AI/machine learning
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
From AI-enabled walking rigs to subsea drones and autonomous inspection robots, robotics is rapidly moving from pilot projects to field deployment. The technology promises not only greater efficiency but also a fundamental shift in how the industry approaches safety and asset management.
-
Applications include identifying new green catalysts to enable the conversion of waste products to useful matter, green hydrogen generation, CO2 utilization, and the development of fuel cells. Novel catalysts also could be used to replace expensive and rare materials such as iridium, the metal used to generate green hydrogen and CO2 reduction products. -
This selection of papers will familiarize facilities engineers with a variety of topics so they are more prepared for what 2022 may bring.
-
The authors present a review of the capabilities of fog computing and its potential in the petroleum industry.
-
The machine-learning techniques applied aim to deliver a prediction model based on both simulation and real-time field data. The model tracks and monitors system key performance indicators.
-
SponsoredAI can improve your compliance efforts, saving you time and money. Learn how.
-
The authors present an artificial-intelligence and machine-learning technology to obtain a high-level, comprehensive view of all equipment in a facility to detect and map corrosion.
-
This paper provides an alternative solution to identifying, classifying, and vertically distributing fractures and a lateral distribution method for reservoir modeling.
-
The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods.
-
This paper describes a method to determine rig state from camera footage using machine-learning-based vision-analytics approaches.
-
This paper describes the current challenges faced by energy companies, the implications of observable industry trends, the characteristics that potential cybersecurity solutions must meet, and how artificial intelligence (AI) and machine learning (ML) can meet these requirements.
