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USA Emerges as World’s Hydrocarbon Superpower

The U.S. has emerged as the world’s hydrocarbon superpower, exemplified by its meteoric rise in the liquefied natural gas (LNG) market.   That’s what Wood Mackenzie (WoodMac) said in a statement sent to Rigzone recently, which highlighted several charts that “spotlight the most significant trends reshaping the [energy and resources] sector globally” and were included in the company’s […]

The U.S. has emerged as the world’s hydrocarbon superpower, exemplified by its meteoric rise in the liquefied natural gas (LNG) market.  

That’s what Wood Mackenzie (WoodMac) said in a statement sent to Rigzone recently, which highlighted several charts that “spotlight the most significant trends reshaping the [energy and resources] sector globally” and were included in the company’s latest Horizons report.

“You don’t need to look too far back to find a U.S. which was building LNG import infrastructure and now in under 10 years it has become the world’s largest LNG exporter,” WoodMac said in the statement.

The company noted in the statement that, by 2030, the U.S. is projected to account for 30 percent of global LNG output. A chart included in the statement outlined that the U.S. would continue as the world’s largest LNG exporter in 2030, followed by Qatar and Australia.

WoodMac also highlighted in its statement that the U.S. “leads global oil production (including oil, condensate, and natural gas liquids), delivering one-fifth of the world’s volumes”.

“In comparison, its closest competitors, Saudi Arabia and Russia, produce only 65 percent and 50 percent of U.S. volumes, respectively,” it added.  

Malcolm Forbes-Cable, Vice President, Upstream and Carbon Management Consulting at Wood Mackenzie, said in the statement, “the resurrection of U.S. LNG is a crucial reminder of what a resource-rich, free-market country like the U.S. can do”.

“This hydrocarbon hegemony is now being leveraged as a diplomatic tool,” he added.

In its latest short term energy outlook (STEO), which was released on December 9, the U.S. Energy Information Administration (EIA) projected that gross U.S. LNG exports will average 14.9 billion cubic feet per day in 2025 and 16.3 billion cubic feet per day in 2026. Gross U.S. LNG exports averaged 11.9 billion cubic feet per day in 2024, this STEO highlighted.

A quarterly breakdown included in the EIA’s latest STEO forecasted that gross U.S. LNG exports will come in at 16.7 billion cubic feet per day in the fourth quarter of this year, 16.5 billion cubic feet per day in the first quarter of 2026, 16.0 billion cubic feet per day in the second quarter, 15.3 billion cubic feet per day in the third quarter, and 17.7 billion cubic feet per day in the fourth quarter.

The EIA’s latest STEO also projected that total U.S. crude oil production, including lease condensate, will average 13.61 million barrels per day in 2025 and 13.53 million barrels per day in 2026. In 2024, this output averaged 13.23 million barrels per day, the EIA’s December STEO showed.

Another quarterly breakdown included in the EIA’s December STEO forecasted that U.S. crude oil production will average 13.86 million barrels per day in the fourth quarter of this year, 13.63 million barrels per day in the first quarter of next year, 13.58 million barrels per day in the second quarter, 13.44 million barrels per day in the third quarter, and 13.49 million barrels per day in the fourth quarter of 2026.

According to the Energy Institute’s (EI) latest statistical review of world energy, which was released earlier this year, the U.S. was the world’s largest natural gas producer last year with 37.19 exajoules. This figure marked 25.0 percent of the global total, the review highlighted.

The U.S. was also the world’s largest oil producer, with 20.135 million barrels per day, and crude oil and condensate producer, with 13.194 million barrels per day, in 2024, the EI review showed. The former represented 20.8 percent of global oil production and the latter represented 15.9 percent of global crude oil and condensate production in 2024, the review highlighted.

The EI report points out that its natural gas figures exclude gas flared or recycled and include natural gas produced for gas-to-liquids transformation. It also highlights that its oil production figures include crude oil, shale oil, oil sands, condensates (lease condensate or gas condensates that require further refining), and NGLs (natural gas liquids – ethane, LPG and naphtha separated from the production of natural gas). They exclude liquid fuels from other sources such as biofuels and synthetic derivatives of coal and natural gas and also exclude liquid fuel adjustment factors such as refinery processing gain, as well as oil shales/kerogen extracted in solid form, the review outlined.

In its review, the EI also points out that its crude oil and condensate production figures include crude oil, shale/tight oil, oil sands, and lease condensate or gas condensates that require further refining. They exclude liquid fuels from other sources such as biomass and synthetic derivatives of coal and natural gas, the review outlines.

WoodMac’s Horizons report explores “the themes shaping the energy and natural resources landscape, bringing you crucial insights, new perspectives, bold forecasts and food for thought”, the company’s website notes.

The EI states in its review that it is the professional membership body for the world of energy. It adds in its publication that the statistical review of world energy analyzes data on world energy markets from the prior year and notes that the review “has been providing timely, comprehensive, and objective data to the energy community since 1952”.

To contact the author, email [email protected]

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Nvidia moves deeper into AI infrastructure with SchedMD acquisition

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Chevron Reduces Price for Venezuelan Oil

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Phillips 66 Budgets $2.4B for 2026

Phillips 66 said Monday it expects a $2.4 billion budget for next year, consisting of $1.1 billion in sustaining capital and $1.3 billion in growth capital. “The 2026 capital budget reflects our ongoing commitment to capital discipline and maximizing shareholder returns”, chair and chief executive Mark Lashier said in an online statement. “We are investing growth capital in our NGL value chain and high-return refining projects, while also investing sustaining capital to support safe and reliable operations”. Houston, Texas-based Phillips 66 expects to shell out $1.1 billion into its refining business, comprising $590 million in sustaining capital and $520 million into growth projects. “With the consolidation of WRB Refining, we incorporated approximately $200 million of sustaining capital and $100 million of growth capital into the budget”, Lashier said. Phillips 66 recently acquired an additional 50 percent stake in WRB Refining LP from Cenovus Energy Inc for $1.4 billion, fully taking over the Wood River and Borger refineries, as confirmed by Phillips 66 in its third quarter report October 29. Wood River in Roxana, Illinois, has a gasoline and distillates production capacity of 176,000 bpd and 140,000 bpd respectively. Borger in Borger, Texas, produces up to 100,000 bpd of gasoline and 70,000 bpd of distillates, according to Phillips 66. The refining allotment for 2026 also includes a multiyear investment at the Humber refinery to enable the production of higher-quality gasoline and expand the facility’s access to “higher-value global markets”, the company said. Phillips 66 expects to start up the project in the second quarter of 2027. Located in North Lincolnshire on the English east coast, the Humber site produces up to 95,000 barrels per day (bpd) of gasoline and 115,000 bpd of distillates, according to Phillips 66. The refining budget also includes “over 100 low-capital, high-return projects to improve market capture

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USA Emerges as World’s Hydrocarbon Superpower

The U.S. has emerged as the world’s hydrocarbon superpower, exemplified by its meteoric rise in the liquefied natural gas (LNG) market.   That’s what Wood Mackenzie (WoodMac) said in a statement sent to Rigzone recently, which highlighted several charts that “spotlight the most significant trends reshaping the [energy and resources] sector globally” and were included in the company’s latest Horizons report. “You don’t need to look too far back to find a U.S. which was building LNG import infrastructure and now in under 10 years it has become the world’s largest LNG exporter,” WoodMac said in the statement. The company noted in the statement that, by 2030, the U.S. is projected to account for 30 percent of global LNG output. A chart included in the statement outlined that the U.S. would continue as the world’s largest LNG exporter in 2030, followed by Qatar and Australia. WoodMac also highlighted in its statement that the U.S. “leads global oil production (including oil, condensate, and natural gas liquids), delivering one-fifth of the world’s volumes”. “In comparison, its closest competitors, Saudi Arabia and Russia, produce only 65 percent and 50 percent of U.S. volumes, respectively,” it added.   Malcolm Forbes-Cable, Vice President, Upstream and Carbon Management Consulting at Wood Mackenzie, said in the statement, “the resurrection of U.S. LNG is a crucial reminder of what a resource-rich, free-market country like the U.S. can do”. “This hydrocarbon hegemony is now being leveraged as a diplomatic tool,” he added. In its latest short term energy outlook (STEO), which was released on December 9, the U.S. Energy Information Administration (EIA) projected that gross U.S. LNG exports will average 14.9 billion cubic feet per day in 2025 and 16.3 billion cubic feet per day in 2026. Gross U.S. LNG exports averaged 11.9 billion cubic feet per day in 2024, this STEO highlighted. A quarterly breakdown included in the EIA’s latest STEO forecasted that gross U.S. LNG exports will come in at

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North America Rig Count Stays Flat

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Pembina Completes Remarketing of Cedar LNG Share

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TotalEnergies, Galp Agree Exchange Involving Mopane, Venus Discoveries

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Executive Roundtable: Converging Disciplines in the AI Buildout

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DCF Trends Summit 2025: AI for Good – How Operators, Vendors and Cooling Specialists See the Next Phase of AI Data Centers

At the 2025 Data Center Frontier Trends Summit (Aug. 26-28) in Reston, Va., the conversation around AI and infrastructure moved well past the hype. In a panel sponsored by Schneider Electric—“AI for Good: Building for AI Workloads and Using AI for Smarter Data Centers”—three industry leaders explored what it really means to design, cool and operate the new class of AI “factories,” while also turning AI inward to run those facilities more intelligently. Moderated by Data Center Frontier Editor in Chief Matt Vincent, the session brought together: Steve Carlini, VP, Innovation and Data Center Energy Management Business, Schneider Electric Sudhir Kalra, Chief Data Center Operations Officer, Compass Datacenters Andrew Whitmore, VP of Sales, Motivair Together, they traced both sides of the “AI for Good” equation: building for AI workloads at densities that would have sounded impossible just a few years ago, and using AI itself to reduce risk, improve efficiency and minimize environmental impact. From Bubble Talk to “AI Factories” Carlini opened by acknowledging the volatility surrounding AI investments, citing recent headlines and even Sam Altman’s public use of the word “bubble” to describe the current phase of exuberance. “It’s moving at an incredible pace,” Carlini noted, pointing out that roughly half of all VC money this year has flowed into AI, with more already spent than in all of the previous year. Not every investor will win, he said, and some companies pouring in hundreds of billions may not recoup their capital. But for infrastructure, the signal is clear: the trajectory is up and to the right. GPU generations are cycling faster than ever. Densities are climbing from high double-digits per rack toward hundreds of kilowatts. The hyperscale “AI factories,” as NVIDIA calls them, are scaling to campus capacities measured in gigawatts. Carlini reminded the audience that in 2024,

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FinOps Foundation sharpens FOCUS to reduce cloud cost chaos

“The big change that’s really started to happen in late 2024 early 2025 is that the FinOps practice started to expand past the cloud,” Storment said. “A lot of organizations got really good at using FinOps to manage the value of cloud, and then their organizations went, ‘oh, hey, we’re living in this happily hybrid state now where we’ve got cloud, SaaS, data center. Can you also apply the FinOps practice to our SaaS? Or can you apply it to our Snowflake? Can you apply it to our data center?’” The FinOps Foundation’s community has grown to approximately 100,000 practitioners. The organization now includes major cloud vendors, hardware providers like Nvidia and AMD, data center operators and data cloud platforms like Snowflake and Databricks. Some 96 of the Fortune 100 now participate in FinOps Foundation programs. The practice itself has shifted in two directions. It has moved left into earlier architectural and design processes, becoming more proactive rather than reactive. It has also moved up organizationally, from director-level cloud management roles to SVP and COO positions managing converged technology portfolios spanning multiple infrastructure types. This expansion has driven the evolution of FOCUS beyond its original cloud billing focus. Enterprises are implementing FOCUS as an internal standard for chargeback reporting even when their providers don’t generate native FOCUS data. Some newer cloud providers, particularly those focused on AI infrastructure, are using the FOCUS specification to define their billing data structures from the ground up rather than retrofitting existing systems. The FOCUS 1.3 release reflects this maturation, addressing technical gaps that have emerged as organizations apply cost management practices across increasingly complex hybrid environments. FOCUS 1.3 exposes cost allocation logic for shared infrastructure The most significant technical enhancement in FOCUS 1.3 addresses a gap in how shared infrastructure costs are allocated and

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Aetherflux joins the race to launch orbital data centers by 2027

Enterprises will connect to and manage orbital workloads “the same way they manage cloud workloads today,” using optical links, the spokesperson added. The company’s approach is to “continuously launch new hardware and quickly integrate the latest architectures,” with older systems running lower-priority tasks to serve out the full useful lifetime of their high-end GPUs. The company declined to disclose pricing. Aetherflux plans to launch about 30 satellites at a time on SpaceX Falcon 9 rockets. Before the data center launch, the company will launch a power-beaming demonstration satellite in 2026 to test transmission of one kilowatt of energy from orbit to ground stations, using infrared lasers. Competition in the sector has intensified in recent months. In November, Starcloud launched its Starcloud-1 satellite carrying an Nvidia H100 GPU, which is 100 times more powerful than any previous GPU flown in space, according to the company, and demonstrated running Google’s Gemma AI model in orbit. In the same month, Google announced Project Suncatcher, with a 2027 demonstration mission planned. Analysts see limited near-term applications Despite the competitive activity, orbital data centers won’t replace terrestrial cloud regions for general hosting through 2030, said Ashish Banerjee, senior principal analyst at Gartner. Instead, they suit specific workloads, including meeting data sovereignty requirements for jurisdictionally complex scenarios, offering disaster recovery immune to terrestrial risks, and providing asynchronous high-performance computing, he said. “Orbital centers are ideal for high-compute, low-I/O batch jobs,” Banerjee said. “Think molecular folding simulations for pharma, massive Monte Carlo financial simulations, or training specific AI model weights. If the job takes 48 hours, the 500ms latency penalty of LEO is irrelevant.” One immediate application involves processing satellite-generated data in orbit, he said. Earth observation satellites using synthetic aperture radar generate roughly 10 gigabytes per second, but limited downlink bandwidth creates bottlenecks. Processing data in

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Here’s what Oracle’s soaring infrastructure spend could mean for enterprises

He said he had earlier told analysts in a separate call that margins for AI workloads in these data centers would be in the 30% to 40% range over the life of a customer contract. Kehring reassured that there would be demand for the data centers when they were completed, pointing to Oracle’s increasing remaining performance obligations, or services contracted but not yet delivered, up $68 billion on the previous quarter, saying that Oracle has been seeing unprecedented demand for AI workloads driven by the likes of Meta and Nvidia. Rising debt and margin risks raise flags for CIOs For analysts, though, the swelling debt load is hard to dismiss, even with Oracle’s attempts to de-risk its spend and squeeze more efficiency out of its buildouts. Gogia sees Oracle already under pressure, with the financial ecosystem around the company pricing the risk — one of the largest debts in corporate history, crossing $100 billion even before the capex spend this quarter — evident in the rising cost of insuring the debt and the shift in credit outlook. “The combination of heavy capex, negative free cash flow, increasing financing cost and long-dated revenue commitments forms a structural pressure that will invariably finds its way into the commercial posture of the vendor,” Gogia said, hinting at an “eventual” increase in pricing of the company’s offerings. He was equally unconvinced by Magouyrk’s assurances about the margin profile of AI workloads as he believes that AI infrastructure, particularly GPU-heavy clusters, delivers significantly lower margins in the early years because utilisation takes time to ramp.

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New Nvidia software gives data centers deeper visibility into GPU thermals and reliability

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

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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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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