Python package to assist in providing quick-look/ preliminary petrophysical estimation.

1. Create virtual environment (tested working with Python3.10.9)

   python -m venv venv

2. Activate virtual environment

   venv\Scripts\activate (Windows)

   source venv/bin/activate (Linux)

3. Install requirements

   pip install -r requirements.txt

4. Launch the notebook and run the cells

   • 01_data_handler: create the MOCK qppp project file.
   • 02_EDA: quick look on the data
   • 03_*: quick petropohysical interpretation of the MOCK wells.
   • For API notebook, need to run the following before running the cells

     uvicorn quick_pp.api.main:app

To install, use the following command:

pip install quick_pp

To use qpp_assistant, you would need to;

1. Install Ollama
2. Run ollama pull qwen3 in the terminal

To start the App

quick_pp app

You can then access the Swagger UI at http://localhost:8888/docs and qpp_assistant at http://localhost:8888/qpp_assistant.

To use the mcp tools, you would need to first add the following SSE URLS through the interface; http://localhost:8888/mcp - quick_pp tools.

http://localhost:5555/mcp - quick_pp ML model prediction tools (will only be available after ML models are trained and registered).

To train an ML model, these are the requirements;

1. The input file in parquet format need to be available; /data/input/<data_hash>___.parquet
2. The parquet file need to have the input and target features as specified in MODELLING_CONFIG in config.py.

quick_pp train <model_config> <data_hash>

Example >> quick_pp train mock mock

To run the MLflow server

quick_pp mlflow-server

To run prediction, the trained models need to be registered in MLflow first.

quick_pp predict <model_config> <data_hash>

You can access the mlflow server at http://localhost:5015

To deploy the trained ML models

quick_pp model-deployment

You can access the deployed model Swagger UI at http://localhost:5555/docs

Documentation is available at: https://quick-pp.readthedocs.io/en/latest/index.html