Geomechanical Injection Scenario Toolkit
Dependencies: Python (3.9.10), numpy (1.21.4), scipy (1.7.3), pandas (1.3.5)
Additional dependencies for plotting: seaborn, matplotlib, geopandas, contextily
Code is written as classes and subroutines in one .py file, with a second .py file or Jupyter notebook as a driver script
Injection processing, GIST computation, and visualization are broken up into separate steps and separate branches.
Main has been deprecated!
Code to regenerate results from IMAGE presentation in August 2022:
gistMC.py - first Monte-Carlo GIST code used for IMAGE
gistMCExample.py - driver script for IMAGE examples
Injection processing code:
injectionV3.py - basic injection data processing/merging for B3 TX and NM injection .csv files, (version 3)
injectionV3_driver.py - driver script to output time-regularized and filtered injection data with daily sampling
injectionV3_driver_10day.py - same as above, but with 10-day sampling to make the code run a bit faster for events with large numbers of wells
Most recent version:
gistMCLive.py - GIST class built for version 3 of the B3 data
gistMCLiveTarzan.py - driver script built around the Range Hill earthquake
gistPlots.py - collection of plotting functions to visualize results
GIST_Stanton_11-4-23_Compute.ipnyb - Jupyter notebook for 11/4/23 Stanton earthquake - computation
GIST_Stanton_11-4-23Plots.ipynb - Jupyter notebook for 11/4/23 Stanton earthquake - plotting
GIST_TestAnisotropy.ipynb - Jupyter notebook for testing azimuthal permeability anisotropy in gistMCLive
GIST_Stanton_TodayCompute.ipynb - Jupyter notebook for a hypothetical present-day Stanton earthquake - computation
- Validate pore pressure modeling codes
- Include Matlab prototype code
- Regression tests for Python code to match "Gold" results from Matlab code
- Rework series of discrete steps into a workflow:
- QC and edit injection data after well selection
- Update GIST parameterization after plots
- Anisotropy examples
- Incorporate BEG-checked data from Bob Reedy