AI/machine learning
Major increases in hydrocarbon production require both incremental and revolutionary technologies, industry leaders said during the SPE Hydraulic Fracturing Technology Conference.
This paper presents an automated workflow deployed for scheduling and validating steady-state production-well tests across more than 2,300 wells in the Permian Basin.
This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.
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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. -
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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New and evolving artificial lift technology is helping operators improve production rates.
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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 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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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 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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Collaboration and technology will help the industry meet its toughest challenges, experts said during the opening session at ATCE.
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Accuracy, complexity, costs, and skills availability may make it difficult to get the most out of digital twins and even potentially misrepresent or miss actual changes in the status of systems or facilities.
