Data & Analytics
This paper presents a smart safety monitoring system to prevent accidents in environments with moving machinery at use on various global rigs.
The Energy and AI Observatory aims to use up-to-date information on energy demand from data centers to determine how artificial intelligence is optimizing the energy sector.
The company is making available its data on ocean and weather conditions in an effort to boost transparency and innovation.
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This third installment of the Digital Data Acquisition Technology Focus will focus on computer vision for improved data acquisition. Computer vision is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. -
SponsoredEach well drilled, stimulated, and completed represents a significant investment in time, resources, and expenses. From artificial lift system design to maintenance scheduling, maximize your investment by ensuring optimal flow and production throughout the life cycle.
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Integrating physics and machine learning combines the best of the two worlds, resulting in higher accuracy, better scalability, and cost efficiency.
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This paper describes the current challenges faced by energy companies, the implications of observable industry trends, the characteristics that potential cybersecurity solutions must meet, and how artificial intelligence (AI) and machine learning (ML) can meet these requirements.
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Totally automated drilling today looks like a robot doing all the heavy lifting on a drilling floor. By 2025, there may no longer be anything surprising about it.
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The machine-learning techniques applied in this study aim to deliver a fouling-prediction model based on both simulation and real-time field data.
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Gazprom Neft Courts Middle East NOCs With Digital Technologies as it Seeks Bigger Role in the RegionA delegation from the Moscow-headquartered oil company recently visited Dubai with a slew of advanced technologies to see if anyone was interested in making a deal.
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A supervised machine-learning algorithm is developed to classify drilling parameters that increase rate of penetration and bit endurance for use in unconventional fields in Australia.
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This paper describes how severe rig limitations were overcome through an optimization plan in which an optimal bottomhole assembly was designed and drilling practices were customized.
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Schlumberger and Russia’s Gazprom Neft have agreed to jointly develop software and promote other upstream services to be commercialized across Russia and internationally.
