Data & Analytics
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
Terra Drone will support Aramco's operations and Saudi Arabia's technological and economic progress.
Organic data governance emphasizes flexibility, stakeholder engagement, and a culture that values data integrity.
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The SPE Europe Energy GeoHackathon aims at educating and disseminating knowledge to all the participants on how data science applications can support geothermal energy developments and drive the energy transition. Boot camps begin 21 October. -
This paper aims to emphasize the importance of decision-making based on quantitative monitoring outputs, from both a business perspective and an ecosystem-service perspective, in future offshore projects.
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Aker BP plans to use a software platform from TGS as a process data server designed to improve field operations.
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Experts at SPE’s Annual Technical Conference and Exhibition say that despite AI’s great potential, it’s important to be realistic about AI’s capabilities and to remember that successful projects solve specific business problems.
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The industry’s vast untapped data resources have the potential to change how our industry works—if we can piece it together.
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This paper presents the processes of identifying production enhancement opportunities, as well as the methodology used to identify underperforming candidates and analyze well-integrity issues, in a brownfield offshore Malaysia.
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This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value.
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This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.
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This paper describes a new application that leverages advanced machine-learning techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads.
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New and evolving artificial lift technology is helping operators improve production rates.
