The gap between machine learning research and effective deployment in the oil and gas industry is an alignment challenge between research questions and real decisions, between model design and operational constraints, and between innovation and the people expected to use it.

Technology and partnerships remain important, while phased approaches may supplant lengthy appraisal programs, experts said during CERAWeek.

CERAWeek panelists see AI as a way to leverage data and people in interpreting data for exploration, but a cultural shift at companies may still be needed.

The newest recipient of the title SPE Legend of Hydraulic Fracturing talks about his career, the evolution of fracture stimulation, the development of increasingly useful simulators, and the future of the oil and gas industry. The honor was given at the 2026 SPE Hydraulic Fracturing Technology Conference and Exhibition.

The authors propose a deep-learning-based approach enabling near-real-time CO2-plume visualization and rapid data assimilation incorporating multiple geological realizations for predicting future CO2 plume evolution and area-of-review determination.

In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.

This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.

This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.

Data centers could add up to 6 Bcf/D of US gas demand by 2030, creating a new opportunity for producers and reshaping how oil companies think about electricity supply.

This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.