CNOOC to declare force majeure on some prompt LNG deliveries

CNOOC is China’s top importer of LNG. (Reuters)
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Updated 07 February 2020

CNOOC to declare force majeure on some prompt LNG deliveries

SINGAPORE: China National Offshore Oil Corp. (CNOOC) has declared force majeure on prompt liquefied natural gas (LNG) deliveries from at least three suppliers, two sources told Reuters on Thursday.

This is the first known case of a Chinese company declaring an inability to fulfil a contract amid the economic impact of a fast-spreading coronavirus that has killed more than 560 people.

China is the world’s second-largest importer of LNG, and its spot purchases of the super-chilled fuel and other energy products have ground almost to a halt, but this is the first official declaration of force majeure by a Chinese company.

CNOOC is China’s top importer of LNG and operates nearly half of the country’s total receiving capacity.

LNG traders are scrambling to divert or find new outlets for the cargoes meant for China, several of them told Reuters, driving spot Asian prices to record lows.

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China is the world’s second-largest importer of liquefied natural gas.

“China was the place we sent cargoes to if demand was weak elsewhere in Asia, but now people are trying to find alternative locations,” one of the traders said.

The force majeure notice covers LNG purchase by CNOOC during the periods of February and March, said one of the two sources.

The two sources, both with knowledge of CNOOC’s move, declined to be named due to the sensitivity of the matter.

No further details were immediately available, and calls to a CNOOC spokeswoman went unanswered.

A Chinese international trade promotion agency said last week it would offer force majeure certificates to companies struggling with the impact of the coronavirus epidemic on their business to give to their overseas partners.

The top suppliers of LNG to CNOOC include Royal Dutch Shell, Total, Woodside Petroleum and Qatargas, industry sources said.

China’s own PetroChina and Sinopec also supply CNOOC’s terminals during the winter heating season from mid-November to mid-March, under a government-mandated so-called “inter-connected” supply scheme, said a separate industry official with knowledge of the matter.

It was not clear which of all these companies were issued a force majeure notice.

A Woodside representative said the Australian energy company was closely monitoring the
situation. The other companies were not immediately available
for comment.

CNOOC had been offering to resell LNG cargoes even before the outbreak of the coronavirus, with Chinese buyers grappling with high inventory amid weak demand due to a slowing economy and a milder-than-usual winter. 

Related


Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.