This repository contains code for the synthetic case studies used as part of the paper "Ensemble Kalman Inversion for Geothermal Reservoir Modelling", by Alex de Beer, Elvar K. Bjarkason, Michael Gravatt, Ruanui Nicholson, John P. O'Sullivan, Michael J. O'Sullivan, and Oliver J. Maclaren. Ensemble Kalman inversion (EKI; [1, 2]) is a derivative-free algorithm that iteratively updates a set of interacting particles to produce an approximation to the posterior distribution of the model parameters.
@misc{deBeer2024,
title={Ensemble Kalman Inversion for Geothermal Reservoir Modelling},
author={Alex de Beer and Elvar K Bjarkason and Michael Gravatt and Ruanui Nicholson and John P O'Sullivan and Michael J O'Sullivan and Oliver J Maclaren},
year={2024},
eprint={2410.09017},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2410.09017}
}
To run the code in this repository on a local machine, first install Python >= 3.8. Then, clone the repository and install the project dependencies using
pip install -r requirements.txt
You will need to install Waiwera to simuate each model. For information on getting started with Waiwera, consult the Waiwera website.
To run EKI on each model, use the scripts in the top level of the repository (adjusting the algorithm parameters where appropriate).
To reproduce the plots in the main body of the paper, run the plot_slice.py (vertical slice model) or plot_fault.py (three-dimensional model) scripts in the top level of the repository.
To reproduce the plots in the supplementary material, run plot_slice_supplement.py or plot_fault_supplement.py.
If you have any questions about the code or the content of the paper, please email [email protected], or open an issue.
[1] Iglesias, MA, Law, KJ, and Stuart, AM (2013). Ensemble Kalman methods for inverse problems. Inverse Problems 29, 045001.
[2] Iglesias, M and Yang, Y (2021). Adaptive regularisation for ensemble Kalman inversion. Inverse Problems 37, 025008.