Applied Machine Learning and Data Science for Production Engineering
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Join us for an immersive workshop tailored for professionals in the oil and gas industry to understand how machine learning can be implemented in production engineering and production monitoring domain Through hands-on exercises, attendees will explore data manipulation, visualization, and analysis techniques essential for optimizing production workflows.
Workshop Objectives
Day 1: Regression Problems in Production Domain Data
In day one the following topics will be covered.
Introduction to python.
Python as a tool for ML.
Introduction to regression problems(prediction)
Types of regression.
Time Series Analysis.
Hands on project on Regression Analysis
Day 2: Classification on Production Data
In day 2 we will be taking a look at the possibilities and features of applying classifications algorithms to oil and gas data.
Introduction to Classification.
Introduction to Decision tree
Introduction to Labeled Data
Hands on Project on Classification
About the Presenter
Mr. Nashat Jumaah Omar - 12+Years of Experience In Oil & Gas Industry
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For PEA Members Only
Machine Learning for Drilling Engineering with Python
Drilling generates huge amounts of data — but most of it sits unused. This hands-on workshop shows you how to turn that data into better decisions using Python and machine learning.Over the sessions, you'll work with real drilling datasets to build models that predict problems before they happen — stuck pipe, ROP optimization, bit wear, formation changes, and more. No heavy math theory. You write code, run it on actual data, and see the results.By the end, you'll have working Python scripts you can adapt to your own field data, plus a clear understanding of where ML adds value in drilling and where it doesn't.Built for drilling engineers, not data scientists. If you can read a drilling report, you can follow this course.