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Oil & Gas Training
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    Oil and Gas Training Courses SLB NEXT

    Intro to artificial intelligence for the energy industry

    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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