxt=1.3.0=hd590300_1 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xf86vidmodeproto=2.3.1=h7f98852_1002 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.2.6=h166bdaf_0 + - yaml=0.2.5=h7f98852_2 + - yarl=1.9.4=py310h2372a71_0 + - zeromq=4.3.5=h75354e8_4 + - zipp=3.19.2=pyhd8ed1ab_0 + - zlib=1.2.13=h4ab18f5_6 + - zstandard=0.23.0=py310h64cae3c_0 + - zstd=1.5.6=ha6fb4c9_0 +prefix: /home/michael/miniconda3/envs/flow2quake diff --git a/flow2quake/__init__.py b/flow2quake/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flow2quake/cases/groningen-reservoir-model/reservoir-model-GMG.ipynb b/flow2quake/cases/groningen-reservoir-model/reservoir-model-GMG.ipynb new file mode 100644 index 0000000..2bc1826 --- /dev/null +++ b/flow2quake/cases/groningen-reservoir-model/reservoir-model-GMG.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Groningen reservoir model\n", + "This notebook allows running the reservoir model developped at GMG for gas extraction from the Groningen gas field following Meyer et al., 2022.\n", + "\n", + "The most complete description of the model can be found in:\n", + "Meyer, H., Smith, J. D., Bourne, S., & Avouac, J. P. (2023). An integrated framework for surface deformation modeling and induced seismicity forecasting due to reservoir operations. Geological Society, London, Special Publications, 528(1), SP528-2022.\n", + "\n", + "A full description of the needed libraries and tested practice on installation and running can be found in the file: Groningen_GMG_Reservoir_model_libraries.txt\n", + "\n", + "A description of the integration of this module with the rest of the modelling workflow can be found in the file:\n", + "../README_GroningenModels_GMG.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Libraries, functions, and data loading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Import librairies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Importing librairies...\n", + "Librairies imported\n" + ] + } + ], + "source": [ + "print ('Importing librairies...')\n", + "\n", + "# System operations\n", + "import sys\n", + "from time import time\n", + "# Math, Array and Dataformating\n", + "import numpy as np\n", + "import pandas as pd\n", + "import math\n", + "from scipy.interpolate import griddata\n", + "# Plotting\n", + "from shapely.geometry import Point as ShapelyPoint\n", + "from shapely.geometry.polygon import Polygon as ShapelyPolygon\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import Circle, Wedge, Polygon\n", + "# Gaussian Smoothing\n", + "from astropy.convolution import Gaussian2DKernel\n", + "from astropy.convolution import convolve\n", + "\n", + "# VFE modules\n", + "from fenics import *\n", + "import time as tm\n", + "from scipy import signal\n", + "from mshr import *\n", + "\n", + "print ('Librairies imported')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Importing auxiliary functions...\n", + "Auxiliary functions imported\n" + ] + } + ], + "source": [ + "# Import functions\n", + "print ('Importing auxiliary functions...')\n", + "from util.auxiliary import *\n", + "print ('Auxiliary functions imported')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Define the folders to be used" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_folder = '../Reservoir_data/'\n", + "results_folder = '../Simulation_results/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Load reservoir data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading reservoir data...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/michellejaramillo/Documents/GMG_DeepDive2024/cleancodes_groningen_GMG_V0.2/GroningenModels/Groningen_GMG_Reservoir_model/Functions/auxiliary_functions.py:46: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df1[col].iloc[idx0:idx1 + 1] = data[col] # Insert updated data\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reservoir data loaded\n" + ] + }, + { + "data": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print ('Loading reservoir data...')\n", + "# Reservoir data\n", + "X, Y = np.mgrid[221.250:269.750:0.500, 566.000:615.500:0.500] # in km\n", + "Thickness = np.load(data_folder + 'ReservoirThickness.npy') / 1e3 #in km\n", + "Outline = pd.DataFrame(np.load(data_folder + 'ReservoirOutline.npy')) / 1e3 #dim change\n", + "\n", + "# Gas extraction data\n", + "GasMolWeight = 18.3815e-3 # kg.mol-1\n", + "GasData = np.load(data_folder + 'WellInformation.npy', allow_pickle=True).item() # the information on the well clusters and gas extraction data\n", + "GasExtractionData = UpdateGasExtractionData(GasData_file = data_folder+'WellInformation.npy',winter = 'ColdWinter',Excel_file = data_folder+'Gaswinning-maandelijks_2010_2022.xlsx',plot_YN=True,Extrapolate_increase=False)\n", + "# Data from 1956 to 2039 the last years from 2022 onward are previsions of gas extraction. Here we use the cold winter scenario but it can be changed\n", + "\n", + "BottomHP = GasData['PressureMeasurements'] #The bottomhole pressure measurements for history matching\n", + "Well = GasData['WellLocations'] #The well cluster locations\n", + "print (\"Reservoir data loaded\")\n", + "\n", + "# the previous line is optional" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Wells data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from util.well_config import create_well_configuration\n", + "\n", + "#Chose here your well configuration (only for control process)\n", + "# 1 : one central well\n", + "# 2 : two wells (one north and one south)\n", + "# 5 : One central well surrounded by four other wells\n", + "# 29 : All real wells' positions\n", + "well_configuration = 29\n", + "\n", + "#Well and GasData are arguments so that the function knows the real wells positions for configuration 29\n", + "wells_names, wells_locations, Well, initial_extraction_data = create_well_configuration(well_configuration, Well, GasData)\n", + "\n", + "historic_extraction = np.nan_to_num(GasExtractionData.fillna(0).rolling(1).mean().to_numpy()) /(30.4375*24) # convert units to bbm/month\n", + "extraction_data = []\n", + "for ii in range(historic_extraction.shape[0]):\n", + " extraction_data.append(pd.Series(data=historic_extraction[ii,:].tolist(),index=wells_names))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Run the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Creation of the diffusion model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n" + ] + } + ], + "source": [ + "from util.diffusion_model import DiffusionModel\n", + "\n", + "#Define an iteration for the change in gas saturation.\n", + "TimeNewInit = 431 \n", + "# This is used to simulate an artificial change in gas saturation at a given iteration it can be removed\n", + "#its value is : 431 -- corresponds to the nmber of months over 1957-1992\n", + "\n", + "#Create the diffusion model class\n", + "DiffMod = DiffusionModel(X, #X and Y are the spatial meshgrid [221250:269750:500, 566000:615500:500]\n", + " Y,\n", + " Thickness, #Table loaded from ReservoirThickness.npy\n", + " 385, #Temperature in K\n", + " 34.68, #Initial pressure in MPa\n", + " GasMolWeight,\n", + " TimeNewInit,\n", + " 2629800. / 3600, #In seconds, corresponds to 1 month, used for dimension change\n", + " GasExtractionData,\n", + " Well, #Table with 1 line per well (column 0 : name, columns 1 and 2 : coordinates)\n", + " Outline)\n", + "\n", + "#Dump the unstructured mesh of the reservoir to a numpy file for later use\n", + "MESH = DiffMod.coordinates2_\n", + "with open(results_folder+'MESH.npy', 'wb') as f:\n", + " np.save(f,MESH)\n", + " \n", + " \n", + "#Parameters for the pressure model deduced from first history matching (1957-1992)\n", + "optimal_permeability = 2.5e-13 * 1e-6 #dimension change, permeability dimension : m^2\n", + "optimal_porosity = 0.16\n", + "optimal_gas_saturation = 0.31\n", + "optimal_gas_saturation2 = 0.357" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Main loop" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#Creation of empty lists which will contain the results\n", + "pressures_list = []\n", + "extractions_list = []\n", + "deformations_list = []\n", + "\n", + "#Definition on initial pressure and gas extraction\n", + "initial_pressure = np.ones(MESH.shape[0]) * 34.68 #dim change, pressure change\n", + "pressures_list.append(initial_pressure)\n", + "# extractions_list.append(initial_extraction_data)\n", + "extractions_list.append(extraction_data[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progression : 0%\n", + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n", + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n", + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n", + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n", + "Calling FFC just-in-time (JIT) compiler, this may take some time.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progression : 91%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 92%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 93%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 94%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 95%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 96%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 97%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 98%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Progression : 99%\n", + "Total time for main loop : 18.49016785621643\n", + "Mean time per iteration : 0.01849016785621643\n" + ] + } + ], + "source": [ + "### MAIN DIFFUSION SIMULATION LOOP ###\n", + "\n", + "#Always run the previous cell before this one, otherwise pressures_list, extractions_list and deformations_list won't be reset\n", + "\n", + "nb_iterations = 1000 #One iteration = one month\n", + "\n", + "t_begin = time()\n", + "\n", + "for i in range (1,nb_iterations) :\n", + " #Set gas saturation to the new value since 1991 :\n", + " if i > TimeNewInit :\n", + " optimal_gas_saturation = optimal_gas_saturation2\n", + " \n", + " print (f'Progression : {int(i/nb_iterations*100)}%')\n", + "\n", + " #Get last values for pressure and gas_extraction\n", + " pres = pressures_list[-1]\n", + " gas_extraction = extractions_list[-1]\n", + "\n", + " #Compute the new pressure\n", + " new_pres = DiffMod.diffusion_process_for_control(optimal_permeability,optimal_porosity,optimal_gas_saturation, pres, gas_extraction)\n", + " pressures_list.append(new_pres)\n", + " #Set extraction values for the next iteration\n", + " new_extraction = pd.Series(data=historic_extraction[i,:].tolist(),index=wells_names) #Here we use a constant extraction rate\n", + " extractions_list.append(new_extraction)\n", + " \n", + "\n", + "\n", + "t_end = time()\n", + "\n", + "print ('Total time for main loop : ' + str(t_end-t_begin))\n", + "print ('Mean time per iteration : ' + str((t_end-t_begin)/nb_iterations))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Save simulation results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print ('Saving simulations results...')\n", + "with open(results_folder + 'extractions_list.npy', 'wb') as f:\n", + " np.save(f,extractions_list)\n", + "with open(results_folder + 'pressures_list.npy', 'wb') as f:\n", + " np.save(f,pressures_list)\n", + "print (\"[DONE]\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fist plot: temporal evolution\n", + "\n", + "fig,axs = plt.subplots(3,1,figsize=(10,15))\n", + "ax=axs[0]\n", + "extractions_sum = [np.sum(extractions_list[ii]) for ii in range(len(extractions_list))]\n", + "ax.plot(extractions_sum,c='k',lw=3)\n", + "ax.set_xlabel('Month since 1956.01')\n", + "ax.set_ylabel('Total extraction')\n", + "ax=axs[1]\n", + "extractions_indwells = [[extractions_list[ii][well] for ii in range(len(extractions_list))] for well in range(Well.shape[0])]\n", + "for well in range(Well.shape[0]):\n", + " ax.plot(extractions_indwells[well],alpha=0.5)\n", + "ax.set_xlabel('Month since 1956.01')\n", + "ax.set_ylabel('Individual well extraction')\n", + "ax=axs[2]\n", + "pressures_avg = [np.mean(pressures_list[ii]) for ii in range(len(extractions_list))]\n", + "ax.plot(pressures_avg,c='navy',lw=3)\n", + "ax.set_xlabel('Month since 1956.01')\n", + "ax.set_ylabel('Average reservoir pressure')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Maps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# second plot: top view maps\n", + "plot_iterations = [100,200,500,800]\n", + "\n", + "extractions_indwells = [[extractions_list[ii][well] for ii in range(len(extractions_list))] for well in range(Well.shape[0])]\n", + "vmin_e = np.min([np.min(extraction) for extraction in extractions_indwells])\n", + "vmax_e = np.max([np.max(extraction) for extraction in extractions_indwells])\n", + "\n", + "vmin_p = 5\n", + "vmax_p = 35\n", + "\n", + "\n", + "fig,axs = plt.subplots(2,len(plot_iterations),figsize=(5*len(plot_iterations),10))\n", + "for kk in range(len(plot_iterations)):\n", + " ax = axs[0,kk]\n", + " plot_it = plot_iterations[kk]\n", + " for well in range(Well.shape[0]):\n", + " quad1 = ax.scatter(float(Well[well,1]),float(Well[well,2]),c=np.sum(extractions_indwells[well][:plot_it]),vmin=vmin_e,vmax=vmax_e,cmap='coolwarm')\n", + " ax.text(float(Well[well,1]),float(Well[well,2])-1,Well[well,0])\n", + " ax.plot(Outline[2],Outline[3],c='k')\n", + " ax.set_aspect('equal')\n", + " ax.set_title(f'After {plot_it} months')\n", + "\n", + "for kk in range(len(plot_iterations)):\n", + " ax = axs[1,kk]\n", + " plot_it = plot_iterations[kk]\n", + " quad2 = ax.scatter(MESH[:,0],MESH[:,1],c=pressures_list[plot_it],vmin=vmin_p,vmax=vmax_p,cmap='PuBuGn')\n", + " \n", + " ax.plot(Outline[2],Outline[3],c='k')\n", + " ax.set_aspect('equal')\n", + " \n", + "cb_extr = fig.colorbar(quad1,ax=axs[0,-1],orientation='vertical',label='Cumulative extraction (Mm3)')\n", + "cb_press = fig.colorbar(quad2,ax=axs[1,-1],orientation='vertical',label='Pressure (MPa)')\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.subplots_adjust(wspace=-0.4,hspace=0.08)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/flow2quake/cases/groningen-reservoir-model/util/auxiliary.py b/flow2quake/cases/groningen-reservoir-model/util/auxiliary.py new file mode 100644 index 0000000..d3a3712 --- /dev/null +++ b/flow2quake/cases/groningen-reservoir-model/util/auxiliary.py @@ -0,0 +1,78 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +def UpdateGasExtractionData( + GasData_file="./Data/WellInformation.npy", + winter="ColdWinter", + Excel_file="../../Workshop2021/UpdatedData/Gaswinning-maandelijks_2010_2022.xlsx", + Extrapolate_increase=False, + plot_YN=False): + """ + Updates gas extraction data from an Excel file and optionally extrapolates values. + + Args: + GasData_file (str): Path to the existing gas data file. + winter (str): Specifies the winter type to use ('ColdWinter', 'AverageWinter', or 'HotWinter'). + Excel_file (str): Path to the Excel file containing updated data. + Extrapolate_increase (bool): If True, extrapolates data beyond the last available date. + plot_YN (bool): If True, generates a plot to visualize the updated data. + + Returns: + pd.DataFrame: Updated gas extraction data. + """ + + # Load updated data from Excel file + # For this to work, the second column of the file Gaswinning-maandelijks needs to contain the updated well clusters acronyms + a = pd.read_excel(Excel_file) + names1 = a[a.columns[0]][:-2] + names2 = a[a.columns[1]][:-2] + data = a[a.columns[2:-1]][:-2].T + data.columns = names2 + idx = [type(data.index[ii]) != str for ii in range(len(data.index))] + data = data[idx] + # Load existing gas data + GasData = np.load(GasData_file, allow_pickle=True).item() + + # Find matching indices for data alignment + idx0 = np.argmin(abs(GasData["Date"] - data.index[0])) + idx1 = np.argmin(abs(GasData["Date"] - data.index[-1])) + + # Update gas extraction data + df1 = GasData[winter].copy() + df1.iloc[idx0:idx1 + 1] = 0 # Clear existing data in the specified range + for col in data.columns: + df1[col].iloc[idx0:idx1 + 1] = data[col] # Insert updated data + + # Extrapolate data if requested + if Extrapolate_increase: + df1.iloc[idx1 + 1 :] = df1.iloc[500 : 500 + len(df1.iloc[idx1 + 1 :])] + + # Generate plot to check consistency (if requested) + if plot_YN: + fig, ax = plt.subplots(1, 1, figsize=(15, 10)) + for scenario in ["HotWinter", "AverageWinter", "ColdWinter"]: + GasData[scenario].sum(axis=1).plot( + x=GasData["Date"], ax=ax, ls="--", label=scenario + ) + df1.sum(axis=1).plot(x=GasData["Date"], ax=ax, label="Corrected data") + ax.axvline(x=idx1 + 1, c="r", ls="-.", label="End of data") + ax.legend() + ax.set_xlabel("Month since 1956") + ax.set_ylabel("Total gas extraction") + + return df1 + + + +import os +def file_exists(file_path): + """ + Checks if a file exists at the specified path. + Args: + file_path (str): The path to the file to check. + Returns: + bool: True if the file exists, False otherwise. + """ + # Use the os.path.isfile function to determine if the file exists + return os.path.isfile(file_path) diff --git a/flow2quake/cases/groningen-reservoir-model/util/diffusion_model.py b/flow2quake/cases/groningen-reservoir-model/util/diffusion_model.py new file mode 100644 index 0000000..0cd1692 --- /dev/null +++ b/flow2quake/cases/groningen-reservoir-model/util/diffusion_model.py @@ -0,0 +1,284 @@ +import numpy as np +import pandas as pd +from fenics import * +from scipy import signal +from mshr import * + +class DiffusionModel: + """A class that computes the pressure depletion inside a reservoir given + its physical properties (boundaries,thickness,permeability,porosity, + temperature,initial pressure), the gas compositon (or Molar Weight) + and the spatial and temporal parameters (grid,timestep and + extracted volume)""" + + def __init__(self, + x: np.ndarray, + y: np.ndarray, + thickness: np.ndarray, + temperature: int, + initial_pressure: float, + gas_molecular_weight: float, + num_steps: int, + time_step: float, + extraction_data: pd.DataFrame, + wells: np.ndarray, + outline: pd.DataFrame, + p_new_init: np.ndarray = None, + begin: int = 0, + name: str = 'DiffusionModel'): + """ + Initializes the reservoir model with invariant parameters. + """ + + # Model name and settings: + self.name = name + self.begin = begin + + # Input data: + self.x_meshgrid = x + self.y_meshgrid = y + self.outline = outline + self.reservoir_temperature = temperature # In Kelvin + self.gas_molecular_weight = gas_molecular_weight # In kg/mol + self.pressure_new_init = p_new_init + self.extraction_data = extraction_data + self.wells = wells + + # Mesh dimensions: + self.dim_x = self.x_meshgrid.shape[0] - 1 + self.dim_y = self.x_meshgrid.shape[1] - 1 + + # Time parameters: + self.step_number = num_steps # Number of time steps in months + self.d_t = time_step # Duration of a time step in seconds + + # Derived constants: + self.d_rho = (self.gas_molecular_weight / (8.314 * self.reservoir_temperature)) * 1e6 / 3600**2 # dρ/dp + self.rho_std_condition = self.rho(101325 * 1e-6, 293.25) # Density in kg/m³ + + # Create rectangular mesh for thickness: + self.mesh = RectangleMesh(Point(self.x_meshgrid[0, 0], self.y_meshgrid[0, 0]), + Point(self.x_meshgrid[-1, 0], self.y_meshgrid[0, -1]), + self.dim_x, self.dim_y) + self.w = FunctionSpace(self.mesh, 'CG', 1) + self.coordinates = self.mesh.coordinates() + self.intermediate_thickness = self.array_2d_to_fenics(thickness, 1e-6) + + # Create triangle mesh for flexibility: + mesh_x = signal.resample(self.outline[2], 500)[::-1] + mesh_y = signal.resample(self.outline[3], 500)[::-1] + structure = [Point(x, y) for x, y in zip(mesh_x, mesh_y)] + domain = Polygon(structure) + self.mesh2 = generate_mesh(domain, 40) + self.v = FunctionSpace(self.mesh2, 'CG', 1) + self.coordinates2_ = self.mesh2.coordinates() + + # Interpolate thickness onto triangle mesh: + self.thickness = Function(self.v) + self.thickness.interpolate(self.intermediate_thickness) + + # Define initial pressure on the mesh: + self.initial_pressure = interpolate(Constant(initial_pressure), self.v) + + + def array_2d_to_fenics(self, numpy_2d_array: np.ndarray, error_avoir_number: float = None) -> Function: + """ + Converts a 2D NumPy array to a Fenics array, handling potential errors and adjusting for mesh layout. + + Args: + numpy_2d_array: The 2D NumPy array to convert. + error_avoir_number: An optional value to add to all elements of the array for error mitigation. + + Returns: + The converted Fenics array. + """ + + # Handle potential errors: + numpy_2d_array = np.nan_to_num(numpy_2d_array) # Replace NaNs with numerical values + + # Transpose the array to match Fenics mesh layout: + numpy_2d_array = numpy_2d_array.T + + # Flatten the array for assignment to Fenics function: + numpy_array = numpy_2d_array.reshape((self.dim_x + 1) * (self.dim_y + 1)) + + # Add error mitigation value if specified: + if error_avoir_number is not None: + numpy_array = numpy_array + np.ones_like(numpy_array) * error_avoir_number + + # Create the Fenics function and assign values: + fenics_array = Function(self.w) + fenics_array.vector()[:] = numpy_array[dof_to_vertex_map(self.w)] + + return fenics_array + + def array_1d_to_fenics(self, numpy_1d_array: np.ndarray, error_avoid_number: float = None) -> Function: + """ + Converts a 1D NumPy array to a Fenics array, optionally adding a value to all elements for error mitigation. + + Args: + numpy_1d_array: The 1D NumPy array to convert. + error_avoid_number: An optional value to add to all elements of the array for error mitigation. + + Returns: + The converted Fenics array. + """ + + # Add error mitigation value if specified: + if error_avoid_number is not None: + numpy_1d_array = numpy_1d_array + np.ones_like(numpy_1d_array) * error_avoid_number + + # Create the Fenics function and assign values: + fenics_array = Function(self.v) + fenics_array.vector()[:] = numpy_1d_array[dof_to_vertex_map(self.v)] + + return fenics_array + + + def rho(self, + p, + temperature: float = None): + """Function that computes the density of a gas given the molecular + weight of the gas, its pressure, and its temperature + If no temperature is given, Temperature of Reservoir is by default + Pressure given in MPa, result in kg.km-3""" + if temperature is None: + return p*1e6 * self.gas_molecular_weight / \ + (8.314 * self.reservoir_temperature) *1e9#dim change change2 + + return p*1e6 * self.gas_molecular_weight / (8.314 * temperature) *1e9 #dim change change2 + + def viscosity(self, p): + """Compute the viscosity of a gas given its density. + Empirical Formula from Lee–Gonzalez Semiempirical Method (1966) + + !!!!!!!Constants are set for Groningen parameters, they need + to be changed if another case study!!!!!!!!""" + vertex_values = p.compute_vertex_values(self.mesh2) + rho_vertex = self.rho(vertex_values) / 1000 / 1e9 #pressure change : *(1e9*3600**2), change2 + + viscosity = 137.071e-4 * np.exp(5.094 * rho_vertex **1.314) / 1000 * 1000*3600 #dim change 1e9 et 1000*3600 + fenics_viscosity = self.array_1d_to_fenics(viscosity) + return fenics_viscosity + + def distance(self, array: np.ndarray, x: float, y: float): + """ + Determines the index, distance, and coordinates of the point in the given array that is closest to a target point. + Args: + array: A 2D NumPy array of coordinates, where each row represents a point. + x: The x-coordinate of the target point. + y: The y-coordinate of the target point. + Returns: + - The index of the closest point in the array. + - The distance between the target point and the closest point. + - The x-coordinate of the closest point. + - The y-coordinate of the closest point. + """ + closest_index = None + closest_distance = float('inf') # Initialize with positive infinity + closest_x = None + closest_y = None + + for i, (point_x, point_y) in enumerate(array): + distance_to_point = np.sqrt((x - point_x)**2 + (y - point_y)**2) + if distance_to_point < closest_distance: + closest_index = i + closest_distance = distance_to_point + closest_x = point_x + closest_y = point_y + + return closest_index, closest_distance, closest_x, closest_y + + + def well_pressure_difference(self, + measurements_data: pd.DataFrame): + """ + Calculates the total absolute difference between measured well pressures and calculated pressures + at the closest points on the grid, as well as the number of valid measurements used. + + Args: + measurements_data: A pandas DataFrame containing well pressure measurements. + + Returns: + A tuple containing: + - The total absolute well pressure difference. + - The number of valid measurements used in the calculation. + """ + + # Find the indices of the closest grid points to each well: + location = np.zeros(self.wells.shape[0]) + for k in range(self.wells.shape[0]): + location[k] = self.distance(self.coordinates2_, int(Well[k][1]), int(Well[k][2]))[0] + + # Initialize variables for calculating the difference: + well_difference = 0 + well_nb_measurements = 0 + + # Determine the maximum number of time steps to consider: + IHM = min(self.step_number, len(measurements_data)) + + # Iterate through time steps and wells: + for T in range(self.begin, IHM): + for i in range(self.wells.shape[0]): + # Check if the measurement is valid: + if np.isfinite(measurements_data[self.wells[i][0]][T]): + # Calculate the absolute difference between measured and calculated pressures: + difference = abs( + measurements_data[self.wells[i][0]][T] - self.PArray_[int(location[i]), T] + ) + well_difference += difference + well_nb_measurements += 1 + + return well_difference, well_nb_measurements + + def diffusion_process_for_control(self, + permeability: float, + porosity: float, + gassat: float, + instantaneous_pressure: np.ndarray, + instantaneous_extraction_data: np.ndarray): + """ + Computes the diffusion model for a single iteration, given specific parameters + and instantaneous pressure and extraction data. Returns the pressure field at time t+1. + """ + + # Initialize pressure array: + self.PArray_ = np.zeros((self.coordinates2_.shape[0], self.step_number)) + + # Set parameters and trial/test functions: + permeability_fenics = Constant(permeability) + self.permeability_ = interpolate(permeability_fenics, self.v) + p = TrialFunction(self.v) + p0i = self.array_1d_to_fenics(instantaneous_pressure) + p0 = interpolate(p0i, self.v) + v = TestFunction(self.v) + + # Define variational problem for pressure: + a = ((porosity * self.d_rho * self.thickness) * p * (1e9 * 3600**2) * v * dx + + (self.d_t * self.thickness * self.permeability_ + / self.viscosity(p0)) * dot(self.rho(p0) * grad(p * (1e9 * 3600**2)), grad(v)) * dx) + L = ((porosity * self.d_rho * self.thickness) * p0 * (1e9 * 3600**2) * v * dx) + + # Define point sources for extraction: + point_sources = [] + for i in range(self.wells.shape[0]): + extraction_rate = -instantaneous_extraction_data[self.wells[i][0]] # Convert to positive + point_sources.append((Point(float(self.wells[i][1]), float(self.wells[i][2])), + extraction_rate * self.d_t * self.rho_std_condition / gassat * 1e-9)) + ps = PointSource(self.v, point_sources) + + # Assemble and solve the system: + b = assemble(L) + A = assemble(a) + ps.apply(b) + self.p1_ = Function(self.v) + self.p1_.assign(p0) + solver = KrylovSolver('minres', 'hypre_euclid') + solver.parameters["maximum_iterations"] = 1000 + solver.parameters["error_on_nonconvergence"] = False # Set to True for debugging + solver.solve(A, self.p1_.vector(), b) + + # Extract vertex values and return: + self.vertex_values_P_ = self.p1_.compute_vertex_values(self.mesh2) + return self.vertex_values_P_ + diff --git a/flow2quake/cases/groningen-reservoir-model/util/well_config.py b/flow2quake/cases/groningen-reservoir-model/util/well_config.py new file mode 100644 index 0000000..33b1f66 --- /dev/null +++ b/flow2quake/cases/groningen-reservoir-model/util/well_config.py @@ -0,0 +1,53 @@ +import numpy as np +import pandas as pd + +def create_well_configuration(well_configuration, Well, GasData): + """ + Creates well configuration data based on the specified configuration type. + + Args: + well_configuration (int): The type of well configuration to create. + Well (np.ndarray): Array containing well data for Groningen real wells. + GasData (pd.DataFrame): DataFrame containing gas data for Groningen wells. + + Returns: + tuple: (wells_names, wells_locations, Well, initial_extraction_data) + """ + + wells_list = [] # List to store well information + + if well_configuration == 1: # Unique well + wells_names = ['Unique well'] + wells_locations = np.array([[252.500, 587.500]]) + initial_extraction_rate = [4e8 / (30.4375 * 24)] # m3/hour + + elif well_configuration == 2: # Two wells (north and south) + wells_names = ['Extraction well', 'Injection well'] + wells_locations = np.array([[260.000, 575.000], [245.000, 600.000]]) + initial_extraction_rate = [4e8 / (30.4375 * 24), -4e8 / (30.4375 * 24)] + + elif well_configuration == 5: # Five wells (central and surrounding) + wells_names = ['Constant well', 'Variable well up_right', 'Variable well up_left', + 'Variable well down_left', 'Variable well down_right'] + wells_locations = np.array([[252.500, 587.500], + [257.500, 592.500], [247.500, 592.500], + [247.500, 582.500], [257.500, 582.500]]) + initial_extraction_rate = [8e8 / (30.4375 * 24)] * 5 + + elif well_configuration == 29: # Groningen real wells + wells_names = [Well[i, 0] for i in range(Well.shape[0])] + wells_locations = np.array([[float(Well[i, 1]) / 1000, float(Well[i, 2]) / 1000] + for i in range(Well.shape[0])]) + historic_extraction = np.nan_to_num(GasData['AverageWinter'].fillna(0).rolling(1).mean().to_numpy()) / (30.4375 * 24) + initial_extraction_rate = historic_extraction[300, :].tolist() # Extraction rate of a random month + + else: + print('Wrong well_configuration') + return None + + # Create the Well array directly using list comprehension + Well = np.array([[name, str(loc[0]), str(loc[1])] for name, loc in zip(wells_names, wells_locations)]) + + initial_extraction_data = pd.Series(data=initial_extraction_rate, index=wells_names) + + return wells_names, wells_locations, Well, initial_extraction_data \ No newline at end of file diff --git a/flow2quake/common/__init__.py b/flow2quake/common/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flow2quake/common/mechanical/__init__.py b/flow2quake/common/mechanical/__init__.py new file mode 100644 index 0000000..dc40ff6 --- /dev/null +++ b/flow2quake/common/mechanical/__init__.py @@ -0,0 +1,3 @@ +""" +Geomechanical deformation functions +""" diff --git a/flow2quake/common/reservoir/__init__.py b/flow2quake/common/reservoir/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flow2quake/common/seismicity/__init__.py b/flow2quake/common/seismicity/__init__.py new file mode 100644 index 0000000..e69de29