Data & Analytics
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents a competency-centered, data-driven approach implemented to strengthen control-room emergency-response capability through a cloud-hosted, scenario-based virtual plant simulator.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across teams and vendors. -
The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.
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The oil and gas industry is undergoing a significant shift with the advent of intelligent operations. This transformation is enabling upstream operations to move away from a reactive and manual mode of operation toward a more efficient, safe, and optimal state of operation.
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This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
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In this paper, a case study is described in which a software solution enabled prescriptive optimization of well delivery using a physics-informed machine-learning approach for predictive identification and characterization of well-construction risks.
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The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.
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The companies also agreed to collaborate on new AI models to unlock further insights from S&P Global Energy’s upstream data.
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As AI drives record heat loads in data centers, immersion liquid cooling is gaining momentum, and energy companies are lining up to support it.
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Artificial intelligence is prompting oil and gas companies to redefine roles, rethink trust, and rework operations, experts said during CERAWeek.
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The gap between machine learning research and effective deployment in the oil and gas industry is an alignment challenge between research questions and real decisions, between model design and operational constraints, and between innovation and the people expected to use it.
