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

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.

This paper discusses and demonstrates the limitations of quantitative risk assessment (QRA) with respect to the usefulness of the concept in managing day-to-day and emerging risks as well as the effect of change.

This study explores enhancing gas production through a novel combination of prestimulation using a coiled tubing unit and high-rate matrix acidizing.

Gas production faces several technical challenges, from extracting shale gas in complex geological settings such as tight formations and deepwater environments to processing sour gas with high hydrogen sulfide content. Although various technologies exist to mitigate these challenges, the dynamic nature of subsurface conditions and operational environments continues to…