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Python Fundamentals
From zero to fluent — variables, functions, OOP, and the scientific stack tailored for petroleum workflows.
1st Edition · 2026
Python for
Oil & Gas
Data Science, Engineering Analytics, and AI
in the Petroleum Industry.
24 hands-on chapters taking you from Python fundamentals through PVT correlations, decline curve analysis, and reservoir simulation — all the way to deep learning and cloud deployment. Built on real Niger Delta field data.
By Johnpaul Okeke · SPE Member
What You'll Learn
From Setup to Production
Each chapter builds on the last — starting with Python basics and ending with production-grade ML models deployed on the cloud. Every concept is grounded in real petroleum engineering problems.
database
Petroleum Data Engineering
Load, clean, and transform LAS files, production CSVs, and well test data using Pandas and domain libraries.
calculate
Well Log Analysis
Compute porosity, water saturation, and net pay with Archie's equation and triple-combo log interpretation.
insights
Production Forecasting
Apply Arps decline curves, material balance, and nodal analysis to predict well and reservoir performance.
dashboard
Machine Learning
Build supervised and unsupervised models for facies classification, production prediction, and anomaly detection.
biotech
Dashboards & Deployment
Ship production-ready Streamlit and Dash apps, deploy to the cloud, and integrate with real-time SCADA data.
Interactive Playground
Write Real Engineering Code
Every chapter includes runnable Python code. Edit it, run it, see the output — right in your browser. No installation required.
hydrostatic_pressure.py
def calculate_hydrostatic_pressure(tvd_ft, mud_weight_ppg):
"""Compute hydrostatic pressure in psi."""
pressure_psi = 0.052 * mud_weight_ppg * tvd_ft
return pressure_psi
# OML 58 — Well OD-003, 10,000 ft TVD, 12.5 ppg mud
p = calculate_hydrostatic_pressure(10_000, 12.5)
print(f"Hydrostatic Pressure: {p:,.0f} psi")
>>> Hydrostatic Pressure: 6,500 psi
Table of Contents
24 Chapters. 5 Parts. One Complete Journey.
Chapters 1 and 2 are free — no signup required. Activate the book to unlock all chapters, the interactive playground, and exercises.
Part I: Python Fundamentals
Part II: Petroleum Data Engineering
Part III: Reservoir & Production Engineering
Part IV: Machine Learning & AI
Who This Book Is For
Built for Engineers Who Build Things
school
Engineering Students
Supplement your petroleum engineering coursework with practical Python skills that employers actually look for.
engineering
Working Engineers
Automate repetitive calculations, build custom analysis tools, and make data-driven decisions faster.
code
Python Developers
Apply your existing programming skills to the energy sector — one of the world's largest and most data-rich industries.
About the Author
Johnpaul Okeke
Petroleum engineer, SPE member, and the lead developer behind PhiDrillSim — a Python-based drilling simulation engine that models torque-and-drag, hydraulics, and rate-of-penetration in real time. Former VP of the NGA UNILAG Student Chapter, where he organized technical workshops bridging petroleum engineering and modern software practices.
His work focuses on what he calls “Industrial Intelligence” — applying Python, data science, and AI to solve real problems in the Nigerian oil and gas sector and beyond. This book distills that experience into 24 practical chapters.
verifiedSPE MemberterminalPhiDrillSim LeadschoolUNILAG
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Digital Edition
$29
One-time purchase. Lifetime updates.
- check_circleAll 24 chapters (PDF + EPUB)
- check_circleInteractive code playground
- check_circleAll exercises with solutions
- check_circle5 real industry datasets
- check_circleProgress tracking dashboard
Recommended
Print + Digital
$49
Everything digital, plus the hardcover.
- check_circleEverything in Digital Edition
- check_circlePremium hardcover (full color)
- check_circleQuick reference card included
- check_circleFree global shipping
- check_circlePriority support from the author
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