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Analysts Flag Key Price Determinant for Crude Oil

In a report sent to Rigzone by Standard Chartered Bank Commodities Research Head Paul Horsnell late Tuesday, analysts at the bank, including Horsnell, noted that, in their view, the key price determinant for crude oil over the past week was the U.S. tariff announcement. “The downwards vortex set in play by the announcement took Brent […]

In a report sent to Rigzone by Standard Chartered Bank Commodities Research Head Paul Horsnell late Tuesday, analysts at the bank, including Horsnell, noted that, in their view, the key price determinant for crude oil over the past week was the U.S. tariff announcement.

“The downwards vortex set in play by the announcement took Brent prices lower by more than $12 per barrel over just four trading days from the 2 April intra-day high ($74.95 per barrel) to the 7 April intra-day low of $62.51 per barrel,” the analysts said in the report.

“Brent 30-trading day realized annualized volatility rose by 15.2 percentage points week on week to 35.7 percent at settlement on 7 April, with 10-trading day volatility rising 34 percentage points to 47.3 percent over the same period,” they added.

“In all, we saw a sharp fall at the front of the curve accompanied by large trading ranges on the way down,” they continued.

The Standard Chartered Bank analysts highlighted in the report that they have been asked by clients whether the scale of the fall was justified. They noted in the report that, in their view, “given market positioning and normal market dynamics, the fall was fully justified and could have gone significantly further.”

“The U.S. tariff announcement was a severe shock to a market that was predominantly of the view that tariff rates would be limited, well-thought out, likely delayed and rapidly negotiated away, and would still lie within the ambit of the normal staid range of international trade diplomacy,” they added.

“What was announced did not conform with the dominant oil market consensus view. Instead, the market immediately started to price in a significant reduction in expectations of global GDP, with U.S. recessionary risk in particular marked sharply higher,” they continued.

The analysts stated in the report that oil markets are particularly sensitive to sudden discounting of global GDP expectations. They pointed out that this is “a theme that showed itself strongly from the oil price watersheds in 2008 and 2020 and which has now re-emerged”.

“After the initial push lower due to the sudden shift in GDP growth expectations, a series of other factors accelerated the decline,” the Standard Chartered Bank analysts said in the report.

“A general risk-off environment led to positions being reduced by money-managers in particular, who had added 172 million barrels to their crude oil longs in the three weeks before the tariff announcement, including 66 million barrels in the week prior to the announcement,” they added.

The analysts noted in the report that they think almost all of the new long positions were closed out very rapidly.

“A further acceleration in the decline came when prices fell into the zone where potential gamma effects are at their greatest,” they said.

“We showed this zone in an earlier report … noting that the market had survived some gamma effects in early March when the 2025 WTI futures strip fell below $65 per barrel and started biting into the distribution of producer put options,” they added.

“This time there was to be no reprieve, with the 2025 WTI strip falling below $60 per barrel; the sudden price fall, together with the sharp increase in volatility, meant that banks had to sell large volumes of crude futures in order to manage the positions they held as a result of providing hedges to producers,” they continued.

“The gamma effects become a positive when prices rise and volatility subsides, but they proved a highly significant depressive factor in a short time frame during the move following the U.S. tariff announcement,” they went on to state.

The Standard Chartered Bank analysts also noted in the report that the latest positioning data shows a further drift towards long crude oil positions but added that the next data is likely to be very different.

“The latest positioning data relates to 1 April, the day before the announcement of new U.S. tariffs; next week’s data is likely to look radically different after the heavy net selling of recent days,” the analysts said.

“The 1 April data is the last snapshot of a set of relatively positive oil market dynamics that would soon be swept away, but it is notable that they showed a shift to greater positivity from money managers on crude oil,” the analysts added.

“This was particularly evident in Brent, with our ICE Brent money-manager positioning index advancing 29.3 week on week to +71.1, which is the most positive reading since May 2018,” they continued.

The analysts went on to state in the report that the overall crude oil index moved into positive territory, “gaining 20.0 week on week to +12.2”, and pointed out that “for the first time since April 2024, crude oil moved to the top of the ranking of money-manager preferences across energy contracts”.

“This general move to a more balanced fund view of crude oil was described in an earlier report … however, the basis for that reappraisal has since been removed by changes in U.S. tariff policy and in OPEC+ policy,” the analysts added in the report.

In a research note sent to Rigzone by the JPM Commodities Research team late Monday, J.P. Morgan said the estimated value of open interest across energy markets “declined by -$43.5 billion week on week (seven percent week on week), despite contract-based inflows, as crude oil prices declined by over -12 percent week on week”.

“Contract-based inflows reached~$11.6 billion week on week, largely into crude markets ($13 billion week on week) while outflows departed natural gas markets (-$2.3 billion week on week),” it added.

“Our oil strategists flag that a potential trade war could reduce 2025 U.S. and global GDP by 0.5 percent-pt, lowering global oil demand by 250,000 barrels per day,” it continued.

Rigzone has contacted the White House for comment on Standard Chartered Bank’s report and the JPM Commodities Research team note. Rigzone has also contacted OPEC for comment on the Standard Chartered Bank report. At the time of writing, the White House and OPEC have not responded to Rigzone.

Standard Chartered Bank’s report showed that the company is forecasting that the ICE Brent nearby future crude oil price will average $77 per barrel in 2025 and $85 per barrel in 2026. The company is projecting that the NYMEX WTI basis nearby future crude oil price will average $75 per barrel this year and $82 per barrel next year, the report outlined.

A research note sent to Rigzone by the JPM Commodities Research team on Friday showed that J.P. Morgan expected the Brent crude price to average $73 per barrel in 2025 and $61 per barrel in 2026 and the WTI crude price to average $69 per barrel in 2025 and $57 per barrel in 2026.

To contact the author, email andreas.exarheas@rigzone.com

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IBM aims to bring AI agent sprawl under enterprise control

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HPE extends AI-driven networking to SD-WAN and SASE

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Energy Department Closes $5.1 Billion Loan to Lower Electricity Costs and Strengthen Grid Reliability in Iowa and Wisconsin

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Chevron adds operated Gulf of Mexico positions in interest swap with TotalEnergies

@import url(‘https://fonts.googleapis.com/css2?family=Inter:wght@100..900&display=swap’); .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; color: color-mix(in srgb, currentColor 60%, transparent); } Chevron Corp. has agreed to acquire operatorship and a 75% working interest in two exploration leases in the US Gulf of Mexico from TotalEnergies as part of a deal that will see the companies shuffle interest positions in jointly held acreage, TotalEnergies said in a release Oct. 28. As part of the same transaction, TotalEnergies has taken a 25% working interest in 26 Chevron-operated exploration leases. Financial terms were not disclosed. The acreage package includes 24 leases in the Green Canyon area and two in Walker Ridge, located 150-250 km offshore. The blocks target what TotalEnergies described as well-proven geological plays. TotalEnergies said the deal strengthens collaboration between the two companies, while expanding its US offshore exploration portfolio, building on its 2025 entry into 40 Chevron-operated exploration blocks. Since 2022, TotalEnergies has invested nearly $12 billion in the US to accelerate development in oil, LNG, and electricity.

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Energy Department Announces $5 Billion Loan to Boost Nuclear Power and Help Lower Costs in Illinois, Maryland, and Pennsylvania

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Neuquén awards eight blocks to expand Vaca Muerta exploration

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Ghana’s TOR proposes expanding Tema refinery to 200,000 b/sd by 2029

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Howard Energy Partners expands Anadarko position with Iron Horse Midstream acquisition

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HPE’s Quantum Computing Bet: A Multi-Vendor Path to the Data Center

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At 7×24 Exchange, AI Data Center Growth Confronts New Realities of Power, Grid Reliability and Community Trust

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NVIDIA’s DSX POD Pushes AI Factory Construction Toward the Assembly Line

Crusoe Brings an Established Modular Model to DSX POD For Crusoe, NVIDIA’s announcement builds on a modular computing strategy already in operation. In its October 21 announcement, Crusoe confirmed that its Spark modular AI data center platform will support DSX POD, extending a manufacturing and deployment approach the company says it has pursued since 2020. Crusoe has expanded its manufacturing capabilities through the acquisition of electrical equipment specialist Easter-Owens Electric and the development of its 352,000-square-foot Crusoe Spark Factory in Brighton, Colorado. The company also introduced Crusoe Spark Liquid, a sixth-generation modular system engineered for ultra-high-density infrastructure, including NVIDIA Vera Rubin NVL72. Next-generation liquid-cooled units are planned for 2027. Crusoe’s vertically integrated model connects infrastructure design and manufacturing with deployment and cloud operations through Crusoe Cloud. The company says it can deliver productive AI capacity in as little as three months, although that timeline is distinct from NVIDIA’s narrower 14-day prepared-site deployment claim. “Modular isn’t a new strategy or pivot for us. It’s been in our DNA since our founding,” said Bryan Schramm, Crusoe’s head of Spark. Crusoe’s involvement demonstrates that the manufacturing model predates DSX POD. NVIDIA is not inventing modular data center infrastructure; it is attempting to establish a common architecture around capabilities that suppliers such as Crusoe have already spent years developing. That distinction matters as the industry evaluates whether standardized AI factory designs can deliver manufacturing efficiencies at a scale beyond individual suppliers’ existing operations. Vertiv and Polar Put the Deployment Model to Work in Norway The European dimension of NVIDIA’s announcement is reflected in a deployment involving Vertiv and Polar Data Centers at Herøya, Norway. According to Vertiv’s October 21 announcement, Polar is applying a factory-built infrastructure approach to the initial 41 MW phase of a multiphase AI data center campus designed to scale to

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Hyperscalers allow more scrutiny of controversial data center plans

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Google will pay people to use less electricity so its data centers can use more

Google has an answer to some of the electricity supply problems that data center construction is causing: It has signed an agreement with energy supplier Voltus to build what it’s calling a 1-gigawatt virtual power plant, expanding on an earlier 100 megawatt deal the two signed earlier this year. This power plant is virtual in the sense that it doesn’t generate any electricity itself, but aggregates so-called flexible grid capacity into one central resource that can be deployed at times of peak demand. This flexible capacity can be in the form of batteries or of load-shedding: asking others to temporarily stop consuming electricity by shutting down industrial processes or adjusting tens of thousands of smart thermostats at once, effectively moving their electricity usage to a time when there is less demand. Voltus operates by offering incentives to companies to reduce their power consumption temporarily, and also offers inducements to electricity resellers that they can pass on to their customers.

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Big Tech’s Next Nuclear Bet: More Power From Reactors Already Running

The next wave of nuclear power for AI data centers may come not from new reactors, but from generating stations that have been operating for decades. In a pair of agreements announced just a week apart, Google and Amazon committed to long-term electricity purchases supporting approximately 1,080 MW of additional nuclear generating capacity from Constellation Energy’s existing fleet. The transactions follow earlier commitments by Meta to support reactor upgrades at operating nuclear facilities in Ohio, Pennsylvania and Illinois, establishing a broader commercial model in which hyperscale technology companies help finance increases in nuclear output. Under its October 6 agreement with Google, Constellation plans to invest more than $4.3 billion in equipment and technology upgrades at 11 operating reactor units across Illinois, Pennsylvania and New Jersey. The 20-year power purchase agreement supports 890 MW of new generating capacity, with the first upgrades expected to begin delivering additional electricity in 2028. A separate 15-year agreement covers 2,700 MW of existing nuclear generation, underscoring the distinction between securing today’s electricity supply and financing additional capacity. Amazon’s September 30 agreement similarly supports 190 MW of new generation at Maryland’s Calvert Cliffs nuclear station, alongside purchases of existing output. Together, the two arrangements underpin more than $7 billion in planned infrastructure investment. Unlike proposed advanced reactors, these facilities already have operating licenses, grid connections, established workforces and decades of operating experience. Increasing their output avoids some of the cost, permitting and construction challenges associated with building entirely new nuclear plants, although significant upgrades still require extensive engineering, equipment modifications and regulatory approval. The potential scale is substantial. According to the Nuclear Regulatory Commission’s current application schedule, the agency anticipates 31 applications for reactor power uprates through 2032, representing approximately 2,421 MW of potential additional electrical generating capacity. That is roughly equivalent to the output of

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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