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Data Science and Engineering Analytics
University of Texas at Austin
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SPE/PIVOT23 AInnovation Competition
The Gulf Coast Section Data Analytics Study Group invites teams from around the globe to participate in an exciting virtual competition aimed at showcasing the potential of AI-assisted workflows in the Geothermal lifecycle. Open to all AI enthusiasts who want to explore opportunities in the geothermal world, the competition provides an opportunity for students and young professionals to utilize advances of AI to excel in a new energy domain.
The contest is set to run from 19 July - 31 August, requiring participants to develop a LLM-based workflow. Participants will submit a video describing their target problem, the selected information and datasets to apply the LLM-Model, and a visualization of how the coded workflow works in one geothermal example.
SPE Energy Stream
SPE Energy Stream is your go-to for watching thought leaders, subject-matter experts, and leading companies share their perspectives and technical solutions.
Data Science and Digital Engineering is SPE's newest online publication, presenting the evolving landscape of data management and use in the industry with original content from SPE and content curated from other relevant publications.
Data-Driven Reservoir Modeling
Shahab D. Mohaghegh
Data-Driven Reservoir Modeling introduces new technology and protocols (intelligent systems) that teach the reader how to apply data analytics to solve real-world, reservoir engineering problems. The book describes how to utilize machine-learning-based algorithmic protocols to reduce large quantities of difficult-to-understand data down to actionable, tractable quantities. Through data manipulation via artificial intelligence, the user learns how to exploit imprecision and uncertainty to achieve tractable, robust, low-cost, effective, actionable solutions to challenges facing upstream technologists in the petroleum industry.
Watch a video of Shahab D. Mohaghegh describing his new book.