Testing page for app

The Data Science and Engineering Analytics Technical Section has been selected to receive the 2025 Presidential Award for Outstanding Technical Section, and the Hydrogen and Sustainable Development technical sections have been awarded the 2025 Technical Section Excellence Award.

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.

This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.

This paper reviews best practices and learnings from floating-production-unit transportation and installation, hookup, and commissioning and discusses subsea flowlines, export pipelines, and subsea-equipment installations.

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