ADNOC, XRG and Masdar have announced agreements with leading German companies, across energy, industry and advanced technology, potentially involving more than €5 billion of investment, combining the UAE’s expertise in energy and capital with Germany’s industrial and technological capabilities

The deals with RWE, Securing Energy for Europe (SEFE), MB Energy, Covestro, Siemens Energy, Siemens Industrial and Bosch Middle East span liquefied natural gas (LNG), gas, renewable energy, advanced materials and technology.

The agreements were signed during the state visit by UAE President His Highness Sheikh Mohamed bin Zayed Al Nahyan to the Federal Republic of Germany, when €40 billion in long-term investment in Germany was announced.

The agreements signed by ADNOC, XRG and Masdar include:
• ADNOC and RWE Supply & Trading GmbH Letter of Intent to progress LNG deliveries into Germany and Europe as well as Asia, supplied from ADNOC Gas’ and XRG’s growing LNG portfolio including Ruwais, Das, Rio Grande, Mozambique and Argentina, with supply commencing in the early 2030s.
• TA’ZIZ and Covestro are looking to progress a world-scale methylene diphenyl diisocyanate (MDI) value chain in Ruwais
• ADNOC, XRG and SEFE signed an agreement to explore cooperation in natural gas and LNG, spanning gas supply, infrastructure, logistics and portfolio optimisation, to support long-term energy security and market development in Europe.
• Covestro, Fertiglobe and MB Energy signed an MoU to collaborate on the development of low-carbon ammonia supply chains into Germany.
• In renewables, Masdar and RWE signed an MoU to consider joint participation in future German offshore wind auctions, while Masdar and Luxcara established a strategic partnership to explore joint investments in offshore wind and battery storage projects in Germany and wider Europe.
• ADNOC also signed Strategic Collaboration Agreements with Bosch Middle East, Siemens Energy and Siemens Industrial to explore collaboration on advanced technology and artificial intelligence.

The agreements build on existing investments by ADNOC, XRG and Masdar across Germany’s energy and industrial base. ADNOC also has 1.6 million tonnes per annum (MTPA) of long-term LNG supply agreements into the German market.

His Excellency Dr. Sultan Ahmed Al Jaber, ADNOC managing director and group CEO, Executive chairman of XRG, and Chairman of Masdar, said, “The UAE and Germany are building on decades of trusted partnership to advance economic growth and shared prosperity for the long-term. The additional €40 billion of intended long-term investments announced this week, together with the agreements ADNOC, XRG and Masdar signed today with our German partners, build on our investments across Germany’s energy and industrial landscape and mark another step forward in greater cooperation that will create new opportunities for both countries.”

In an interview with Bloomberg TV, UAE Minister of Foreign Trade Thani Al Zeyoudi said the €40 billon investment is about “reaffirming the long standing relationship and historical partnership” between the two countries.

“The 40 billion is just the beginning,” Al Zeyoudi said. “We’re going to shop around for the big opportunities that are going to bring this relationship to the next level.”

The UAE and other Gulf states are increasingly looking to broaden defense ties beyond Washington, as the Iran war drags on with no end in sight.

“The conflict is something we’re dealing with,” Al Zeyoudi said. “We’re maneuvering around the challenges and the impacts of the geopolitics and the region.”

The UAE is hoping that, by diversifying its international partners, it can pave the way to securing vital supply chains and potentially attracting production of equipment locally to bypass any export constraints, Bloomberg notes.

The IEA has once again revised down its oil demand and supply forecasts, as the stalemate in resolving the conflict in the Middle East and renewed attacks in both the Gulf and the Red Sea’s Bab el-Mandeb choke point continue to disrupt oil flows

World oil supply is now projected to average 100.7mn bpd in 2026, down 5.7mn bpd y-o-y, compared with the 4.3mn bpd forecast by the IEA a month ago, with a normalisation of supplies from Middle East producers now not expected until 2027.

Global oil production fell by 1.6mn bpd to 100.1mn bpd in August, as more than 10mn bpd of Gulf output remained shut in. Global oil stocks fell by 3.1mn bpd in August, leaving inventories at their lowest levels since 2023. Tanker costs were also up sharply, reflecting rising security risks and strong demand for ships.

OPEC+ crude production declined by 1.5mn bpd to 33.1mn bpd in August, as losses in Saudi Arabia and Iran outweighed a 980,000 bpd gain from Iraq. However output from some non-OPEC+ producers grew, particularly from the Americas.

Flows through the Strait of Hormuz averaged only 7.6mn bpd in August, 13.1mn bpd below pre-war levels, with cumulative export losses from the waterway approaching 2.8bn barrels.

Saudi Arabia hard hit

Saudi Arabia was particularly hard hit, seeing crude supply falling 2.3mn bpd to 6mn bpd in August, the lowest level in more than three decades, after Houthi-linked attacks on vessels and refineries, while Iran-backed militias in Iraq attacked the Abqaiq processing complex with drone strikes. Saudi Arabia has recently announced that the East-West pipeline has been shut as a precautionary measure, following drone attacks launched from Iraq. It is not known how long it will be until it is operational again. This could lead to a further squeeze on supply, given that the Kingdom had been able to reroute oil exports through the pipeline, which has a 7mn bpd capacity, to avoid the Strait of Hormuz.

Crude oil prices surged in September to their highest level since May, touching US$110 a barrel as hopes for a diplomatic solution to the crisis faded amid renewed attacks. After settling back slightly prices rose again following the attack on the Saudi East-West pipeline. Refined products prices have risen even more sharply, with fuels such as diesel reaching record highs, as both the Middle East conflict and Russia/Ukraine war damages oil refineries. Net diesel and gasoil exports from the Gulf and Russia were 1.6mn barrels a day lower in August than before the Middle East conflict.

Falling oil demand

Oil demand is also falling more than expected, partly ⁠due to sharp losses of petrochemical feedstocks and refined product supplies as well as record fuel prices, particularly for diesel, which are forcing consumers to cut their usage.

World oil demand will drop by 2.5mn bpd this year, the IEA predicted, more than its previous forecast of a 1.6mn bpd decline. (This is in contrast to OPEC, which still expects world oil demand to grow this year by 380,000 bpd). China has seen the biggest reduction, with oil imports, refinery activity and product deliveries significantly reduced. Demand reductions have also risen elsewhere, particularly in the Middle East as petrochemical operations and aviation have been impacted. With supplies still constrained, and commercial inventory buffers rapidly depleting, further demand reductions may be required in the coming months to close the gap, the IEA says.

Both the IEA and OPEC expect demand to rise next year; the IEA forecasts demand to rise by 2.6mn bpd in 2027 while OPEC forecasts a rise of 2.36mn bpd.

"Inventories have so far played a crucial role in balancing the market," the IEA said.

"With buffers shrinking and the global refining system stretched to the limit, the need for progress in resolving the conflict in the Middle East – and the Russia-Ukraine war, which is now in its fifth year – is greater than ever to avoid further market tightening."

Wassim Ghadban, global SVP, AI & Digital Engineering at Kent, discusses the concept of autonomous AI-driven review, combined with human-in-the-loop validation, as a means to remove structural bottlenecks in project delivery, and how the convergence of AI capabilities across engineering and operations can enable earlier production, reduce capital inefficiencies, and redefine how projects are executed

In the delivery of energy projects, the industry has long focused on optimising engineering productivity, refining project controls, and improving procurement and construction strategies. Despite these efforts, delays remain a persistent challenge. These delays are often attributed to complexity, uncertainty, or resource constraints. However, a closer examination reveals that the underlying issue is more structural in nature.

Engineering outputs are developed rapidly, supported by sophisticated tools and experienced teams. Yet, the progression of these outputs through the project lifecycle is governed by validation processes that are inherently sequential and fragmented. Documents and models move through multiple layers of review, often across different disciplines, organisations, and geographies. Each step introduces latency, not necessarily because of the effort required, but because of coordination, alignment, and the need to reconcile inconsistencies.

In this context, the constraint is not the ability to produce engineering work, but the ability to validate and integrate it efficiently.

The nature of the bottleneck

As projects evolve from conceptual design to detailed engineering and construction, the volume and granularity of deliverables increase significantly. This expansion is accompanied by a corresponding increase in interfaces between disciplines. Mechanical systems must align with structural supports, electrical systems must integrate with control architectures, and all must comply with operational and safety requirements.

Traditional review processes address this complexity through iterative, discipline-specific validation. While effective in ensuring technical integrity, these processes are limited in their ability to scale. Reviews are conducted sequentially or in loosely coordinated parallel streams, and issues identified at later stages often require revisiting earlier decisions. The result is a cycle of iteration that extends project timelines and introduces inefficiencies.

This dynamic is further compounded by the fact that review cycles are not purely technical activities. They involve communication, coordination, and decision-making across multiple stakeholders. As such, the duration of a review cycle is often driven more by process than by technical effort.

The emergence of AI in engineering contexts

Advances in AI have reached a level of maturity that allows systems to interpret, analyse, and generate engineering content with increasing accuracy. AI models can process structured and unstructured data, identify relationships between elements, and detect patterns that may not be immediately visible to human reviewers.

In practical terms, AI can now extract information from engineering documents, generate drawings based on defined parameters, and evaluate the consistency of designs across multiple domains. It can compare specifications against standards, identify deviations, and highlight potential conflicts between systems.

These capabilities extend beyond isolated tasks. AI can operate across interconnected datasets, enabling a more integrated view of engineering systems. This creates the foundation for a new approach to validation, one that is not constrained by discipline boundaries or sequential workflows.

Autonomous review with human-in-the-loop validation

The concept of autonomous AI review builds on these capabilities by applying them to the validation process itself. Rather than relying on multiple rounds of manual review, AI systems can evaluate entire engineering packages in a single pass, checking for completeness, compliance, and cross-discipline consistency.

Crucially, this approach does not eliminate the role of the engineer. Instead, it redefines it. Engineers remain responsible for final validation and decision-making, but their role shifts from conducting detailed manual reviews to assessing and confirming the outputs generated by AI. This human-in-the-loop model ensures that accountability and professional judgment are preserved, while significantly reducing the time required for validation.

The impact of this shift is not limited to efficiency. By enabling rapid, comprehensive review, it reduces the likelihood of late-stage issue discovery. Inconsistencies and conflicts can be identified earlier, when they are less costly and easier to resolve.

Implications for project execution

The introduction of autonomous review has broader implications for how projects are executed. By reducing the duration and frequency of review cycles, it allows engineering activities to progress more continuously. Dependencies between disciplines become less restrictive, as validation can occur in parallel rather than in sequence.

This creates the conditions for a more dynamic and responsive execution model. Decisions can be made earlier, with greater confidence, and adjustments can be implemented without significant disruption.

The traditional concept of rigid stage gates begins to give way to a more fluid process, in which validation is embedded within the workflow rather than applied after the fact.
The cumulative effect is a compression of the project timeline. Engineering completion is achieved sooner, procurement can be initiated earlier, and construction activities can proceed with fewer interruptions.

Enabling earlier production

For owner-operators, the most significant consequence of this transformation is the potential to achieve first production earlier than originally planned. The value of this acceleration extends beyond cost savings. It directly impacts revenue generation and the overall economic performance of the asset.

By reducing delays in engineering and execution, autonomous review contributes to a more predictable and shorter path to operational readiness. This not only improves capital efficiency but also enhances the strategic flexibility of operators, allowing them to respond more effectively to market conditions.

In this sense, the primary value of AI in project delivery is not merely in reducing costs, but in unlocking time. Time, in turn, is directly linked to value creation in the form of earlier production and extended asset utilisation.

Extending AI into operations

The same principles that enable autonomous review can be extended into the operational phase. AI systems are increasingly capable of monitoring plant conditions, interpreting sensor data, and applying predefined operational philosophies to optimise performance.

In environments where safety, efficiency, and reliability are critical, AI can support or even execute operational decisions within defined boundaries. This reduces the reliance on manual intervention and enhances the consistency of operations.

In certain scenarios, AI can effectively operate a plant by following established control strategies and responding to real-time conditions. While human oversight remains essential, the role of operators evolves toward supervision and exception management.

Conclusion

Autonomous AI review, combined with human-in-the-loop validation, offers a practical pathway to addressing one of the most persistent challenges in project delivery. By transforming how engineering outputs are validated and integrated, it removes a key constraint that has historically limited project performance.

The resulting benefits extend beyond efficiency gains. They include earlier production, improved capital utilisation, and a more responsive and resilient execution model. When combined with AI-enabled operations, this approach forms the foundation of a new paradigm in which projects are not only digitally enabled but increasingly autonomous.

The future of project delivery will not be defined by how quickly engineering work can be produced, but by how effectively it can be validated, integrated, and executed. In this context, AI is not simply a tool for optimisation, but a catalyst for fundamental change.