New well at BP’s giant UK oilfield ‘exceeds expectations’

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New well at BP’s giant UK oilfield ‘exceeds expectations’

Energy giant BP continues to ramp up its newest facility in the North Sea, with production from its latest well “exceeding expectations”. BP said the well on the Clair Ridge platform in the UK Continental Shelf (UKCS), achieved an “unusual level of success” by producing 12,500 barrels of oil per day, according to a report […]

Energy giant BP continues to ramp up its newest facility in the North Sea, with production from its latest well “exceeding expectations”.

BP said the well on the Clair Ridge platform in the UK Continental Shelf (UKCS), achieved an “unusual level of success” by producing 12,500 barrels of oil per day, according to a report seen by Energy Voice.

Production at Clair Ridge started in 2018 following a £4.5 billion investment by BP and its field partners including Shell, Chevron and ConocoPhillips.

The field is 47 miles (75km) west of Shetland. It is the second phase of development of Clair, which has been hailed as the largest oilfield in the UKCS.

BP discovered Clair in 1977 but did not commence production until 2005 due to the complexity of the geology presented by the find. BP estimates the field holds 7 billion barrels of hydrocarbons.

West of Shetland is technically in the Atlantic margin, but is often described as being included in the UK North Sea.

BP said the results from the B22 well represented a “global first” for its offshore team, which deployed high-pressure technology that helped to achieve “maximum productivity”.

The analysis came after BP said upstream production this year was likely to be lower than in 2024. In guidance published earlier this month in its 2024 full-year results, BP said oil production is expected to be “broadly flat” in 2025.

BP is under pressure to improve its performance – it unveiled a 36% slump in annual profits to $8.9 billion (£7.2bn) in 2024. Chief executive Murray Auchincloss has pledged to reveal a “new direction” for BP at its delayed capital markets day later this month on 26 February.

In a statement, senior vice president of BP North Sea Doris Reiter said the success of the well means it will add more drilling in the area as part of its “ramp-up” strategy, which would “offset decline” in its North Sea production.

She said: “The Clair phase one platform celebrates 20 years of production this year and Clair Ridge, the second phase of the field, continues to ramp up following start-up in 2018.

“The success of the B22 well is testament to the innovation and dedication of the Clair team, who apply advanced technologies to achieve maximum safe production.

“Building on this success, BP and our Clair field partners plan to add more wells at Clair Ridge as part of our ramp-up strategy, aiming to offset decline and unlock the full potential of this complex field.”

BP added it was still “considering options” for future development at Clair, potentially Clair phase three – although this could depend on whether or not the contested Rosebank field is allowed to go ahead.

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The AI energy challenge is coming to a head

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Ireland says there will be no computation without generation

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Data Center Tours: Iron Mountain VA-1, Manassas, Virginia

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His projections account for recent advances in AI and data center efficiency, he says. For example, the open-source AI model from Chinese company DeepSeek seems to have shown that an LLM can produce very high-quality results at a very low cost with some clever architectural changes to how the models work. These improvements are likely to be quickly replicated by other AI companies. “A lot of these companies are trying to push out more efficient models,” says Fung. “There’s a lot of effort to reduce costs and to make it more efficient.” In addition, hyperscalers are designing and building their own chips, optimized for their AI workloads. Just the accelerator market alone is projected to reach $392 billion by 2029, Dell’Oro predicts. By that time, custom accelerators will outpace commercially available accelerators such as GPUs. The deployment of dedicated AI servers also has an impact on networking, power and cooling. As a result, spending on data center physical infrastructure (DCPI) will also increase, though at a more moderate pace, growing by 14% annually to $61 billion in 2029. “DCPI deployments are a prerequisite to support AI workloads,” says Tam Dell’Oro, founder of Dell’Oro Group, in the report. The research firm raised its outlook in this area due to the fact that actual 2024 results exceeded its expectations, and demand is spreading from tier one to tier two cloud service providers. In addition, governments and tier one telecom operators are getting involved in data center expansion, making it a long-term trend.

The Future of Property Values and Power in Virginia’s Loudoun County and ‘Data Center Alley’

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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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2025 playbook for enterprise AI success, from agents to evals

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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

OpenAI’s ChatGPT explodes to 400M weekly users, with GPT-5 on the way

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI’s ChatGPT has surpassed 400 million

Medical training’s AI leap: How agentic RAG, open-weight LLMs and real-time case insights are shaping a new generation of doctors at NYU Langone

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Patient data records can be convoluted

Identity is the breaking point — get it right or zero trust fails

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

Milliseconds to breach: How patch automation closes attackers’ fastest loophole

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

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