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The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

The world’s reliable engine of crude demand growth is stalling out, and its impact on the upstream market is already being felt.

The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.

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This paper focuses on the vital task of identifying bypassed oil and locating the remaining oil in mature fields, emphasizing the significance of these activities in sustaining efficient oilfield exploitation.

This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.

In this paper, a dynamic multiphase-flow simulator is used to evaluate the effectiveness and suitability of using a subsea capping stack to respond to a CO₂ well blowout.

In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.

Geothermal energy in the US has historically been concentrated in the West due to favorable geology, but emerging technologies have expanded the possibilities.

Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.