Management

This study identifies critical knowledge gaps in wellbore integrity and underscores areas that require further investigation, providing insights into how wellbores must evolve to meet the technical demands of the energy transition.

This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for CO₂ enhanced oil recovery and storage.

Only about one-third of Africa’s discovered hydrocarbon resources have reached commercialization.

This article is the sixth and final Q&A in 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.

Optimization of water-alternating-gas (WAG) processes is critical for maximizing both oil recovery and carbon-sequestration efficiency in CO2 enhanced oil recovery (EOR) projects. Conventional optimization using simulation models can be cumbersome because of the vast design space and high uncertainty. In this study, a deep-learning model, the temporal fusion transform…

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.

This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.

This study illustrates the new capabilities, tailored for CO₂ storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells.

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.