AI/machine learning
From AI-enabled walking rigs to subsea drones and autonomous inspection robots, robotics is rapidly moving from pilot projects to field deployment. The technology promises not only greater efficiency but also a fundamental shift in how the industry approaches safety and asset management.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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Physics-based simulations plus machine-learning exercises are yielding a more comprehensive look at production volumes from unconventional assets. -
Vaarst, a spinoff from subsea robotic and hydrographic survey company Rovco, wants to accelerate advancement in ocean robotics by providing access to artificial-intelligence technology to marine and subsea providers for autonomous robotic work.
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Petrolern has received a $1.15-million grant from the US Department of Energy to develop and commercialize its technology that models in-situ stresses by using available data.
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Anomalies in heart function can be diagnosed in real time by measuring an electrical signal. Petroleum engineers have adapted the concept to diagnose anomalous drilling conditions in real time using a shock signature recorded downhole.
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For oil and gas companies to remain in existence in the second half of the 21st century, they must find ways to dramatically reduce, if not eliminate, their output of carbon dioxide and other greenhouse gases. Artificial intelligence technology could provide one tool to help the energy industry accomplish that staggeringly difficult goal.
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The new DeeperSense project, an international consortium led by the German Research Center for Artificial Intelligence, is working on technologies that combine the strengths of visual and acoustic sensors with the help of artificial intelligence. The aim is to significantly improve the perception of robotic underwater vehicles.
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Technology is advancing, and applications are growing, but scaling faces technological and human challenges.
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Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, and sports. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface en…
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Artificial intelligence is opening new ways to analyze data from microseismic events that occur during hydraulic fracturing. One researcher at Moscow’s Skolkovo Institute of Science and Technology is building a convolutional neural network to get a subsurface view of permeability after fracturing.
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Wintershall Dea set out to demystify digital for engineers with an informal network of staff experts who help fill the gaps in this new way of doing things and have a focus on maximizing the return on problems previously solved.
