This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.

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.

Even as output hits record highs, a growing recognition of the Permian’s maturity is opening the door for new technologies to improve performance.

This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of CO₂ EOR and storage operations quickly in mature oil fields.

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.

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…

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.