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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The support vessel operator has invested in project-management software and in making connections using Starlink satellites. -
European oil and gas company Aker BP has agreed to a software-as-a-service collaboration with software firm Aize.
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The Norwegian data company has launched a 3D seismic survey in the Equatorial Margin area.
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The industry is balancing brains and bots as it squeezes out barrels of oil production.
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Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…
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SponsoredDive into TAQA’s digitalization and deep learning initiatives that are shaping the company's new approach to its Journey Management System. This innovative concept minimizes transportation-related risks in a period of rapid operations expansion.
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The company says the field has achieved a 25% production capacity increase through the user of advanced digital technology.
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This paper presents an integrated view of three key areas of knowledge that typically are addressed individually—cybersecurity, process safety, and human factors—from the perspective of cybersecurity.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
