Data & Analytics
Canada’s development of large-scale digital drilling core libraries and open geoscience data platforms follows approaches pioneered in Australia, particularly Western Australia, through decades of government investment in geoscience data collection and preservation.
After years of market shocks, technological breakthroughs, and rising uncertainty, ATCE 2026 will provide new insights on how industry leaders and technical experts are preparing for the next era of the upstream business.
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
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Baker Hughes, a GE company, (BHGE) and C3.ai announced a joint venture agreement that brings together BHGE’s fullstream oil and gas expertise with C3.ai’s unique artificial-intelligence software suite to deliver digital transformation technologies and drive productivity for the oil and gas industry. -
Funded by a €10.6 million grant from the European Union’s Horizon 2020 program, the ambitious project will, for the first time, undertake an oceanwide approach to understanding the factors that control the distribution, stability, and vulnerability of deep-sea ecosystems.
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Marathon Oil says its shale fields are producing more oil and gas with less hands-on work from company personnel thanks to a growing arsenal of digital technologies and workflows.
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Drones are becoming an important tool for energy companies looking to improve on-site safety and operational efficiencies, and the industry is looking for the best way to maximize their value. What are some the challenges in getting these programs off the ground?
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The oil and gas industry already lives on the edge when it comes to the remote and often inhospitable geographic locations that it operates in, but now it is moving its computing to the edge to gain valuable business insights that can increase operational efficiency and profitability.
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The Independent Project Analysis recently reached out to its clients to understand why digitalization tools are so burdensome for projects organizations to implement.
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Hamiltonian neural networks draw inspiration from Hamiltonian mechanics, a branch of physics concerned with conservation laws and invariances. By construction, these models learn conservation laws from data, revealing major advantages over regular neural networks on a variety of physics problems.
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Schlumberger introduced the GAIA digital exploration platform, which it says enables exploration teams to rapidly discover and access basin-scale data and manage their exploration opportunities.
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Researchers at the University of Massachusetts, Amherst, performed a life-cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 lbm of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car.
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Random Forest and Neural Network are the two widely used machine-learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
