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Deploying High Performance Computing Applications of AI/ML in the Energy Sector

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Course Credit: 0.1 CEU, 1 PDH

Recent advances in autonomous learning and artificial intelligence algorithms have allowed for breakthrough calculations in a number of fields – particularly excelling at efficient data-driven forward modeling and solving computationally intensive inverse modeling problems. We present an overview of state-of-the-art reinforcement learning strategies and GPU-accelerated computational frameworks. We will briefly cover applications of these methods to model problems including mathematical puzzles, mining optimization and path planning, followed by a deep dive into the industrial problem of well placement optimization. Here, reservoir simulations are coupled to a Soft-Actor-Critic reinforcement learning technique that predicts optimal locations and temporal steps to drill wells to maximize the Net Present Value of cumulative production. The optimal results obtained will be shared for a benchmark reservoir model. Subsequently, tangible breakthroughs enabled by deep reinforcement learning over conventional numerical strategies, as well as the future of HPC and AI in the energy sector will conclude the presentation.

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Course Chapters

  • 1Deploying High Performance Computing Applications of AI/ML in the Energy Sector - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.1 CEU credit1 PDH credit

Speakers

Vidyasagar
Nefeli Moridis