data science

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.

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

This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.

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

This paper describes the implementation of a one-way wave equation in slowness domain outputting gathers up to Nyquist frequency, which can be used to generate high-resolution seismic sections and amplitude-vs.-offset studies.