artificial intelligence

Aramco’s latest MOUs focus on driving innovation and growth across oil, gas, and downstream sectors.

This article presents a comparative study evaluating four machine-learning approaches, including three deep-learning methods, for forecasting gas and condensate production over a 5-year horizon.

After a successful trial phase of ENERGYai, AIQ has been tapped to roll out the technology across ADNOC’s upstream operations.

Weatherford and AIQ say they aim to enable the energy sector to unlock efficiencies, boost productivity, and reduce operational costs by combining their strengths.

The full potential of data can only be realized when it is viewed not in isolation but as part of the dynamic triad of hydrocarbons, the data, and the people who interpret it and act on it.

The 2025 Global Energy Talent Index survey found most energy professionals got a pay bump last year, but hiring managers face new challenges.

This comprehensive review of stuck pipe prediction methods focuses on data frequency, approach to variable selection, types of predictive models, interpretability, and performance assessment with the aim of providing improved guidelines for prediction that can be extended to other drilling abnormalities, such as lost circulation and drilling dysfunctions.

Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.

Whether it’s reviving inactive gas-condensate wells or identifying overlooked reserves in brownfields, operators are making the most of older wells and fields.

New case studies highlight how artificial intelligence, advanced hardware, and innovative business models are enabling success in drilling automation.