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Learning whilst drilling

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# Mystic Bit

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](O&G_Hackathon_Mystic_Bit.pptx).

### Vision

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

### Results

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.

### Repository layout

- mysticbit: core python module containing ML models
- notebooks: Jupyter notebooks
- data: anonymized well log data data
- webapp/petex-hackathon: plotted/interactive charts

### Conda environment

To create the python environment (windows), use:
```bash
conda create -n mysticbit python=3 anaconda
conda activate mysticbit
python -m ipykernel install --name mysticbit
```

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