Learn to Build Python and Machine Learning Tools for Real Drilling Problems
Machine Learning Application for Drilling Engineering Using Python
5 Days of Live Online Practical Training with Dr. Behzad Elahifa
Machine Learning Application for Drilling Engineering Using Python
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Machine Learning Application for Drilling Engineering Using Python - DP26PEA
| Code | Date | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|
| DP26PEA | 02 - 06 Nov 2026 | 4 Hours Per Day |
Online |
USD |
3000 |
The Classes Will be Online Via Zoom from Mon to Friday starting 9 PM Indian Time for 4 hours each session.
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Machine Learning Application for Drilling Engineering Using Python
A 5-day online practical training that teaches drilling engineers how to use Python and machine learning to work with drilling data — from hydraulics and well control calculations to ROP and formation prediction. Live coding with real datasets throughout.
Description
This 5-day online training combines drilling engineering with practical Python programming and machine learning. Participants start with drilling data collection and transmission, move into Python coding for standard drilling calculations — well hydraulics, MPD, well control, trajectory design — and then apply machine learning methods such as linear regression, random forest, decision trees, and gradient boosting to real drilling problems like ROP prediction and formation prediction. Every session is hands-on. Participants code live, work on real datasets, and complete a capstone project so they can apply what they learn at work immediately. No prior programming experience is required.
Demo Class
Faster wells, lower non-productive time, and safer operations depend on how well drilling teams use their data. Most rigs still run on manual spreadsheets or vendor software that engineers cannot modify. This course closes that gap. By combining drilling domain knowledge with Python and machine learning, engineers gain predictive capability that until recently was limited to the largest operators — and they build it themselves, in tools they fully control.
By the end of this training, participants will be able to:
- Understand the fundamentals of digitalization in drilling engineering
- Capture, clean, and visualise surface and downhole drilling data
- Write Python code for drilling calculations using NumPy and Pandas
- Apply data analytics techniques to optimize drilling performance
- Evaluate and select appropriate downhole communication systems
- Build and compare machine learning models (Scikit-learn) for ROP, formation, and risk prediction
- Implement digital solutions that improve drilling safety and efficiency
The training is delivered live online over 5 days. Each session combines short concept briefings with live coding demonstrations, guided exercises on real drilling datasets, and open Q&A. Participants write and run code alongside the instructor rather than watching slides. A capstone project ties the full workflow together — from raw data to a working prediction model — and all code, datasets, and session recordings are provided for continued practice after the course.
Organisations sending participants to this training will:
- Build in-house capability to analyze drilling data without depending on vendor software
- Reduce non-productive time through data-driven identification of drilling problems
- Improve drilling performance with ROP and formation prediction models built on their own data
- Standardize drilling calculations in transparent, auditable Python tools instead of scattered spreadsheets
- Strengthen well control and safety decisions with better use of real-time data
- Prepare drilling teams for the industry-wide shift toward automated and digital drilling operations
Participants will:
- Gain working Python programming skills applied directly to drilling engineering
- Learn to build, test, and interpret machine learning models with confidence
- Automate repetitive drilling calculations and reporting tasks
- Add a clearly in-demand digital skill set to their professional profile
- Leave with a personal library of working code and a completed capstone project
- Be able to lead or contribute to digitalization initiatives in their teams
- Drilling engineers and drilling supervisors
- Well engineers and well planning engineers
- Drilling optimization and real-time operations engineers
- Petroleum engineers moving into data-driven roles
- Drilling fluids, MPD, and well control specialists
- Engineering graduates and early-career professionals targeting drilling and digitalization roles
No prior coding experience is needed — Python is taught from the basics.
Module 1 — Drilling Data and Python Foundations
- Drilling (raw) data measurement and collection
- Data transmission and downhole measurement
- Python coding basics
- Python for drilling calculations using the NumPy library
- Drilling sequences and algorithms with the Pandas library — well hydraulics, MPD, CML, drilling problems, well control, trajectory design
Module 2 — Visualisation, Automation, and Machine Learning
- Drilling data visualisation with the Matplotlib library
- Automation of drilling workflows with Seaborn and Scikit-learn
- Machine learning methods — linear regression, random forest, decision trees, gradient boosting, and more
- Applying ML to drilling and geoscience projects — ROP prediction, formation prediction, and a capstone project
On successful completion of this training course, PEA Certificate will be awarded to the delegates
Dr. Behzad Elahifar – Associate Professor, NTNU
With over 20 years of experience in the oil and gas industry, Dr. Behzad Elahifar brings extensive expertise in drilling engineering, digitalization, and automation. He holds a Ph.D. in Drilling Engineering from the University of Leoben, an MEng in Petroleum Well Engineering from Curtin University, and a BSc in Drilling Engineering from the Petroleum University of Technology.
He began his career as a Drilling Supervisor with NIOC and PPZ, before moving into specialized roles with leading international companies. As a Drilling Engineer with Advanced Drilling Solutions (TDE) and later as Project Manager / MPD Specialist with Enhanced Drilling, he designed and executed managed pressure drilling and cementing operations across the North Sea, Barents Sea, Gulf of Mexico, Caspian Sea, and Norwegian Continental Shelf. He then served as Project Manager at NOV (National Oilwell Varco), working with Wired Drill Pipe and real-time downhole communication systems.
For more than a decade, Dr. Elahifar has combined industry consulting with academic leadership. As a Drilling Instructor and Researcher at the University of Leoben and now as an Associate Professor in the Department of Geoscience and Petroleum at NTNU, he has developed and delivered training courses and university modules for students and professionals worldwide. In the last several years, he has focused heavily on technical consulting and course instruction, leading programs on:
- Python and data analytics for drilling engineers
- Machine learning applications for drilling optimization and risk prediction
- Managed Pressure Drilling (MPD), Controlled Mud Level (CML), and Managed Pressure Cementing (MPC)
- Real-time downhole issue detection and digital twins
- Plug & Abandonment (P&A) optimization, including innovative bismuth-alloy sealing solutions
Dr. Elahifar’s two decades of hands-on field experience, advanced degrees, and background working for major service companies, operators, and academia make him a highly skilled petroleum engineering consultant and trainer, uniquely positioned to bridge traditional drilling practice with the latest digital and AI-driven technologies.
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
