Data & Analytics
Switching from continuous circulation to cyclic huff-‘n’-puff operation in enhanced geothermal systems can significantly delay thermal breakthrough, sustain higher production temperatures, and improve long-term economic performance.
The two companies say they plan to work together to use agentic AI to increase the capabilities of technical experts.
This article is the first in a Q&A series from the SPE Methane Emissions Management Technical Section (MEMTS) on methane intelligence and how oil and gas teams translate emissions data into credible decisions and measurable reductions.
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This paper presents a robot integrated with a microcontroller that provides multiple functions to help with data logging, analysis, and reporting to identify hazards and improve safety protocols. -
Aurora Innovation and Detmar Logistics have inked a deal for 30 autonomous trucks that will begin hauling sand in the region next year.
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Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
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SponsoredIn oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
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Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.
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This study presents a novel hybrid approach to enhance fraud detection in scanned financial documents.
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This paper presents an approach to management and interpretation of pipeline-integrity data, ensuring integrity, safety, and reliability of the operator’s critical pipelines.
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This paper describes the development of a system for comprehensive mapping and asset registration using a digital-twin approach.
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This paper describes a decision-support system that integrates field data, system specifications, and simulation tools to quantify system performance, forecast operational challenges, and evaluate the effect of system modifications in water management.
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This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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