Introduction
Python for Well Log Data Analysis and Visualisation is a cumlmination of multiple articles that I have published over the years on Medium, LinkedIn, my website and on YouTube. One of the issues with this, was that content was disassociated and scattered in multiple places. This book is an attempt to bring all of that content together in a single place, which can be gradually expanded.
At the time of writing, there is not a single source for introducing Python to geoscientists and petrophysicists. The aim of this book is to be that reference.
If you are new to python or curious to see how it can be used to solve everyday petrophysical tasks, including visualisation, working with various file formats and applying AI tools to problems, then this book is an excellent place to start.
What this is
Python for Well Log Data Analysis and Visualisation is a practical guide to working with subsurface data using Python, in particular well log and petrophysical data.
It focuses on the patterns, workflows, and ways of thinking that make subsurface data analysis clearer, more reliable, and easier to reason about — particularly when dealing with depth-indexed data, messy measurements, and real-world constraints.
This is not a Python tutorial that teaches Python from scratch, nor is not a petrophysics textbook.
It sits deliberately in between: at the point where subsurface domain knowledge meets data analysis, visualisation and machine learning.
What this is not
This book intentionally avoids:
- full petrophysical interpretation workflows
- proprietary heuristics or company-specific practices
- detailed derivations of petrophysical equations
Examples and techniques are used to illustrate approaches and decision-making, not to prescribe authoritative interpretations.
If you are looking for a comprehensive guide to petrophysical theory, this is not that book.
Prerequisites
This book assumes a basic familiarity with Python programming syntax and does not cover the foundations.
For this, the reader is referred to the following resources which will help get you started.
Who this is for
This book is written for people who:
- work with subsurface data (logs, curves, measurements, models)
- already know some Python, or are learning it through use
- care about data quality, visualisation, and reproducibility
You do not need to be a data scientist.
You do not need to be a Python expert. You do not need to be a Petrophysical expert.
You do need to be curious about how you can use python to improve your interpretation workflow and understanding of your data.
How to use this book
You can read this book front-to-back, but it is also designed to be dipped into.
Each chapter focuses on a specific idea: - how subsurface data behaves - how problems reveal themselves visually - how models fail quietly - how small design decisions compound over time
A note on data and scope
All examples use public, synthetic, or anonymised datasets.
The goal is to explore general patterns that apply across subsurface work, not to recreate any specific field, asset, or interpretation.
Where details are abstracted or simplified, this is done deliberately — to keep the focus on transferable ideas rather than domain-specific implementation.