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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.

This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.

This paper outlines the importance of numerical rate transient analysis for dry gas wells, describing a simple, fully penetrating planar fracture model.

This paper presents an integrated view of three key areas of knowledge that typically are addressed individually—cybersecurity, process safety, and human factors—from the perspective of cybersecurity.

The paper details subsea system development for the Mero field and how operation of Mero’s early production system influenced the final configuration for all Mero projects.

This section lists with regret SPE members who recently passed away.

This work chronicles a series of operational activities in hydrate-blockage detection, modeling assessment, and safe and successful plug-remediation efforts in a Norwegian gas condensate subsea asset.

This paper describes a solution for downhole salt precipitation involving the installation of velocity strings.

The papers presented here highlight uncertainties and challenges incurred during the production of natural gas and the application of innovative technologies and advancement in digitalization capabilities to ensure sustainable production.

The integration of AI within the oil and gas industry is rapidly gaining momentum. With ongoing advancements, the future of formation evaluation promises transformative changes, leading to more-efficient and accurate reservoir characterization methodologies.