Shell farms out partial interest in North Cleopatra block offshore Egypt

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Shell farms out partial interest in North Cleopatra block offshore Egypt

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Shell plc agreed to farm out to QatarEnergy a 27% non-working interest in the North Cleopatra block offshore Egypt.

In a release Oct. 5, state-owned QatarEnergy said under terms of the agreement, which is subject to approval by the Egyptian government, Shell will retain a 36% participating interest as operator. Other participating interest holders are Chevron (27%) and Tharwa Petroleum Co. (10%).

The 3,400-sq km North Cleopatra block, in the frontier Herodotus basin, lies north and adjacent to North El-Dabaa block where QatarEnergy holds a 23% participating interest.

North Cleopatra lies in water depths of up to 2,600 m and is one of the last underexplored areas in the region, according to QatarEnergy.

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NextDecade names Mott as interim CFO

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The business case for microsegmentation: Lower insurance costs, 33% faster ransomware response

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Three options for wireless power in the enterprise

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IBM unveils advanced quantum computer in Spain

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Oil Ends Third Straight Weekly Loss

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Colombia Warms to Fossil Fuels

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Phillips 66 progresses California refinery shuttering plan, Chevron El Segundo fire adds to state’s refining uncertainty

Capacity crash Phillips 66’s announcement of its progress on the refinery shuttering comes amid growing concerns by California’s in-state refiners that unfavorable market conditions alongside regulatory pressure stemming from aggressive state legislation aimed at refiners will prevent long-term viability and competitiveness of crude processing activities in the region. While state officials recently voted to delay enactment of legislation blamed for decisions by California refiners to end operations at their conventional processing sites—which, in addition to Phillips 66 in Los Angeles, includes Valero Energy Corp.’s planned closure of its 145,000-b/d Benicia refinery, just northeast of San Francisco, by midyear 2026—refiners have yet to indicate any signs of backing down from advancing existing shutdown plans. With the state’s loss of about 20% of its traditional crude processing capacity since 2020—and nearly another estimated 20% at threat with the closure of the Los Angeles and Benicia sites—California’s refining capacity may face an additional short-term blow to its consumer fuels market following a late-evening fire on Oct. 2 at Chevron 290,000-b/d refinery in El Segundo. Chevron El Segundo refinery fire “Chevron fire department personnel, including emergency responders from the City of El Segundo and Manhattan Beach [were] actively responding to an isolated fire inside the Chevron El Segundo [r]efinery [on Oct. 2]. All refinery personnel and contractors have been accounted for and there are no injuries,” Chevron said in a statement posted to its official website. “No evacuation orders for area residents [were] put in place by emergency response agencies monitoring the incident, and no exceedances have been detected by the facilities fence line monitoring system,” the operator said. In a separate early morning statement on Oct. 3, the city of El Segundo said the fire “originated at a process unit at the southeast corner of the refinery.” Details of the impacted unit, however,

OPEC+ to hike oil production by 137,000 b/d starting in November

The Organization of the Petroleum Exporting Countries, along with Russia and a few smaller producers, known as OPEC+, on Oct. 5 announced plans to increase oil production by 137,000 b/d starting in November, maintaining the same increase as in October in response to ongoing worries about a potential supply surplus. OPEC+ described its decision as a reaction to stable global economic outlooks and current healthy market fundamentals, as reflected in low oil inventories. The group also indicates that adjustments to production could be halted or reversed if circumstances warrant. By end-September 2025, OPEC+ had fully phased out the 2.2 million b/d voluntary production cuts introduced in late 2023, completing the process months earlier than initially signaled. They also confirmed their intention to fully compensate for any overproduced volume since January 2024. The next meeting will be hold on Nov. 2. “The market was expecting a somewhat larger increase from OPEC+ as shown in the structure last week. However, the modest 137,000 b/d bloats the already-oversupplied balance for the fourth quarter of 2025 and 2026,” said Janiv Shah, an analyst at Rystad Energy. Global crude oil markets are experiencing a significant bearish trend, with ICE Brent prices dropping from last week’s peak of $70/bbl to $65/bbl on Oct. 5, following OPEC’s announcement to increase production. Rystad Energy forecasts that under these circumstances, price dynamics will likely shift substantially downward. It is unlikely that ICE Brent will remain above $60-65/bbl in 2026 unless OPEC+ alters its approach or sanctions on Russia and Iran drastically curtail exports from those countries.

Oversupply concerns tank oil prices

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The Business: Mitigating and Eliminating Corrosion Under Insulation

@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: #c19a06; } .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; font-family: Inter; } 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; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; background-color: undefined !important; } <!–> In this episode of the Oil & Gas Journal ReEnterprised podcast, Chris Smith, editor-in-chief, talks with Kristin Leonard, North American Marketing Director for the Energy Market at Sherwin-Williams. They discuss the causes and consequences of CUI, including its impact on asset integrity and operational safety. This episode offers insights into how evolving engineering solutions are addressing long-standing challenges in energy operations. ]–> Podcast Guest: <!–> [–> <!–> Kristin LeonardNorth American Marketing DirectorSherwin-Williams –> <!–> Kristin Leonard is the North American Marketing Director for the Energy Market for Sherwin-Williams Protective & Marine Division. With extensive experience in the coatings industry, she has held roles at Bechtel Corporation EPC and ExxonMobil Downstream. Kristin holds a bachelor’s degree in Polymer Science and High-Performance Materials from The University of Southern Mississippi and has received several industry awards. She is also a past Chair for the AMPP Global Center Board of Directors. ]–> <!–> This content is sponsored by: ]–> <!–> –> <!–> ]–> <!–> ]–>

Roundup: Digital Realty Marks Major Milestones in AI, Quantum Computing, Data Center Development

Key features of the DRIL include: • High-Density AI and HPC Testing. The DRIL supports AI and high-performance computing (HPC) workloads with high-density colocation, accommodating workloads up to 150 kW per cabinet. • AI Infrastructure Optimization. The ePlus AI Experience Center lets businesses explore AI-specific power, cooling, and GPU resource requirements in an environment optimized for AI infrastructure. • Hybrid Cloud Validation. With direct cloud connectivity, users can refine hybrid strategies and onboard through cross connects. • AI Workload Orchestration. Customers can orchestrate AI workloads across Digital Realty’s Private AI Exchange (AIPx) for seamless integration and performance. • Latency Testing Across Locations. Enterprises can test latency scenarios for seamless performance across multiple locations and cloud destinations. The firm’s Northern Virginia campus is the primary DRIL location, but companies can also test latency scenarios between there and other remote locations. DRIL rollout to other global locations is already in progress, and London is scheduled to go live in early 2026. Digital Realty, Redeployable Launch Pathway for Veteran Technical Careers As new data centers are created, they need talented workers. To that end, Digital Realty has partnered with Redeployable, an AI-powered career platform for veterans, to expand access to technical careers in the United Kingdom and United States. The collaboration launched a Site Engineer Pathway, now live on the Redeployable platform. It helps veterans explore, prepare for, and transition into roles at Digital Realty. Nearly half of veterans leave their first civilian role within a year, often due to unclear expectations, poor skill translation, and limited support, according to Redeployable. The Site Engineer Pathway uses real-world relevance and replaces vague job descriptions with an experience-based view of technical careers. Veterans can engage in scenario-based “job drops” simulating real facility and system challenges so they can assess their fit for the role before applying. They

BlackRock’s $40B data center deal opens a new infrastructure battle for CIOs

Everest Group partner Yugal Joshi said, “CIOs are under significant pressure to clearly define their data center strategy beyond traditional one-off leases. Given most of the capacity is built and delivered by fewer players, CIOs need to prepare for a higher-price market with limited negotiation power.” The numbers bear this out. Global data center costs rose to $217.30 per kilowatt per month in the first quarter of 2025, with major markets seeing increases of 17-18% year-over-year, according to CBRE. Those prices are at levels last seen in 2011-2012, and analysts expect them to remain elevated. Gogia said, “The combination of AI demand, energy scarcity, and environmental regulation has permanently rewritten the economics of running workloads. Prices that once looked extraordinary have now become baseline.” Hyperscalers get first dibs The consolidation problem is compounded by the way capacity is being allocated. North America’s data center vacancy rate fell to 1.6% in the first half of 2025, with Northern Virginia posting just 0.76%, according to CBRE Research. More troubling for enterprises: 74.3% of capacity currently under construction is already preleased, primarily to cloud and AI providers. “The global compute market is no longer governed by open supply and demand,” Gogia said. “It is increasingly shaped by pre-emptive control. Hyperscalers and AI majors are reserving capacity years in advance, often before the first trench for power is dug. This has quietly created a two-tier world: one in which large players guarantee their future and everyone else competes for what remains.” That dynamic forces enterprises into longer planning cycles. “CIOs must forecast their infrastructure requirements with the same precision they apply to financial budgets and talent pipelines,” Gogia said. “The planning horizon must stretch to three or even five years.”

Nvidia, Infineon partner for AI data center power overhaul

The solution is to convert power right at the GPU on the server board and to upgrade the backbone to 800 volts. That should squeeze more reliability and efficiency out of the system while dealing with the heat, Infineon stated. Nvidia announced the 800 Volt direct current (VDC) power architecture at Computex 2025 as a much-needed replacement for the 54 Volt backbone currently in use, which is overwhelmed by the demand of AI processors and increasingly prone to failure. “This makes sense with the power needs of AI and how it is growing,” said Alvin Nguyen, senior analyst with Forrester Research. “This helps mitigate power losses seen from lower voltage and AC systems, reduces the need for materials like copper for wiring/bus bars, better reliability, and better serviceability.” Infineon says a shift to a centralized 800 VDC architecture allows for reduced power losses, higher efficiency and reliability. However, the new architecture requires new power conversion solutions and safety mechanisms to prevent potential hazards and costly server downtimes such as service and maintenance.

Meta details cutting-edge networking technologies for AI infrastructure

ESUN initiative As part of its standardization efforts, Meta said it would be a key player in the new Ethernet for Scale-Up Networking (ESUN) initiative that brings together AMD, Arista, ARM, Broadcom, Cisco, HPE Networking, Marvell, Microsoft, NVIDIA, OpenAI and Oracle to advance the networking technology to handle the growing scale-up domain for AI systems. ESUN will focus solely on open, standards-based Ethernet switching and framing for scale-up networking—excluding host-side stacks, non-Ethernet protocols, application-layer solutions, and proprietary technologies. The group will focus on the development and interoperability of XPU network interfaces and Ethernet switch ASICs for scale-up networks, the OCP wrote in a blog. ESUN will actively engage with other organizations such as Ultra-Ethernet Consortium (UEC) and long-standing IEEE 802.3 Ethernet to align open standards, incorporate best practices, and accelerate innovation, the OCP stated. Data center networking milestones The launch of ESUN is just one of the AI networking developments Meta shared at the event. Meta engineers also announced three data center networking innovations aimed at making its infrastructure more flexible, scalable, and efficient: The evolution of Meta’s Disaggregated Scheduled Fabric (DSF) to support scale-out interconnect for large AI clusters that span entire data center buildings. A new Non-Scheduled Fabric (NSF) architecture based entirely on shallow-buffer, disaggregated Ethernet switches that will support our largest AI clusters like Prometheus. The addition of Minipack3N, based on Nvidia’s Ethernet Spectrum-4 ASIC, to Meta’s portfolio of 51Tbps OCP switches that use OCP’s Switch Abstraction Interface and Meta’s Facebook Open Switching System (FBOSS) software stack. DSF is Meta’s open networking fabric that completely separates switch hardware, NICs, endpoints, and other networking components from the underlying network and uses OCP-SAI and FBOSS to achieve that, according to Meta. It supports Ethernet-based RoCE RDMA over Converged Ethernet (RoCE/RDMA)) to endpoints, accelerators and NICs from multiple vendors, such as Nvidia,

Arm joins Open Compute Project to build next-generation AI data center silicon

Keeping up with the demand comes down to performance, and more specifically, performance per watt. With power limited, OEMs have become much more involved in all aspects of the system design, rather than pulling silicon off the shelf or pulling servers or racks off the shelf. “They’re getting much more specific about what that silicon looks like, which is a big departure from where the data center was ten or 15 years ago. The point here being is that they look to create a more optimized system design to bring the acceleration closer to the compute, and get much better performance per watt,” said Awad. The Open Compute Project is a global industry organization dedicated to designing and sharing open-source hardware configurations for data center technologies and infrastructure. It covers everything from silicon products to rack and tray design. It is hosting its 2025 OCP Global Summit this week in San Jose, Calif. Arm also was part of the Ethernet for Scale-Up Networking (ESUN) initiative announced this week at the Summit that included AMD, Arista, Broadcom, Cisco, HPE Networking, Marvell, Meta, Microsoft, and Nvidia. ESUN promises to advance Ethernet networking technology to handle scale-up connectivity across accelerated AI infrastructures. Arm’s goal by joining OCP is to encourage knowledge sharing and collaboration between companies and users to share ideas, specifications and intellectual property. It is known for focusing on modular rather than monolithic designs, which is where chiplets come in. For example, customers might have multiple different companies building a 64-core CPU and then choose IO to pair it with, whether like PCIe or an NVLink. They then choose their own memory subsystem, deciding whether to go HBM, LPDDR, or DDR. It’s all mix and match like Legos, Awad said.

BlackRock-Led Consortium to Acquire Aligned Data Centers in $40 Billion AI Infrastructure Deal

Capital Strategy and Infrastructure Readiness The AIP consortium has outlined an initial $30 billion in equity, with potential to scale toward $100 billion including debt over time as part of a broader AI infrastructure buildout. The Aligned acquisition represents a cornerstone investment within that capital roadmap. Aligned’s “ready-to-scale” platform – encompassing land, permits, interconnects, and power roadmaps – is far more valuable today than a patchwork of single-site developments. The consortium framed the transaction as a direct response to the global AI buildout crunch, targeting critical land, energy, and equipment bottlenecks that continue to constrain hyperscale expansion. Platform Overview: Aligned’s Evolution and Strategic Fit Aligned Data Centers has rapidly emerged as a scale developer and operator purpose-built for high-density, quick-turn capacity demanded by hyperscalers and AI platforms. Beyond the U.S., Aligned extended its reach across the Americas through its acquisition of ODATA in Latin America, creating a Pan-American presence that now spans more than 50 campuses and over 5 GW of capacity. The company has repeatedly accessed both public and private capital markets, most recently securing more than $12 billion in new equity and debt financing to accelerate expansion. Aligned’s U.S.–LATAM footprint provides geographic diversification and proximity to fast-growing AI regions. The buyer consortium’s global relationships – spanning utilities, OEMs, and sovereign-fund partners – help address power, interconnect, and supply-chain constraints, all of which are critical to sustaining growth in the AI data-center ecosystem. Macquarie Asset Management built Aligned from a niche U.S. operator into a 5 GW-plus, multi-market platform, the kind of asset infrastructure investors covet as AI demand outpaces grid and supply-chain capacity. Its sale at this stage reflects a broader wave of industry consolidation among large-scale digital-infrastructure owners. Since its own acquisition by BlackRock in early 2024, GIP has strengthened its position as one of the world’s top owners

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.

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

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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

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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