digital transformation

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 paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.

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.

The authors describe a study on key technologies for intelligent risk monitoring of workover operations.

The authors write that by replacing outdated, labor-intensive processes with an integrated, cloud-based platform, companies can streamline planning, improve accuracy, and foster better coordination across teams and vendors.

The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.

Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.

Even as industry faces policy and tariff uncertainty, companies view spending on digital transformation as a driver of efficiency.

SPE and The Open Group have signed a memorandum of understanding to advance collaboration and innovation in the global energy industry.

This paper proposes how the strengths of cloud computing can become key enablers for oil and gas organizations in helping them enhance their overall security posture and manage risks within operational-technology environments.