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As reservoirs become more complex and economics tighter, the industry is shifting toward more-adaptive, data-informed, and targeted solutions. New research highlights innovative solutions that not only address technical limitations in existing designs but also enhance decision-making through digitalization and cross-disciplinary integration. The papers highlighted her…

This paper presents a case study highlighting the demonstration, refinement, and implementation of a machine-learning algorithm to optimize multiple electrical-submersible-pump wells in the Permian Basin.

This paper presents a closed-loop iterative well-by-well gas lift optimization workflow deployed to more than 1,300 operator wells in the Permian Basin.

This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.

Emissions management remains an active and crucial research area in the oil and gas industry. It is a broad research category spanning several different directions. The papers of the past year demonstrate the rich diversity of ideas and analytical techniques used for tackling different research questions in this space.

This paper focuses on developing a model that can be used in an automated, end-to-end flare-smoke detection, alert, and distribution-control solution that leverages existing flare closed-circuit television cameras at manufacturing facilities.

The dynamic realm of offshore drilling and completion continually sees the development of innovative technologies aimed at enhancing safety, efficiency, and cost-effectiveness. This edition highlights three papers showcasing transformative potential within the industry.

This section lists with regret SPE members who recently passed away.

This study recommends favoring the combustion of ammonia over hydrogen for the purpose of reducing CO₂ and nitrogen emissions.

The accelerating deployment of machine learning and automation is changing the artificial lift landscape. By embedding intelligence into the control loop, operators now can move from reactive decision-making to proactive, continuous optimization.