AI/machine learning
Industry experts dissected the challenges in deploying artificial intelligence across the energy sector during a special session at SPE’s Annual Technical Conference and Exhibition.
AI is transforming the field of cybersecurity, offering new possibilities and challenges for both defenders and attackers, but AI also can introduce new vulnerabilities and risks and raise new ethical, legal, and social issues for cybersecurity.
This paper presents a case study highlighting the demonstration, refinement, and implementation of a machine-learning algorithm to optimize multiple electrical-submersible-pump wells in the Permian Basin.
-
This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value. -
The industry’s vast untapped data resources have the potential to change how our industry works—if we can piece it together.
-
This paper presents the processes of identifying production enhancement opportunities, as well as the methodology used to identify underperforming candidates and analyze well-integrity issues, in a brownfield offshore Malaysia.
-
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.
-
Collaboration and technology will help the industry meet its toughest challenges, experts said during the opening session at ATCE.
-
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.
-
The new contract extends a decadelong relationship and expands the use of AI and digital twins.
-
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.
-
You’ve heard of generative artificial intelligence, and odds are you’ve used it. But do you know how it works?
