| 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
This program introduces oil and gas professionals to the practical applications of data science and machine learning, bridging domain knowledge with advanced computational techniques.
Description
The Data Science and Machine Learning Applications in Oil & Gas course is designed to empower professionals with the knowledge and skills to harness data for better decision-making across the petroleum value chain. From exploratory data analysis and feature engineering to supervised and unsupervised machine learning, participants will learn how to apply algorithms and workflows to solve real industry challenges. Practical use cases include production forecasting, EUR/well estimation, geomechanical clustering, sand prediction, and nodal analysis. By the end of the course, learners will be equipped to translate raw data into actionable insights, enhancing efficiency, safety, and profitability in oil and gas operations.
The oil and gas industry is undergoing a digital transformation, where data science and machine learning are becoming critical enablers of innovation. This course brings together the fundamentals of data analytics and petroleum engineering to illustrate how artificial intelligence can optimize exploration, development, and production. With tailored workflows, case studies, and Python-based demonstrations, participants will gain both conceptual understanding and applied skills that connect machine learning to petroleum applications.
• Understand digitalization frameworks and their relevance to oil and gas.
• Learn key data science workflows, from data cleaning to model deployment.
• Explore supervised learning algorithms including regression, KNN, SVM, and decision trees.
• Apply unsupervised learning techniques such as clustering to petroleum datasets.
• Gain an introduction to deep learning, neural networks, and gradient descent.
• Perform production data visualization and analysis using Python libraries.
• Connect machine learning theory to upstream oil and gas use cases.
• Evaluate models with industry-relevant performance metrics.
• Drive digital transformation initiatives with in-house expertise in data science.
• Enhance asset productivity through predictive analytics and forecasting models.
• Improve safety and reduce downtime by detecting anomalies in real time.
• Support smarter reservoir and production decisions through data integration.
• Build a competitive edge by aligning petroleum operations with emerging AI technologies.
• Acquire valuable cross-disciplinary skills at the intersection of petroleum engineering and data science.
• Strengthen your ability to forecast production, optimize designs, and detect anomalies.
• Gain confidence in using Python for data handling, visualization, and machine learning tasks.
• Build a skill set that enhances employability in digital oilfield and energy analytics roles.
• Become a forward-looking professional ready for the data-driven future of energy.
• Petroleum Engineers and Reservoir Engineers
• Data Scientists entering the oil and gas sector
• Production and Drilling Engineers seeking digital skills
• Researchers and Academics in petroleum analytics
• Managers and decision-makers focused on digital transformation
• Machine learning fundamentals for O&G
• Digital &digitalization framework.
• Fitment to O&G.
• Types of ml techniques.
• Ml workflows.
• Exploratory data analysis.
• Outlier/anomaly detection.
• Data cleaning/imputation.
• Feature engineering.
• Model building and evaluation
• supervised learning & related applications
• Introduction to supervised learning.
• Supervised ml algorithms such as linear/logistic regression,
• Knn, svm, decision trees.
• Evaluation metrics.
• Deep learning basics
• Artificial neural network
• Weights and biases, activation functions
• Forward and backward propagation
• Gradient descent
• Unsupervised learning & related applications
• Introduction to unsupervised learning.
• Unsupervised ml algorithms such as k-means algorithm, dbscan, Hierarchical clustering, math behind clustering.
• Use cases for unsupervised ml such as liquid loading prediction
• Geomechanical data clustering for hydraulic fracturing design.
• Use cases for supervised ml such as production forecasting, Eur/well estimation, sand Production prediction, frac intensity Classification, nodal analysis, etc
• Introduction to python
• Brief summary of python libraries (numpy, pandas, matplotlib)
• Basis operations, creating string lists, dictionary, tuple, data import and visualization.
• Production data visualization using python.
• Structuring machine learning projects
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.
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