The digital learning ecosystem An efficient management approach to capability development, delivering smarter teams, improved productivity and better business outcome for the managers.
Bridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
The digital learning ecosystem Digitally and seamlessly connecting you, the learner, with pertinent learning objects and related technologies ensuring systematic, engaging and continued learning.
Industry and client recognition
Best Outreach Program Finalist: WorldOil Awards
Overall Customer Satisfaction Score
Training provider of the year: 2013, 14 and 15
Upstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableUpstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableBridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
The digital learning ecosystem Digitally and seamlessly connecting you, the learner, with pertinent learning objects and related technologies ensuring systematic, engaging and continued learning.
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Upstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableUpstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.
Engaging. Educational. EnjoyableBridging industry with academia An immersive and collaborative learning experience event, using OilSim simulator, providing highly relevant industry knowledge and soft skills.
Develop measurable skills and capabilities
This training covers a range of topics related to artificial intelligence and its applications in the energy industry.
On the first day, participants will be introduced to the basics of artificial intelligence, including its history, knowledge bases, and different types of machine learning. They will also learn about representation learning and deep learning. The second half of the day will focus on classic machine learning techniques, including supervised and unsupervised learning, as well as classification, regression and clustering.
On the second day, the training will cover various technologies and applications of AI in the energy industry, including computer vision, time series analysis, and natural language processing. Specifically, participants will learn about convolutional neural networks (CNNs) and their application to borehole data, as well as the use of Fourier Transform and other techniques for analyzing time series data. They will also learn about preprocessing and feature representation in natural language processing, and how to use word embedding and machine learning models for NLP tasks.
Introduction to artificial intelligence
Topics: A short history of artificial intelligence; Knowledge bases; Introduction to machine learning; Introduction to representation learning; Introduction to deep learning
Classic machine learning
Topics: supervised learning; unsupervised learning; classification; regression; clustering
Computer Vision - Technologies and Applications to the energy industry
Topics: CNN introduction (tasks – NN – Activation functions – Feed Forward – spatial convolutions – pooling – FC); borehole Data Examples; a scientific paper about inpainting
Time Series - Technologies and Applications to the energy industry
Topics: CNN introduction (tasks – NN – Activation functions – Feed Forward – spatial convolutions – pooling – FC); borehole Data Examples; a scientific paper about inpainting
Natural Language Processing - Technologies and Applications to the energy industry
Topics: Introduction to NLP; Textual data loading & preprocessing; Feature representation (statistical, syntactic, semantic); Word embedding; NLP ML model.
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