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North America Adds 1 Rig Week on Week

North America added one rig week on week, according to Baker Hughes’ latest North America rotary rig count, which was released on October 10. The total U.S. rig count dropped by two week on week and the total Canada rig count increased by three during the same period, taking the total North America rig count […]

North America added one rig week on week, according to Baker Hughes’ latest North America rotary rig count, which was released on October 10.

The total U.S. rig count dropped by two week on week and the total Canada rig count increased by three during the same period, taking the total North America rig count up to 740, comprising 547 rigs from the U.S. and 193 rigs from Canada, the count outlined.

Of the total U.S. rig count of 547, 529 rigs are categorized as land rigs, 15 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 418 oil rigs, 120 gas rigs, and nine miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 480 horizontal rigs, 55 directional rigs, and 12 vertical rigs.

Week on week, the U.S. offshore and inland water rig counts remained unchanged, and the country’s land rig count dropped by two, Baker Hughes highlighted. The U.S. oil rig count dropped by four, its gas rig count increased by two, and miscellaneous rig count remained unchanged, week on week, the count showed. The U.S. directional rig count dropped by three week on week, while the country’s horizontal rig count increased by one and its vertical rig count remained unchanged, the count revealed.

A major state variances subcategory included in the rig count showed that, week on week, Texas dropped six rigs, Oklahoma dropped three rigs, Wyoming dropped one rig, New Mexico added four rigs, Utah added two rigs, and Louisiana and North Dakota each added one rig.

A major basin variances subcategory included in Baker Hughes’ rig count showed that, week on week, the Granite Wash basin dropped two rigs and the Eagle Ford, DJ-Niobrara and Permian basins each dropped one rig. The Haynesville basin and the Ardmore Woodford basins each added two rigs week on week and the Williston and Cana Woodford basins each added one rig week on week, the count revealed.

Canada’s total rig count of 193 is made up of 129 oil rigs, 63 gas rigs, and one miscellaneous rig, Baker Hughes pointed out. Week on week, the country’s oil and miscellaneous rig counts remained unchanged, and its gas rig count increased by three, the count revealed.

The total North America rig count is down 65 rigs compared to year ago levels, according to Baker Hughes’ count, which showed that the U.S. has cut 39 rigs and Canada has cut 26 rigs, year on year. The U.S. has dropped 63 oil rigs and added 19 gas rigs and five miscellaneous rigs, while Canada has dropped 25 oil rigs and two gas rigs, and added one miscellaneous rig, year on year, the count outlined.

In a report sent to Rigzone by the JPM Commodities Research team on Monday, analysts at J.P. Morgan highlighted that “total U.S. oil and gas rigs decreased by two this week to 547, according to Baker Hughes”.

“Oil-focused rigs saw a decrease of four, bringing the total to 418, following the loss of two rigs the previous week. Meanwhile, natural gas-focused rigs increased by two to 120 following an increase of one rig last week,” the analysts added.

“The rig count in the five major tight oil basins – we use the EIA [U.S. Energy Information Administration] basin definition- decreased by five to 396 rigs, while the rig count in the two major tight gas basins increased by one to 83 rigs. Miscellaneous rigs remained unchanged at nine,” they continued.

“This week, the U.S. oil rig count fell by four, marking the second consecutive weekly decline. Three rigs were lost in Delaware, Texas, while four were added in Delaware, New Mexico; Midland also saw a reduction of two rigs, with some of these changes likely linked to oil and gas pipeline maintenance in Texas,” the analysts stated.

“The Anadarko region experienced a drop of three rigs. Overall, the trend is mildly negative but not cause for concern – the slowdown in Permian activity appears to be driven more by logistical and midstream constraints than by underlying market weakness,” the analysts went on to note.

The J.P. Morgan analyst stated in the note that U.S. oil production growth remained robust, “largely fueled by strong gains in the Permian Basin”.

“In October, U.S. oil production growth is expected to moderate to 100,000 barrels per day year-over-year, with the Permian Basin contributing 170,000 barrels per day. This reflects slightly weaker prices, lower rig counts, and ongoing midstream maintenance,” it added.

In its previous rig count, which was released on October 3, Baker Hughes revealed that North America’s rig count remained unchanged week on week. The total U.S. rig count and the total Canada rig count didn’t budge week on week, that count showed.

Baker Hughes’ September 26 rig count revealed that North America added eight rigs week on week, its September 19 rig count revealed that North America added six rigs week on week, its September 12 rig count showed that North America added seven rigs week on week, and its September 5 rig count also revealed that North America added seven rigs week on week.

In its August 29 rig count, Baker Hughes showed that North America cut seven rigs week on week. The company’s August 22 rig count showed that North America cut four rigs week on week, its August 15 rig count revealed that North America added three rigs week on week, and its August 8 rig count revealed that North America added two rigs week on week.

Baker Hughes’ August 1 rig count showed that North America dropped seven rigs week on week, its July 25 rig count revealed that North America added eight rigs week on week, its July 18 count showed that North America added 17 rigs week on week, its July 11 rig count showed that North America added nine rigs week on week, and its July 3 count highlighted that North America added three rigs week on week.

In its June 27 rig count, Baker Hughes revealed that North America dropped six rigs week on week. The company’s June 20 rig count showed that the total North America rig count remained unchanged week on week, its June 13 rig count showed that North America added 20 rigs week on week, and its June 6 rig count showed that North America cut two rigs week on week.

Baker Hughes’ May 30 rig count revealed that North America dropped five rigs week on week, its May 23 count showed that North America dropped 17 rigs week on week, and its May 16 rig count showed that North America added five rigs week on week. The company’s May 9 rig count revealed that North America cut 12 rigs week on week, its May 2 count revealed that North America dropped 11 rigs week on week, and its April 25 count showed that North America dropped four rigs week on week.

Baker Hughes’ April 17 count showed that North America dropped two rigs week on week, its April 11 rig count revealed that North America cut 22 rigs week on week, the company’s April 4 rig count showed that North America cut 12 rigs week on week, its March 28 count revealed that North America cut 18 rigs week on week, and its March 21 rig count also revealed that North America cut 18 rigs week on week. Baker Hughes’ March 14 count showed that North America dropped 35 rigs week on week and its March 7 rig count revealed North America cut 15 rigs week on week.

In its February 28 rig count, Baker Hughes showed that North America added five rigs week on week. Its February 21 count revealed that North America added three rigs week on week, its February 14 rig count showed that North America dropped two rigs week on week, and its January 31 rig count showed that North America added 19 rigs week on week.

The company’s January 24 rig count revealed that North America added 12 rigs week on week, its January 17 count showed that North America added nine rigs week on week, and its January 10 rig count outlined that North America added 117 rigs week on week.

Baker Hughes’ January 3 rig count revealed that North America dropped one rig week on week and its December 27 rig count showed that North America dropped 71 rigs week on week.

Baker Hughes, which has issued rotary rig counts since 1944, describes the figures as an important business barometer for the drilling industry and its suppliers. The company notes that working rig location information is provided in part by Enverus.

To contact the author, email [email protected]

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Intel details new efficient Xeon processor line

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Shell Approves New Upstream Project for Nigeria LNG

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Repsol Starts Renewable Gasoline Production at Tarragona Complex

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North America Adds 1 Rig Week on Week

North America added one rig week on week, according to Baker Hughes’ latest North America rotary rig count, which was released on October 10. The total U.S. rig count dropped by two week on week and the total Canada rig count increased by three during the same period, taking the total North America rig count up to 740, comprising 547 rigs from the U.S. and 193 rigs from Canada, the count outlined. Of the total U.S. rig count of 547, 529 rigs are categorized as land rigs, 15 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 418 oil rigs, 120 gas rigs, and nine miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 480 horizontal rigs, 55 directional rigs, and 12 vertical rigs. Week on week, the U.S. offshore and inland water rig counts remained unchanged, and the country’s land rig count dropped by two, Baker Hughes highlighted. The U.S. oil rig count dropped by four, its gas rig count increased by two, and miscellaneous rig count remained unchanged, week on week, the count showed. The U.S. directional rig count dropped by three week on week, while the country’s horizontal rig count increased by one and its vertical rig count remained unchanged, the count revealed. A major state variances subcategory included in the rig count showed that, week on week, Texas dropped six rigs, Oklahoma dropped three rigs, Wyoming dropped one rig, New Mexico added four rigs, Utah added two rigs, and Louisiana and North Dakota each added one rig. A major basin variances subcategory included in Baker Hughes’ rig count showed that, week on week, the Granite Wash basin dropped two rigs and the Eagle Ford, DJ-Niobrara and Permian basins

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OpenAI–Broadcom alliance signals a shift to open infrastructure for AI

The decision also reflects a future of AI workloads running on heterogeneous computing and networking infrastructure, said Lian Jye Su, chief analyst at Omdia. “While it makes sense for enterprises to first rely on Nvidia’s full stack solution to roll out AI, they will generally integrate alternative solutions such as AMD and self-developed chips for cost efficiency, supply chain diversity, and chip availability,” Su said. “This means data center networking vendors will need to consider interoperability and open standards as ways to address the diversification of AI chip architecture.” Hyperscalers and enterprise CIOs are increasingly focused on how to efficiently scale up or scale out AI servers as workloads expand. Nvidia’s GPUs still underpin most large-scale AI training, but companies are looking for ways to integrate them with other accelerators. Neil Shah, VP for research at Counterpoint Research, said that Nvidia’s recent decision to open its NVLink interconnect to ecosystem players earlier this year gives hyperscalers more flexibility to pair Nvidia GPUs with custom accelerators from vendors such as Broadcom or Marvell. “While this reduces the dependence on Nvidia for a complete solution, it actually increases the total addressable market for Nvidia to be the most preferred solution to be tightly paired with the hyperscaler’s custom compute,” Shah said. Most hyperscalers have moved toward custom compute architectures to diversify beyond x86-based Intel or AMD processors, Shah added. Many are exploring Arm or RISC-V designs that can be tailored to specific workloads for greater power efficiency and lower infrastructure costs. Shifting AI infrastructure strategies The collaboration also highlights how networking choices are becoming as strategic as chip design itself, suggesting a change in how AI workloads are powered and connected.

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Inside Nvidia’s ‘grid-to-chip’ vision: How Vera Rubin and Spectrum-XGS push toward AI giga-factories

Vera Rubin MGX brings together Nvidia’s Vera CPUs and Rubin CPX GPUs, all using the same open MGX rack footprint as Blackwell. The system allows for numerous configurations and integrations. “MGX is a flexible, modular building block-based approach to server and rack scale design,” Delaere said. “It allows our ecosystem to create a wide range of configurations, and do so very quickly.” Vera Rubin MGX will deliver almost eight times more performance than Nvidia’s GB 300 for certain types of calculation, he said. The architecture is liquid-cooled and cable-free, allowing for faster assembly and serviceability. Operators can quickly mix and match components such as CPUs, GPUs, or storage, supporting interoperability, Nvidia said. Matt Kimball, principal data center analyst at Moor Insights and Strategy, highlighted the modularity and cleanness of the MGX tray design. “This simplifies the manufacturing process significantly,” he said. For enterprises managing tens or even hundreds of thousands of racks, “this design enables a level of operational efficiency that can deliver real savings in time and cost.” Nvidia is also showing innovation with cooling, Kimball said. “Running cooling to the midplane is a very clean design and more efficient.”

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Cisco seriously amps-up Silicon One chip, router for AI data center connectivity

Some say deep buffers shouldn’t be used to handle this type of traffic; the contention is that these buffers fill and drain, creating jitter in the workloads, and that slows things down, Chopra told Network World. “But the real source of that challenge is not the buffers. It’s a poor congestion management scheme and poor load balancing with AI workloads, which are completely deterministic and predictable. You can actually proactively figure out how to place flows across the network and avoid the congestion,” he said. The 8223’s deep-buffer design provides ample memory to temporarily store packets during congestion or traffic bursts, an essential feature for AI networks where inter-GPU communication can create unpredictable, high-volume data flows, according to Gurudatt Shenoy, vice president of Cisco Provider Connectivity. “Combined with its high-radix architecture, the 8223 allows more devices to connect directly, reducing latency, saving rack space, and further lowering power consumption. The result is a flatter, more efficient network topology supporting high-bandwidth, low-latency communication that is critical for AI workloads,” Shenoy wrote in a blog post. NOS options Notably, the first operating systems that the 8223 supports are the Linux Foundation’s Software for Open Networking in the Cloud (SONiC) and Facebook open switching system (FBOSS) – not Cisco’s own IOS XR.  IXR will be supported, too, but at a later date, according to Cisco.  SONiC decouples network software from the underlying hardware and lets it run on hundreds of switches and ASICs from multiple vendors while supporting a full suite of network features such as Border Gateway Protocol (BGP), remote direct memory access (RDMA), QoS, and Ethernet/IP. One of the keys to SONiC is its switch-abstraction interface, which defines an API to provide a vendor-independent way of controlling forwarding elements such as a switching ASIC, an NPU, or a software switch in a uniform

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Utilities Race to Meet Surging Data Center Demand With New Power Models

Over the last 18 months or so, the energy generation industry and its public utilities have been significantly impacted by the AI data center boom. It has been demonstrated across North America that the increase in demand for power, as driven by the demand for hyperscale and AI data centers, greatly exceeds the ability of the industry to actually generate and deliver power to meet the demand. We have covered many of the efforts being made to control the availability of power. In response, utilities and regulators have begun rethinking how to manage power availability through means such as: temporary moratoriums on new data center interconnections; the creation of new rate classes; cogeneration and load-sharing agreements; renewable integration; and power-driven site selection strategies.  But the bottom line is that in many locations utilities will need to change the way they work and how and where they spend their CAPEX budgets. The industry has already realized that their demand forecast models are hugely out of date, and that has had a ripple effect on much of the planning done by public utilities to meet the next generation of power demand. Most utilities now acknowledge that their demand forecasting models have fallen behind reality, triggering revisions to Integrated Resource Plans (IRPs) and transmission buildouts nationwide. This mismatch between forecast and actual demand is forcing a fundamental rethink of capital expenditure priorities and long-term grid planning. Spend More, Build Faster Utilities are sharply increasing CAPEX and rebalancing their resource portfolios—not just for decarbonization, but to keep pace with multi-hundred-megawatt data center interconnects. This trend is spreading across the industry, not confined to a few isolated utilities. Notable examples include: Duke Energy raised its five-year CAPEX plan to $83 billion (a 13.7% increase) and plans to add roughly 5 GW of natural gas capacity

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Duos Pairs Mobile Power and Modular Edge Data Centers for Rapid Texas Rollout

Duos Technology Group has launched the fifth of its AI edge data centers, part of a plan to deploy 15 units by the end of 2025. The projects are executed through Duos Edge AI, a subsidiary focused on modular, rapidly installed edge data centers (EDCs) in underserved markets, beginning with school districts and regional carrier hubs across Texas. The newest site is being deployed on-premises with the Dumas Independent School District in Dumas, Texas. High-Density Edge Design Duos’ EDCs emphasize very high rack densities (100 kW+ per rack), SOC 2 Type II compliance, N+1 power with dual generators, and a 90-day build/turn-up cycle. Each site is positioned approximately 12 miles from end users, cutting latency for real-time workloads. To meet the power demands of these edge deployments, Duos formed Duos Energy and partnered with Fortress/APR Energy to deliver behind-the-meter mobile gas turbines. This approach allows compute to go live in 90 days without waiting years for utility interconnection upgrades. The goal is straightforward: move power and compute close to demand, with rapid deployment. Duos’ modular pods are designed for exurban and rural locations as localized compute hubs for carriers, schools, healthcare systems, and municipal users. The rugged design pairs high-density racks with the short deployment cycle and proximity targeting, enabling a wide range of applications. With Dumas ISD now live, Duos has five sites in Texas, including Amarillo/Region 16, Victoria/Region 3, Dumas ISD, and multiple Corpus Christi locations. Mobile Power vs. Modular Compute While Duos doesn’t consistently describe its data center units as “mobile,” they are modular and containerized, engineered for rapid, site-agnostic deployment. The “mobile” label more explicitly applies to Duos’ power strategy—a turbine fleet that can be fielded or re-fielded to match demand. From an operator’s perspective, the combined proposition functions like a mobile platform: pre-integrated compute pods

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Report: AMD could be Intel’s next foundry customer

[ Related: More Intel news and insights ] AMD has lagged behind Nvidia in the AI business but has done well in the federal supercomputing business, holding numerous top spots with supercomputers like El Capitan and Frontier. Manufacturing its chips in the United States would be a good way to get the Trump administration off its back given its push for domestic manufacturing of semiconductors. The Trump administration is pushing for 50% of chips sold in America to be manufactured domestically, and tariffs on chips that are not. It also faces outbound restrictions. Earlier this year, AMD faced export restrictions GPUs meant for China as part of U.S. export controls against China’s AI business. “I believe this is a smart move by AMD to secure capacity in the local market without fighting against Nvidia and Apple and their deeper pockets for the limited capacity at TSMC,” said Alvi Nguyen, senior analyst with Forrester Research.” With the US investment in Intel, followed by Nvidia, this is can be seen as diversifying their supply chain and providing cheaper, locally sourced parts.” For Intel, this will continue a streak of good news it has enjoyed recently. “Having customers take up capacity at their foundries will go a long way in legitimizing their semiconductor processes and hopefully create the snowball effect of getting even more US-based customers,” said Nguyen. In recent weeks, Intel has partnered with Nvidia to jointly make PC and data center chips. Nvidia also took a $5B stake in Intel. Earlier the Trump administration made a $11.1B, or 10%, stake in Intel.

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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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Can we repair the internet?

From addictive algorithms to exploitative apps, data mining to misinformation, the internet today can be a hazardous place. Books by three influential figures—the intellect behind

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