data analytics

This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.

This work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.

This paper presents a novel approach to predict reservoir porosity by conditioning seismic data, calibrating seismic impedance inversion, and tailoring rock-physics analysis.

This paper aims to assess the effectiveness of using advanced integrated production-data-analysis techniques for condensate-rich tight gas fields.

The paper presents the design and successful field deployment of the first closed-loop hydraulic fracturing program.

This paper reviews fracturing-program design, completion technology, real-time data collection, data integration, and lessons learned for the Pikka development on the North Slope of Alaska.

A discussion at the inaugural executive breakfast convened by the SPE Data Science and Engineering Analytics Technical Section, held alongside CERAWeek by S&P Global and powered by Black & Veatch, tackled the challenge of value creation from artificial intelligence in the energy industry.

This case study from Italian technology developer Sentris highlights the effectiveness of using sensors during pigging operations to optimize cleaning efficiency.

The companies also agreed to collaborate on new AI models to unlock further insights from S&P Global Energy’s upstream data.

The cloud platform provider said the initiative is designed to help energy companies manage and analyze large-scale operational data.