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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.

This paper assesses the technical feasibility of geological carbon storage in the operator’s Brazilian brownfields, focusing on mature oil fields and associated saline aquifers.

These papers exemplify value creation at each stage of hydrocarbon maturation and should prompt practitioners to don their thinking caps.

Are we in an AI bubble? The question may seem academic to petroleum engineers who are already capitalizing on the momentum of digitalization across the industry, yet any engineer, regardless of their career stage, could be forgiven for feeling overwhelmed by the sheer scope of specialized skills now demanded in this rapidly evolving digital landscape.

This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.

This paper details a data-driven methodology applied in Indonesia to enhance flare-emission visibility and enable targeted reduction strategies by integrating real-time process data with engineering models.