data science

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.

Organic data governance emphasizes flexibility, stakeholder engagement, and a culture that values data integrity.

Shell’s combination of digital worker technologies enables collaborative troubleshooting and inspections while reducing travel and boosting efficiency.

In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.

This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.

The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.

This paper introduces a novel optimization framework to address CO2 injection strategies under geomechanical risks using a Fourier neural operator-based deep-learning model.

The energy-focused LLM project by Aramco Americas, SPE, and i2k Connect has entered the testing phase and is on track for licensing to operators later this year.

A roundtable discussion during CERAWeek pointed to the necessity of a mindset shift for the oil and gas industry to tap into AI’s true potential.

The acquisition of the imaging company gives Kraken offices in Colorado and Texas.