Goto

Collaborating Authors

Upstream


Metal Geochemistry Meets Machine Learning in the North Atlantic

#artificialintelligence

… the findings from the photo mosaic maps will be extrapolated to the regions covered by the echo-sounder mapping by means of machine learning.”.


China is about to launch its most advanced mission to the moon yet

New Scientist

The robotic Chang'e 5 mission will land on the moon, gather rock samples and then blast off back to Earth - the first such mission in over 40 years. It could be a rehearsal for landing humans on the moon


Plate tectonics may have begun a billion years earlier than thought

New Scientist

Puzzle #86: How many yams do three shipwrecked sailors end up with? What will it take to get a covid-19 vaccine to the world? US Navy's huge uncrewed robot ship has journeyed through Panama Canal


Council Post: The Role Of AI In Carbon Reduction And Increased Efficiency For Energy

#artificialintelligence

AJ Abdallat is CEO of Beyond Limits, the leader in artificial intelligence and cognitive computing. Our world has reached a point where society recognizes the planet is under stress, with energy and technology sectors at the forefront of this reckoning. Microsoft, in association with PwC, revealed the urgency of the challenges currently facing our planet, reporting that 91% of people don't live in standard air quality-controlled areas, 60% of biodiversity has been lost since 1970, and greenhouse gases are at their highest levels in 3 million years. To get ahead of these challenges, we must reduce carbon footprints. AI will play a crucial role in supporting the energy industry's goals of achieving a more efficient, connected and sustainable future.


How will technology affect the future energy landscape?

#artificialintelligence

The oil and gas industry are dealing with massive disruption on several fronts from increasing oil price volatility due to Coronavirus and the failed OPEC deal. Combined with complexity of a rapidly changing energy sector where digital technologies, the drive for greener energy and demand for more consumer-centric services are putting shareholder returns at risk and reconfiguring policy mandates, industry players are forced to make a significant re-evaluation of energy value chains, assets and operations. The way we produce and consume oil & gas is shifting. Renewable energy sources, such as wind and solar, are growing exponentially and are expected to account for nearly 70% of global electricity production in 2050. Transport is being electrified, with 50% of all new cars sold globally forecasted to be electric by 2033.


Artificial Intelligence has learned to estimate oil viscosity

#artificialintelligence

A group of Skoltech scientists developed machine learning (ML) algorithms that can teach artificial intelligence (AI) to determine oil viscosity based on nuclear magnetic resonance (NMR) data. The new method can come in handy for the petroleum industry and other sectors, which have to rely on indirect measurements to characterize a substance. The research was published in the Energy and Fuels journal. An important parameter of oil and petrochemicals, viscosity has implications for production and processing, while helping to better understand and model the natural processes in the reservoir. Standard oil viscosity assessment and monitoring techniques are very time and money consuming and sometimes technically unfeasible.


Artificial Intelligence has learned to estimate oil viscosity

#artificialintelligence

A group of Skoltech scientists have developed machine learning (ML) algorithms that can teach artificial intelligence (AI) to determine oil viscosity based on nuclear magnetic resonance (NMR) data. The new method can come in handy for the petroleum industry and other sectors that have to rely on indirect measurements to characterize a substance. The research was published in the Energy and Fuels journal. An important parameter of oil and petrochemicals, viscosity has implications for production and processing, while helping to better understand and model the natural processes in the reservoir. Standard oil viscosity assessment and monitoring techniques are very time and money consuming and sometimes technically unfeasible.


Machine–Learning-Assisted Approach Analyzes Slug-Flow Root Cause

#artificialintelligence

The approach used machine-learning techniques to model and analyze historical production data to identify the drivers behind slug flow. The results …


Flow Assurance

#artificialintelligence

A novel approach was implemented that uses machine-learning techniques to model and analyze historical production data to find causes behind …


Sensors driven by machine learning sniff-out gas leaks fast – IAM Network

#artificialintelligence

A new study confirms the success of a natural-gas leak-detection tool pioneered by Los Alamos National Laboratory scientists that uses sensors and machine learning to locate leak points at oil and gas fields, promising new automatic, affordable sampling across vast natural gas infrastructure. "Our automated leak location system finds gas leaks fast, including small ones from failing infrastructure, and lowers cost as current methods to fix gas leaks are labor intensive, expensive and slow," said Manvendra Dubey, the lead Los Alamos National Laboratory scientist and coauthor of the new study. "Our sensors outperformed competing techniques in sensitivity to detecting methane and ethane. In addition, our neural network can be coupled to any sensor, which makes our tool very powerful and will enable market penetration." The Autonomous, Low-cost, Fast Leak Detection System (ALFaLDS) was developed to discover accidental releases of methane, a potent greenhouse gas, and won a 2019 R&D 100 award.