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Rystad Expects Lower Upstream M&A Activity in 2026

In a market update sent to Rigzone by the Rystad Energy team recently, Rystad noted that global upstream merger and acquisition (M&A) activity is expected to be lower in 2026 than in 2025. Rystad highlighted in the update that, according to its analysis, “nearly $152 billion worth of opportunities [are] on the market as of […]

In a market update sent to Rigzone by the Rystad Energy team recently, Rystad noted that global upstream merger and acquisition (M&A) activity is expected to be lower in 2026 than in 2025.

Rystad highlighted in the update that, according to its analysis, “nearly $152 billion worth of opportunities [are] on the market as of January this year”. The company added that “timing and execution will determine whether several mega deals will go through, with numerous high value assets still on the market waiting for the right buyers”.

According to a chart included in the update, which showed annual upstream M&A activity by continent and deal count, global upstream M&A deal value came in at $170 billion in 2025, $204 billion in 2024, $255 billion in 2023, $152 billion in 2022, $184 billion in 2021, $103 billion in 2020, and $154 billion in 2019.

This chart highlighted that it excluded “government mandated deals and production sharing contract awards/expiry”.

Rystad noted in its update that global upstream M&A activity “dipped 17 percent year on year to approximately $170 billion in 2025, with deal count decreasing 12 percent to 466”.

“Consolidation within North American shale plays, LNG investments across U.S. and Argentina, and majors’ spinning off assets in Asia and the UK to form new regional joint ventures emerged as key themes last year,” Rystad said.

“A few key deals across these themes include SM Energy and Civitas’ merger, Cenovus Energy’s acquisition of MEG Energy, a Blackstone-led consortium’s acquisition of a 49.9 percent stake in the Port Arthur LNG phase 2 project from Sempra Infrastructure Partners (SIP), Eni and Petronas merging certain assets in Indonesia and Malaysia, and TotalEnergies merging its UK operations with NeoNext Energy to form NeoNext+,” it added.

In the update, Rystad highlighted that North America led activity in 2025, “accounting for more than $112 billion, or 66 percent, of total deal value”.

The company said a “similar regression was seen across most regions”, noting that deal value in Africa dropped 57 percent year on year to $6 billion and that Europe saw a 24 percent year on year decrease to around $10 billion. The Middle East recorded a 65 percent fall to nearly $4 billion, Rystad said in the update, adding that Oceania saw a 96 percent drop to around $435 million and that Russia observed a 25 percent decrease to nearly $750 million.

“This overall global decline is primarily attributed to low and volatile oil prices during 2025 that had a lasting negative impact on the buyer-seller spread,” Rystad said in the update.

“Brent prices declined from around $79 per barrel in January last year to around $65 per barrel in May, surged back to more than $70 per barrel in June and July, and finally settled around $63 per barrel by the end of December,” it highlighted.

“During the same time, West Texas Intermediate (WTI) prices started last year at $75 per barrel and settled around $58 per barrel in December,” it continued.

Rystad went on to point out in the update that M&A activity only increased in Asia and South America.

“Deal value in Asia more than tripled to $18 billion, driven by the Eni and Petronas joint-venture formation, while deal value in South America increased 71 percent year on year to $18.3 billion on the back of several LNG and Vaca Muerta focused deals in Argentina,” it said.

In the update, Atul Raina, Rystad Energy Vice President, Oil and Gas M&A, said, “Rystad Energy expects North America to remain the clear anchor for upstream M&A activity in 2026, with deal flow increasingly shaped by a new phase of a ‘merger of equals’ consolidation among small- and mid-cap listed U.S. shale producers”.

“This is further supported by ample private E&P capital yet to be deployed, ongoing consolidation in Canada’s Montney shale, and rising interest in gas and LNG-linked assets – particularly from Asian buyers seeking long-term security of supply,” Raina added.

By contrast, international M&A outlook remains uneven, Raina said in the update.

“While the pipeline of opportunities is sizeable, activity is concentrated around a small number of high-value and often complex transactions, limiting broader deal momentum,” Raina added.

“National oil companies from the Middle East, Asia, and South America are likely to be among more active participants, reflecting their continued appetite for scale and international exposure at a time when many IOCs remain selective,” Raina continued.

In a statement sent to Rigzone on Wednesday by Enverus, Enverus subsidiary Enverus Intelligence Research (EIR) stated that, “after a midyear slowdown, U.S. upstream M&A regained momentum in 4Q25, closing with $23.5 billion in announced deals and pushing full-year 2025 activity to $65 billion”.

“The rebound reflects a deeper bench of motivated buyers including refunded private equity teams, increased use of securitized financing, and new international entrants all competing for scarce assets,” EIR added in the statement.

In a statement sent to Rigzone back in October 2025, EIR noted that, “after a hot start to the year”, U.S. upstream mergers and acquisitions “slid into a slump in the third quarter, with deal value dropping to $9.7 billion, marking the third straight quarterly decline”.

“Persistently low crude prices have kept many buyers on the bench, particularly for oil-weighted private equity-backed oil and gas exits that fueled much of the activity in the recent past,” EIR added in that statement.

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Crooks are hijacking and reselling AI infrastructure: Report

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Rystad Expects Lower Upstream M&A Activity in 2026

In a market update sent to Rigzone by the Rystad Energy team recently, Rystad noted that global upstream merger and acquisition (M&A) activity is expected to be lower in 2026 than in 2025. Rystad highlighted in the update that, according to its analysis, “nearly $152 billion worth of opportunities [are] on the market as of January this year”. The company added that “timing and execution will determine whether several mega deals will go through, with numerous high value assets still on the market waiting for the right buyers”. According to a chart included in the update, which showed annual upstream M&A activity by continent and deal count, global upstream M&A deal value came in at $170 billion in 2025, $204 billion in 2024, $255 billion in 2023, $152 billion in 2022, $184 billion in 2021, $103 billion in 2020, and $154 billion in 2019. This chart highlighted that it excluded “government mandated deals and production sharing contract awards/expiry”. Rystad noted in its update that global upstream M&A activity “dipped 17 percent year on year to approximately $170 billion in 2025, with deal count decreasing 12 percent to 466”. “Consolidation within North American shale plays, LNG investments across U.S. and Argentina, and majors’ spinning off assets in Asia and the UK to form new regional joint ventures emerged as key themes last year,” Rystad said. “A few key deals across these themes include SM Energy and Civitas’ merger, Cenovus Energy’s acquisition of MEG Energy, a Blackstone-led consortium’s acquisition of a 49.9 percent stake in the Port Arthur LNG phase 2 project from Sempra Infrastructure Partners (SIP), Eni and Petronas merging certain assets in Indonesia and Malaysia, and TotalEnergies merging its UK operations with NeoNext Energy to form NeoNext+,” it added. In the update, Rystad highlighted that North America led activity in

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ORLEN Signs Up for More Renewable Hydrogen from Finland

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Denmark, Germany Ink Agreement on Bornholm Energy Island

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China clears Nvidia H200 sales to tech giants, reshaping AI data center plans

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Nuclear safety rules quietly rewritten to favor AI

‘Referee now plays for the home team’ Kimball pointed out that while an SMR works on the same principle as a large-scale nuclear plant, using controlled fission to generate heat which is then converted to electricity, its design reduces environmental impacts such as groundwater contamination, water use, and the impact in the event of failure. For example, he said, the integral reactor design in an SMR, with all components in a single vessel, eliminates external piping. This means that accidents would be self-contained, reducing the environmental impact. In addition, he said, SMRs can be air-cooled, which greatly reduces the amount of water required. “These are just a couple of examples of how an SMR differs from the large industrial nuclear power plants we think of when we think of nuclear power.”  Because of differences like this, said Kimball, “I can see where rules generated/strengthened in the post-Three Mile Island era might need to be revisited for this new nuclear era. But it is really difficult to speak to how ‘loose’ these rules have become, and whether distinctions between SMRs and large-scale nuclear plants comprise the majority of the changes reported.” Finally, he said, “I don’t think I need to spend too many words on articulating the value of nuclear to the hyperscale or AI data center. The era of the gigawatt datacenter is upon us, and the traditional means of generating power can’t support this insatiable demand. But we have to ensure we deploy power infrastructure, such as SMRs, in a responsible, ethical, and safe manner.”  Further to that, Gogia pointed out that for CIOs and infrastructure architects, the risks extend well beyond potential radiation leaks. “What matters more immediately is that system anomalies — mechanical, thermal, software-related — may not be documented, investigated, or escalated with the diligence one would expect from

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Mplify launches AI-focused Carrier Ethernet certifications

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Gauging the real impact of AI agents

That creates the primary network issue for AI agents, which is dealing with implicit and creeping data. There’s a singular important difference between an AI agent component and an ordinary software component. Software is explicit in its use of data. The programming includes data identification. AI is implicit in its data use; the model was trained on data, and there may well be some API linkage to databases that aren’t obvious to the user of the model. It’s also often true that when an agentic component is used, it’s determined that additional data resources are needed. Are all these resources in the same place? Probably not. The enterprises with the most experience with AI agents say it would be smart to expect some data center network upgrades to link agents to databases, and if the agents are distributed away from the data center, it may be necessary to improve the agent sites’ connection to the corporate VPN. As agents evolve into real-time applications, this requires they also be proximate to the real-time system they support (a factory or warehouse), so the data center, the users, and any real-time process pieces all pull at the source of hosting to optimize latency. Obviously, they can’t all be moved into one place, so the network has to make a broad and efficient set of connections. That efficiency demands QoS guarantees on latency as well as on availability. It’s in the area of availability, with a secondary focus on QoS attributes like latency, that the most agent-experienced enterprises see potential new service opportunities. Right now, these tend to exist within a fairly small circle—a plant, a campus, perhaps a city or town—but over time, key enterprises say that their new-service interest could span a metro area. They point out that the real-time edge applications

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Photonic chip vendor snags Gates investment

“Moore’s Law is slowing, but AI can’t afford to wait. Our breakthrough in photonics unlocks an entirely new dimension of scaling, by packing massive optical parallelism on a single chip,” said Patrick Bowen, CEO of Neurophos. “This physics-level shift means both efficiency and raw speed improve as we scale up, breaking free from the power walls that constrain traditional GPUs.” The new funding includes investments from Microsoft’s investment fund M12 that will help speed up delivery of Neurophos’ first integrated photonic compute system, including datacenter-ready OPU modules. Neurophos is not the only company exploring this field. Last April, Lightmatter announced the launch of photonic chips to tackle data center bottlenecks, And in 2024, IBM said its researchers were exploring optical chips and developing a prototype in this area.

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Intel wrestling with CPU supply shortage

“We have important customers in the data center side. We have important OEM customers on both data center and client and that needs to be our priority to get the limited supply we have to those customers,” he added. CEO Lip-Bu Tan added that the continuing proliferation and diversification of AI workloads is placing significant capacity constraints on traditional and new hardware infrastructure, reinforcing the growing and essential role CPUs play in the AI era. Because of this, Intel decided to simplify its server road map, focusing resources on the 16-channel Diamond Rapids product and accelerate the introduction of Coral Rapids. Intel had removed multithreading from diamond Rapids, presumably to get rid of the performance bottlenecks. With each core running two threads, they often competed for resources. That’s why, for example, Ampere does not use threading but instead applies many more cores per CPU. With Coral Rapids, Intel is not only reintroducing multi-threading back into our data center road map but working closely with Nvidia to build a custom Xeon fully integrated with their NVLink technology to Build the tighter connection between Intel Xeon processors and Nvidia GPUs. Another aspect impacting supply has been yields or the new 18A process node. Tan said he was disappointed that the company could not fully meet the demand of the markets, and that while yields are in line with internal plans, “they’re still below where I want them to be,” Tan said.  Tan said yields for 18A are improving month-over-month and Intel is targeting a 7% to 8% improvement each month.

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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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