Data & Analytics
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
Novel lift systems, AI-driven automation, and real-time engineering models are helping operators extract more value from mature wells.
This case study from Tomax outlines the use of artificial intelligence to analyze high-fidelity data of the full drilling process.
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The companies plan to develop new artificial-intelligence-powered processes and workflows to optimize oil and gas production. -
Routine status reporting often presents a challenge because of its intimidating and time-consuming nature for both employees and supervisors. With large language models, a system was developed to generate coherent artificial-intelligence-driven reports. The goal is to enhance the understanding of overall insights and reduce the time required for individual report read…
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We’re thrilled to announce the launch of the 2025 SPE/JPT Drilling and Hydraulic Fracturing Technology Review. This exclusive, official publication will be distributed at three major SPE industry events.
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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.
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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.
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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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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.
