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This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.

This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.

Updates about global exploration and production activities and developments.

The upstream industry has viewed real-time completions as a long-term goal, but the technology is already in use.

The use of real-time wireless downhole pressure gauges proved a valuable alternative to workover operations in two onshore fields in Iraq.

A compelling triptych of recent research showcases the burgeoning capacity of machine learning to unlock substantial efficiencies and enhance decision-making across the exploration and production lifecycle.

This paper discusses the journey of the Bonga North project, highlighting challenges and resolution strategies in a constantly changing deepwater environment.

This paper addresses how close collaboration has enabled the development of a robust and cost-efficient solution for the Ormen Lange project by using carefully selected technology elements and an accelerated qualification process to mature them.

This paper describes a risk-based self-verification process conducted through a bespoke software application.

This study presents the development of a novel modeling tool designed to predict condensate emulsions, focusing on key factors causing emulsions such as pH, solid content, asphaltene concentration, droplet size, and organic acids.