AI/machine learning
As carbon capture scales up worldwide, the real challenge lies deep underground—where smart reservoir management determines whether CO₂ stays put for good.
This article is the third in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Zikri Bayraktar, a senior machine learning engineer with SLB’s Software Technology and Innovation Center, discusses the expanding use of artificial intelligence in the upstream sector.
This article presents a results-driven case study from an ongoing collaboration between a midstream oil and gas company and Neuralix Inc.
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This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting. -
This paper describes a new application that leverages advanced machine-learning techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads.
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Collaboration and technology will help the industry meet its toughest challenges, experts said during the opening session at ATCE.
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Accuracy, complexity, costs, and skills availability may make it difficult to get the most out of digital twins and even potentially misrepresent or miss actual changes in the status of systems or facilities.
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The new contract extends a decadelong relationship and expands the use of AI and digital twins.
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This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir.
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You’ve heard of generative artificial intelligence, and odds are you’ve used it. But do you know how it works?
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The firm’s latest report, Leading a Data-Driven Transition, presents the results of its annual survey of nearly 1,300 senior professionals and divides the respondents into two groups, which it calls “digital leaders” and “digital laggards.”
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The industry is balancing brains and bots as it squeezes out barrels of oil production.
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Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…
