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India’s state oil company is accepting proposals from potential technical service partners until 15 September for EOR projects in the Arabian Sea.

The company has agreed using its innovations in automation in an effort to derisk ultradeep offshore drilling for the Brazilian national oil company.

The company says the field has achieved a 25% production capacity increase through the user of advanced digital technology.

The SPE IOR-EOR Terminology Review Committee has opened a period for public comments on a draft technical report.

The Management Technical Section has been selected to receive the 2024 Presidential Award for Outstanding Technical Section, and the Data Science and Engineering Analytics and Hydraulic Fracturing Technical Sections have been awarded the Technical Section Excellence Award for 2024.

This paper presents circular environmental initiatives at Abu Ali Island in the Eastern Province of Saudi Arabia that led to sustainable and systematic decarbonization, ecosystem, and biodiversity programs.

This paper describes a data-driven approach for liquid-loading detection and prediction that harnesses high-frequency gas-rate and tubinghead-pressure measurements to identify the onset of liquid loading and correct critical rates computed by empirical methods.

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.

This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.

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.