Project from OGA Hackathon London 2018.
- Connor Tann
- Justin Boylan-Toomey
- Patrick Davies
- Lawrie Cowley
- Alessandro Cristofori
- Dan Austin
- Jeremy Fortun
For a summary of the project, see the PowerPoint presentation.
Real-time, near-bit prediction 1-40m ahead of the drill-bit, using offset well log data
Also predict the uncertainty range.
Delivering value through:
- Improved drilling safety
- Faster decision making
- Improved well targeting
- Leveraging existing field observations
A set of 30 Gradient Boosting Decision Tree Regressors were successfully trained on the well data, enabling prediction ahead of the bit. Lagged OH features were created, and a quartile loss function was used to capture uncertainty. 30+ separate models trained!
A mysticbit Python module was created to deploy the ML framework
Web app created with Flask, Plotly and Dash.
- mysticbit: core python module containing ML models
- notebooks: Jupyter notebooks
- data: anonymized well log data data
- webapp/petex-hackathon: plotted/interactive charts
To create the python environment (windows), use:
conda create -n mysticbit python=3 anaconda conda activate mysticbit python -m ipykernel install --name mysticbit