Repo with the tools developed for predicting energy produced at OSR. It includes both simulation data for OSR, as well as Jupyter notebooks for training and evaluating prediction models.
Disclaimer: OSR was constructed based on the data from Brady Hot Springs reservoir (Nevada, USA) but has a number of sufficiently modified characteristics and does not disclose any sensitive data.
There are two ways to use the code in this repository:
- Use cloud integration provided by binder. Follow 1. Quick Start and then skip to 4. Notebooks.
- Use your own computing resources (e.g., a laptop or a machine in a datacenter) to install all dependencies. For ths option, skip 1. Quick Start and follow: 2. Requirements, 3. Running, and refer to 4. Notebooks for more information about the notebooks.
Option 1 is a great way to get started (no installation required), whereas Option 2 is more suited for long-term experimentation, futher developement, and situations where computing performance matters.
The easiest way to run the notebooks from this repo is through binder. Simply, click this icon to get started:
When run for the first time, the configuration process will take some time (i.e., a brand-new Docker image will need to be created based on the environment.yml file with required dependencies. After the initial slow run, the subsequent runs are going to be much faster.
This Binder integration allows running the notebooks in the cloud, through your browser, without the need to install and configure any Python packages locally. For more information about local installations, refer to the instructions provided in the remainder of this readme file. The main reason why a local installation might be selected is to achieve higher performance and faster processing: Binder resources are cloud resources that are shared across its numerous users, whereas, locally, the notebooks can use all computing resources that are available (on a laptop or on a dedicated compute node in the compute cluster being used).
After Binder completes the setup, you should see a page with the Jupyter icon at the top and the listing of the directories and files in this repo. This will be a fully functional instance of Jupyter notebook server, where you can nagivate to the notebooks folder and run the included notebooks (you can modify them if you want to as well). Refer to the summary of the notebooks near the end of this readme file for more information about each notebook.
conda is not a requirement strictly speaking, but it makes it very easy to get Python dependenies for the code included in this repo. If you don't currently have conda on your machine, it is recommended to install it by following instructions from: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
conda environment with the dependencies required to run code from this repo can be built using the environment.yml file and the following terminal command:
conda env create -f environment.yml
Note that enironment.yml is an environment file was tested and confirmed to be working on Eagle, NREL's supercomputer (more about machine specs: https://www.nrel.gov/hpc/eagle-system.html). No adjustment should be required for using it on similar Linux systems, whereas Windows and OSX machines might require some tuning in this file (e.g., changing versions of the packages).
Once the last command successfully completes, run:
conda activate geothermal_osr
The last command should be run every time you close the terminal window with the activated environment and want to go back to running the code using a new terminal. When the environment is activated, the terminal prompt should start with:
(geothermal_osr)
Alternatively, if you choose to avoid using conda, install manually all Python packages listed in environment.yml and that should be sufficient for running the notebooks.
From the terminal, launch JupyterLab by running:
jupyter lab --no-browser --ip=0.0.0.0
This works well for running JupyterLab on a remote machine and then using ssh to set up port forwarding (i.e., ssh -L <local_port>:localhost:<remote_port> <hostname>). If you run the notebooks on a local machine, it is enough to run: jupyter lab.
After this step, open your browser and navigate to URL that looks like this:
http://127.0.0.1:<port>/?token=XXXYYY...ZZZ -- this URL can be found near the end of the output of the jupyter command in the terminal.
This should take you to the running instance of JupyterLab and you should see the directory listing on the left-hand side of the screen.
Open the notebooks directory where you can find the osr-[0-3]-*.ipynb notebooks. It is recommended that you run them in the logical order, according to the numbers in the names.
Summary of the available notebooks:
notebooks/osr-0-minimal.ipynb-- minimal notebook that trains a single model on the Train subset and evalutes it on the Test subset.notebooks/osr-1-train_with_cross_validation.ipynb-- notebook with cross-validation for model training. Takes a long time to run for a fairly large set of hyperparameter combinations being evaluated. Saves results of cross-validation into a file, which can be read by the following notebook.notebooks/osr-2-analyze_cross_validation.ipynb-- loads results of cross-validation from the specified file, displays summaries, and produces plots.notebooks/osr-3-train_production.ipynb-- trains production models according to the configuration selected based on cross-validation; the notebook also includes final model evaluation and sensitivity analysis. Takes a long time to run.osr-4-use_production.ipynb-- notebook that loads the trained models and runs them to predict the test-subset cases (i.e., the ones that weren't used in the model training). It is fast to run, and it produces plots with true & predicted timeseries for pressure and temperature values for the test cases; it also produces a dataframe with the results of error analysis.
Our 2022 paper "Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning" is available at: https://www.mdpi.com/1996-1073/15/3/967
Written by: Dmitry Duplyakin (dmitry.duplyakin@nrel.gov) in collaboration with the National Renewable Energy Laboratories.
Full team: Dmitry Duplyakin, Koenraad F. Beckers, Drew L. Siler, Michael J. Martin, Henry E. Johnston
Refer to the file called: LICENSE.