• Class and Course

    Machine Learning for Sustainability in Oil & Gas

    The training entitled "Machine Learning for Sustainability in Oil & Gas" is an in-depth, meticulously structured five-day course aimed at empowering industry professionals with the expertise to harness machine learning (ML) in fostering sustainable practices within the oil and gas sector.

    Throughout the duration of the program, attendees will embark on a thorough exploration of machine learning from its historical roots to its cutting-edge applications. The curriculum is strategically segmented into daily themes, each building upon the last to form a cohesive and comprehensive learning journey.

    Introduction to Machine Learning and Sustainability

    • Introduction to the history of ML and its importance for sustainability in the oil and gas sector, emphasizing the role of ML in energy transition.
    • Study of oil markets with Gapminder tools to understand sustainability challenges and opportunities.
    • Clarifying the distinctions and connections between Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL), and discussing how intelligent digital workplaces can contribute to energy transition.
    • Initial Python setup, laying the groundwork for hands-on programming throughout the course.

    Data Handling and Carbon Emission Analysis

    • Introduction to Python basics, including data structures like lists and tuples.
    • Use of NumPy for analyzing carbon emission factors, a critical factor in measuring environmental impact.
    • Pandas library for handling climate datasets, demonstrating its utility in managing and analyzing environmental data.
    • Discussion on location intelligence and greener practices for oil and gas facilities using Python and its libraries.

    Visualization and AI Workflows

    • Visualization techniques using matplotlib/seaborn on a dataset from a multisensor device in an Italian city, for practical insights into pollution measurement.
    • Exploration of AI workflows, comparing supervised and unsupervised learning approaches.
    • Applying principal component analysis to a methane emissions dataset to identify key factors contributing to emissions.
    • Optional additional practice

    Regression Analysis and Classification in AI

    • Understanding regression using climate models to predict future trends and patterns.
    • Application of clustering on datasets from carbon capture and sequestration sites, an important technique for reducing carbon footprint.
    • A demonstration of classification using a chatbot to recognize carbon scope and category, providing a real-world application of NLP.
    • Optional extra learning activities

    Deep Learning and Professional Responsibilities

    • Application of deep learning in NLP, time series, and computer vision, focusing on sustainability.
    • Exploration of advancements in deep learning architectures, enhancing the ability to process complex sustainability-related data.
    • Case study discussion on Schlumberger Limited's (SLB) energy transition initiatives, providing context for ML's role in industry projects.
    • Closing discussions on responsible and explainable AI, intellectual property, legal considerations, and addressing any lingering doubts or questions.

     Oil and Gas Sustainability practioners, Energy Transition Professionals, Carbon and Emissions team Managers and technical people. Sustainability and Energy Transition enthusiasts.

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