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Woodside Posts 2P Reserves of 3.09 Billion Boe

Woodside Energy Group Ltd. has reported 3.09 billion barrels of oil equivalent (boe) proven and probable (2P) reserves as of the end of 2024, up 46.2 million boe (MMboe) when excluding divestments and production. The Australian company had 2P reserves of 3.76 billion boe at the end of 2023. “Excluding the impact of divestments and […]

Woodside Energy Group Ltd. has reported 3.09 billion barrels of oil equivalent (boe) proven and probable (2P) reserves as of the end of 2024, up 46.2 million boe (MMboe) when excluding divestments and production.

The Australian company had 2P reserves of 3.76 billion boe at the end of 2023. “Excluding the impact of divestments and production, reserves additions [in 2024] were driven by strong performance at Sangomar, successful FIDs [final investment decisions] of projects in Australia and the US, and performance-based revisions across the portfolio, notably North West Shelf and Bass Strait”, Woodside said in an online statement.

It started production in the Sangomar field offshore Senegal in the second quarter of 2024, delivering the West African country’s first offshore oil development.

“Early performance from the S500 reservoirs has demonstrated excellent productivity”, Woodside said of Sangomar. “This has resulted in proved and proved plus probable reserves additions of 16.2 MMboe and 15.4 MMboe respectively”.

Woodside’s proven reserves stood at 1.98 billion boe at year-end 2024, compared to 2.45 billion boe at year-end 2023.

The sale of a 25.1 percent stake in the Scarborough field offshore Western Australia to Japanese companies reduced proven reserves by 323 MMboe and 2P reserves by 504.7 MMboe, Woodside said.

Best-estimate contingent resources remaining were 5.87 billion boe at year-end 2024, compared to 5.9 billion boe at year-end 2023.

“The reserves update underscores Woodside’s high-quality assets and disciplined execution”, chief executive Meg O’Neill commented. “The outstanding early performance at Sangomar again demonstrates Woodside’s proven record of delivering large-scale projects that provide sustainable returns over the long term.

“Sangomar is forecast to continue producing on plateau into the second quarter of 2025 and with continued strong asset performance across the portfolio we are well positioned for another year of delivering value for shareholders”.

Sangomar, which Woodside operates with an 82 percent stake, produced 13.3 MMboe of crude last year.

“The project ramped up in less than nine weeks, and achieved over 94 percent production reliability in Q4 2024”, Woodside said. “Both water and gas injection systems have been fully commissioned”.

“Future development decisions will be informed by 12-24 months of production data”, it said.

O’Neill added, “As Woodside embarks on the next phase of growth, continuing to execute Scarborough and Trion and preparing for a final investment decision on Louisiana LNG, we will maintain our disciplined approach and commitment to safety, reliability and performance”.

Trion is an under-construction field on the Mexican side of the Gulf of Mexico. Woodside plans start-up in the second half of 2025. Trion will have a floating production unit with an output capacity of 100,000 barrels a day, to be connected to a floating storage and offloading vessel with a capacity of 950,000 barrels.

In Louisiana LNG, formerly Driftwood LNG, Woodside expects to make a final investment decision this quarter. Woodside took over Driftwood LNG, located near Lake Charles, Lousiana, when it acquired Tellurian Inc. for about $1.2 billion.

The under-construction project is planned to have a capacity of 27.6 million metric tons a year of LNG. 

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Cisco routers knocked out due to Cloudflare DNS change

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Intensity, Rainbow Near FID on North Dakota Gas Pipeline

Intensity Infrastructure Partners LLC and power producer Rainbow Energy Center LLC have indicated they are nearing a positive final investment decision (FID) on a new pipeline project to bring Bakken natural gas to eastern North Dakota. “[T]he firm transportation commitments contained in executed precedent agreements are sufficient to underpin the decision to advance Phase I of their 36-inch natural gas pipeline in North Dakota, reflecting growing confidence in the region’s long-term power and industrial demand outlook”, the companies said in a joint statement. “This approach establishes a scalable, dispatchable power and gas delivery hub capable of adapting to evolving market conditions, supporting sustained data center growth, grid reliability needs and long-term industrial development across North Dakota”. “The system will provide reliable natural gas supply through multiple receipt points, including Northern Border Pipeline, WBI Energy’s existing transmission and storage network, and direct connections to six Bakken natural gas processing plants, creating a highly integrated supply platform from Bakken and Canadian production”, the online statement added. “The pipeline is designed to operate without compression fuel surcharges, reducing operational complexity while enhancing reliability and tariff transparency for shippers. “Uncommitted capacity on phase I supports incremental gas-fired generation along the planned pipeline corridor and at Coal Creek Station, leveraging existing power transmission infrastructure, a strategic geographic location and a proven operating platform. “The 36-inch pipeline enables future throughput increases without the need for duplicative greenfield infrastructure as demand continues to develop”. Rainbow chief executive Stacy Tschider said, “By leveraging established assets like Coal Creek and integrating directly with basin supply and interstate systems, this project is positioned to meet near-term needs while remaining expandable for the next generation of load growth”. The project would proceed in two phases. Phase 1 would build a 136-mile, 36-inch pipeline with a capacity of about 1.1 million dekatherms a day (Dthd). The phase 1 line would

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WoodMac Sees USA Tight Oil Output Shrinking in 2026

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Tamboran Names New CEO

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Distributed load flexibility: The overlooked relief valve for the grid

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AI, edge, and security: Shaping the need for modern infrastructure management

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DCF Poll: Analyzing AI Data Center Growth

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JLL’s 2026 Global Data Center Outlook: Navigating the AI Supercycle, Power Scarcity and Structural Market Transformation

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SoftBank, DigitalBridge, and Stargate: The Next Phase of OpenAI’s Infrastructure Strategy

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Lenovo unveils purpose-built AI inferencing servers

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Samsung warns of memory shortages driving industry-wide price surge in 2026

SK Hynix reported during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, while Micron recently exited the consumer memory market entirely to focus on enterprise and AI customers. Enterprise hardware costs surge The supply constraints have translated directly into sharp price increases across enterprise hardware. Samsung raised prices for 32GB DDR5 modules to $239 from $149 in September, a 60% increase, while contract pricing for DDR5 has surged more than 100%, reaching $19.50 per unit compared to around $7 earlier in 2025. DRAM prices have already risen approximately 50% year to date and are expected to climb another 30% in Q4 2025, followed by an additional 20% in early 2026, according to Counterpoint Research. The firm projected that DDR5 64GB RDIMM modules, widely used in enterprise data centers, could cost twice as much by the end of 2026 as they did in early 2025. Gartner forecast DRAM prices to increase by 47% in 2026 due to significant undersupply in both traditional and legacy DRAM markets, Chauhan said. Procurement leverage shifts to hyperscalers The pricing pressures and supply constraints are reshaping the power dynamics in enterprise procurement. For enterprise procurement, supplier size no longer guarantees stability. “As supply becomes more contested in 2026, procurement leverage will hinge less on volume and more on strategic alignment,” Rawat said. Hyperscale cloud providers secure supply through long-term commitments, capacity reservations, and direct fab investments, obtaining lower costs and assured availability. Mid-market firms rely on shorter contracts and spot sourcing, competing for residual capacity after large buyers claim priority supply.

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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

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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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