Why Choose this Training Course

This hands-on upstream AI training course is crafted to provide participants with in-depth knowledge and practical experience in key Deep Learning techniques, including Reinforcement Learning, Generative Adversarial Networks (GANs), and Large Language Models (LLMs). Blending theoretical insights with practical exercises based on real-world Oil & Gas datasets, the upstream AI training eural enables participants to develop the skills needed to confidently apply Deep Learning in solving everyday challenges within the Oil & Gas industry.

This upstream AI training offers a deep dive into Deep Learning and MLOps tailored for Oil & Gas professionals. Participants begin with foundational knowledge in machine learning, TensorFlow, and MLOps, learning to build and optimize feedforward neural networks. The course progresses into convolutional neural networks, object detection, and image segmentation, applied to tasks like pump monitoring and satellite image analysis. Time-series modeling, anomaly detection, and Bayesian networks are explored with applications in ESP maintenance and reservoir forecasting. Advanced natural language processing techniques such as text classification, summarization, and Named Entity Recognition are applied to industry texts, with a focus on building user-friendly interfaces. The final day of this upstream AI training covers cutting-edge techniques including reinforcement learning for well placement and generative AI—using GANs and LLMs—for creating synthetic data and extracting insights from unstructured documents. Through hands-on exercises and real-world datasets, participants gain practical skills to build, deploy, and manage robust AI solutions in upstream operations.

Who Should Attend

A reservoir engineer, geologist, petrophysicist, or production/drilling engineer with programming experience and foundational knowledge of data science and machine learning, looking to build a strong grasp of neural networks, deep learning, and machine learning operations (MLOps).

Key Learning Objectives

• Recognizing opportunities to apply Deep Learning techniques within your area of expertise
• Making informed choices when selecting appropriate machine learning approaches for specific challenges
• Understanding fundamental Deep Learning algorithms and how to implement them using TensorFlow and Keras
• Applying key machine learning methods to practical, real-world scenarios in the Oil & Gas industry
• Key Deep Learning algorithms will be explored in depth, supported by a variety of reusable code examples drawn from real-world Oil & Gas datasets.
• MLOps principles will also be covered, providing guidance on developing complete end-to-end machine learning solutions—from defining project scope and training models to deploying them and creating user-friendly graphical interfaces.

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