data science

The companies also agreed to collaborate on new AI models to unlock further insights from S&P Global Energy’s upstream data.

As AI drives record heat loads in data centers, immersion liquid cooling is gaining momentum, and energy companies are lining up to support it.

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.

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, forward simulation is executed by a commercial reservoir simulator while external code is developed for backward calculations.

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.

SLB's and Baker Hughes' partnerships with NVIDIA and Google Cloud, respectively, will develop advanced AI-enabled power optimization and sustainability solutions for the global data center sector.

This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.

The objective of this study is to field test a non-nuclear multiphase flowmeter and assess its performance under challenging operating conditions.

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