Data & Analytics
Shell has demonstrated drone-in-a-box operations from its Mars floating production unit, becoming the first operator in the region to secure FAA approval for self-approved offshore beyond-visual-line-of-sight flights. JPT Senior Technology Editor Jennifer Pallanich visited Shell Technology Center Houston to see the system in action.
Panelists at the SPE Subsea Well Intervention Symposium discussed where AI is delivering value today, where risks remain, and how engineers can best determine its usefulness.
As digital technologies become commonplace, industry leaders say core engineering knowledge remains essential for making informed decisions and avoiding costly mistakes.
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The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods. -
This paper describes a method to determine rig state from camera footage using machine-learning-based vision-analytics approaches.
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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.
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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.
