Data Science and Machine Learning Applications in Oil & Gas
| Code | Duration | Currency | Fee Per Person |
|---|---|---|---|
| EL-DS-ML-PEA |
10 Hours
|
USD
|
500
|
This is a self-paced, on-demand e-learning course. Upon enrollment, all course videos and materials will be delivered to your email within 12 hours. A certificate will be issued upon successful completion of the required quizzes and assignments.
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Data Science and Machine Learning Applications in Oil & Gas
A flexible, instructor-led e-learning program designed to equip oil and gas professionals with essential skills in data science, Python programming, and machine learning applications. Learn how digital tools can drive smarter decisions, predictive analytics, and automation across the energy value chain.
Description
This training course introduces participants to the fundamentals of data science and machine learning applied specifically to upstream oil and gas operations. Through interactive lessons, real case studies, and coding exercises, learners gain hands-on experience in solving complex reservoir, production, and geological problems using Python and analytical techniques. Delivered in self-paced video modules, the course bridges engineering expertise with modern digital technologies for the energy industry.
Gain awareness and knowledge of the role of data science in the career path of an oil and gas professional.
Understand the mathematical principles behind machine learning algorithms and how they solve industry problems.
Learn the application of machine learning to real industrial datasets.
Acquire insights into emerging job opportunities for digitally skilled oil and gas professionals.
The course is suited for technical professionals, graduates, and students interested in the applications of data science and machine learning within oil and gas technology.
Prerequisites: None (basic understanding of matrix algebra and calculus is good to have).
Module 1: Machine Learning Fundamentals for Oil & Gas
Digitalization framework and ML fitment to oil & gas
ML workflows, exploratory data analysis
Outlier detection, data cleaning, and feature engineering
Model building and evaluation
Module 2: Supervised Learning Applications
Linear & Logistic Regression, KNN, SVM, Decision Trees
Deep learning basics: Neural Networks, Gradient Descent
Use cases: Production forecasting, EUR estimation, sand production prediction, frac intensity classification, and nodal analysis
Module 3: Unsupervised Learning Applications
Algorithms: K-Means, DBSCAN, Hierarchical Clustering
Use cases: Liquid loading prediction, geomechanical clustering for hydraulic fracturing design
Module 4: Introduction to Python
Python libraries: NumPy, Pandas, Matplotlib
Data creation, import, and visualization
Production data visualization using Python
Module 5: Structuring Machine Learning Projects (subject to time availability)
Overview of end-to-end ML project setup and execution
On successful completion of this training course, PEA Certificate will be awarded to the delegates
This course has been meticulously developed by a seasoned PEA expert renowned in the oil and gas industry. With extensive hands-on experience and a proven track record in delivering innovative solutions, our trainer brings a wealth of technical expertise, deep industry insight, and a commitment to excellence. Learners can trust that they are gaining knowledge from a leading authority whose dedication to professional development ensures you receive only the highest-quality training to elevate your skills and career prospects.
