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This study illustrates the new capabilities, tailored for carbon-dioxide storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells.

Operators are turning to new gas-lift and nanoparticle-fluid technologies to drive up production rates.

This paper examines how real-time monitoring can improve both incident prevention and emergency response in the oil and gas industry. Drawing from real-world examples and case studies, it provides practical strategies for implementing this technology effectively.

This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for carbon-dioxide enhanced oil recovery and storage.

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This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.

This article is the sixth and final Q&A in a series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this final edition, Matthew T. Balhoff, SPE, of The University of Texas at Austin shares his views on the future of upstream education.

This paper introduces a system that leverages sophisticated algorithms and user-friendly interfaces to tackle the challenge of developing complex, compartmentalized reservoirs effectively.

This paper presents a novel application of artificial intelligence in computer vision for automating blowout-preventer pressure-chart-data extraction, demonstrating significant efficiency gains and a high return on investment.

The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.