The collaboration will combine Weatherford's software and hardware solutions with AIQ's AI-driven systems. (Image source: AIQ)

In an era of burgeoning partnerships between energy companies and tech companies, AIQ and Weatherford have signed a strategic collaboration agreement to combine Weatherford's software and hardware solutions with AIQ's AI-driven systems

The combination of these advanced tools will enable operators to optimise their production workflows, reduce downtime, and significantly enhance operational efficiency across global energy facilities.

By combining AIQ's AI technology with Weatherford's Modern Edge, customers will be able to modernise their edge operations, facilitate autonomous production, and offer the flexibility to expand operations, while optimising resource usage and reducing costs. Weatherford’s Universal Normalizer will work in tandem with AIQ’s capabilities to harmonise multi-asset data, combining operational and financial analysis into a unified, API-supported data model, driving smarter decision-aming and streamline operations, while the WFRD Software Launchpad will allow customers to procure all of their software needs via a comprehensive industrial SaaS platform. This eliminates the complexity of managing multiple systems and vendors, providing a single point of access for all Weatherford and partner-built applications, while ensuring data security and autonomy within their own network.

Magzhan Kenesbai, acting managing director of AIQ said, “This partnership marks another step in AIQ’s mission to build partnerships that accelerate the deployment of impactful AI systems across the energy value chain. By integrating our advanced AI-driven tools with Weatherford's energy-specific technology, we are delivering greater efficiencies to the industry through the development of scalable, automated applications. Together, we are set to empower operators to optimise their workflows, reduce downtime, and achieve unparalleled operational excellence.”

The collaboration is the latest in a series of AIQ tie-ups with companies including SLB, Halliburton and Kent.

AI has applications throughout the value chain in the oil and gas sector. (Image source: Adobe Stock)

Artificial intelligence (AI) has the potential to transform the energy sector in the coming decade, boosting electricity demand from data centres around the world while also unlocking opportunities to cut costs, enhance competitiveness and reduce emissions, according to a new report from the IEA

The IEA’s special report Energy and AI argues that, while the increase in electricity demand for data centres is set to drive up emissions, this increase will be small in the context of the overall energy sector and could potentially be offset by emissions reductions enabled by AI if adoption of the technology is widespread. Additionally, as AI becomes increasingly integral to scientific discovery, the report finds that it could accelerate innovation in energy technologies such as batteries and solar PV.

“With the rise of AI, the energy sector is at the forefront of one of the most important technological revolutions of our time,” said Dr Fatih Birol, IEA executive director. “AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments and companies – how we use it.”

AI applications in oil and gas supply can help play a role in energy transitions by ensuring that sufficient supplies are available at lower cost and with lower emissions, the IEA says.

Early adopters

The report notes that oil and gas companies have been among the earliest adopters of new technologies to boost exploration and production. In 2000, 11 supercomputers operated by oil and gas companies ranked among the world’s 500 fastest. By 2024, this number had increased to 24, and total computing capacity has grown at almost 70% annually, outpacing the broader supercomputing industry. Companies including TotalEnergies, Petrobras and Aramco are developing new supercomputer capabilities for applications across exploration and production, operations and safety and emissions management; Eni’s latest supercomputer is currently the fifth fastest in the world.

Oil and gas companies are also investing and partnering with AI experts to develop bespoke tools for their industry, with ADNOC announcing the completion of a trial of an AI agent based on a 70bn-parameter large language model that is reported to have improved the accuracy of seismic processing by 70%, along with other improvements.

Various applications

AI has various applications in the sector, the report notes, including for subsurface data processing, reservoir simulation, remote operations, predictive maintenance, regulatory compliance, leak detection and  automation.

In exploration and development, the use of AI in seismic processing improves interpretation and image quality and makes it up to 90% better at classification. It can also help to determine where precisely to drill production wells. AI can also enhance the accuracy and speed of processes for reservoir simulation models. The use of deep learning algorithms allows faster loading and processing of large volumes of data from multiple sources, which are entered into simulation models. Physics-informed machine learning has enhanced the ability to model more complex reservoir behaviour.

In the realm of operations and safety, various AI and machine learning techniques are being applied to production forecasting. Recently, Exxon-Mobil’s AI-powered demand forecasting model was reported to have reduced forecast errors by 25%. The use of AI can also allow operations, monitoring and control to be carried out remotely. A typical oil platform hosts tens of thousands of sensors, generating terabytes of data. Analysing and leveraging this data from a centralised remote location can increase efficiency and safety and reduce the costs of operations. Cloud computing facilitates the remote analysis of datasets, remote operational decisions and the creation of digital twins.

In terms of cost reduction, AI-led interventions could reduce the costs of finding, developing and operating a new deepwater offshore project by up to 10%, the IEA estimates.

In the area of emissions reduction, AI is being deployed to boost data processing techniques to detect and quantify emissions. For example, automated AI-driven methane emitter monitoring systems using two satellites were recently deployed at the International Methane Emissions Observatory’s Methane Alert and Response System, the IEA notes.

A particularly promising area is in rapidly detecting fugitive emissions, which comprise around 20% of methane emissions from oil and gas operations. These leaks can usually be repaired quickly once found. Leak detection and repair programmes, involving optical gas imaging cameras or the use of airborne and satellite observations can be enhanced with AI, allowing large amounts of data collected to be processed much more quickly.

Another important possible deployment of AI is to improve the planning of CCUS projects; by enhancing reservoir models, additional computing power and AI can provide more certainty around the efficacy and costs of long-term CO2 storage, the IEA notes.

https://www.iea.org/reports/energy-and-ai

The WiNG DFU-3C acquires the most comprehensive and high-definition data for outstanding imaging, characterisation and monitoring of the subsurface. (Image source: Sercel)

Sercel has launched the WiNG DFU-3C, a three-component version of its field-proven WiNG land seismic nodal solution

WiNG is a fully integrated nodal land acquisition system designed with a single data collection platform to manage operations more easily and efficiently.

The new integrated three-component node acquires the most comprehensive and high-definition data for outstanding imaging, characterisation and monitoring of the subsurface, addressing the growing need for high-end seismic applications for the energy and mineral E&P sectors. As part of the WiNG range, it comes complete with unique advanced features as standard, such as the ultra-sensitive QuietSeis broadband digital sensor and Pathfinder transmission management technology. Combined with its market-leading compact and lightweight design, the DFU-3C offers unprecedented precision, efficiency and portability.

Jérôme Denigot, CEO, Sercel said, "The WiNG DFU-3C is an excellent example of Sercel’s commitment to providing innovative and high-performance solutions to our customers. Building on the success of our widely used WiNG single-component node, this three-component version brings greater survey accuracy and flexibility. Its vector fidelity, sensor stability and low-frequency capabilities make the WiNG DFU-3C ideal for the most demanding E&P subsurface challenges, meeting the needs of our customers in both the energy and mining markets."

The collaboration with Gulf will bolster ENERGYai with deep industry insights. (Image source: AIQ)

Artificial intelligence provider for the energy sector, AIQ, has initiated a strategic collaboration agreement with Gulf Energy Information to integrate ENERGYai with Gulf’s vast energy sector data assets 

ENERGYai is a first-of-its-kind agentic AI solution for the energy sector, developed in partnership with ADNOC and in collaboration with G42 and Microsoft. In March 2025, AIQ announced a landmark US$340 million contract with ADNOC to deploy ENERGYai and associated AI solutions across ADNOC’s upstream value chain.

The collaboration with Gulf will bolster ENERGYai’s AI-powered agents and large language model (LLM) with deep industry insights, with Gulf providing AIQ with exclusive access to its proprietary datasets, and industry-leading documents. This will ensure ENERGYai is trained on the most relevant and high-quality information available, enhancing the solution’s ability to interpret complex energy sector challenges, optimize workflows, and generate actionable intelligence.

Commenting on the agreement with Gulf, Magzhan Kenesbai, acting managing director of AIQ said, “ENERGYai is designed to revolutionise how AI supports decision-making and automation across ADNOC’s value chain. The reliability and impact of our models is directly related to the nature and quality of the data we train them on, and with Gulf’s extensive project and industry intelligence, we are set to further reinforce ENERGYai’s capabilities.”

John T Royall, president & CEO of Gulf Energy Information said, “By combining AIQ’s AI expertise with Gulf’s trusted energy sector data, we are shaping a smarter, more informed AI ecosystem. This partnership reflects a shared vision to leverage AI for more efficient decision-making, greater sustainability, and enhanced operational intelligence in the energy industry.”

ENERGYai is a key enabler of ADNOC’s digital transformation with the solution’s AI-powered agents already trained on petabytes of operational data from ADNOC, and possessing the ability to perceive, learn, think, and act. By integrating more industry and domain-specific data, AIQ will further strengthen ENERGYai’s ability to drive intelligent automation, predictive analytics, and transformative efficiencies in the energy landscape.