data science

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.

The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection.

This paper presents various functionalities and benefits of a monitoring tool developed for and used with all critical flowmeters in the operator’s production system.