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2025 should mark a turning point in how the oil and gas industry uses AI.
Over the past three years, AI has significantly advanced the use of digital twins in the oil and gas industry, moving from basic monitoring tools to sophisticated, predictive, and autonomous systems — from using generative AI to analyse subsurface data, applying natural language processing to documentation, to training deep learning models for predictive maintenance. While grouped under the banner of AI, these initiatives vary widely in technology, potential and maturity.
As the industry’s economic outlook grows cloudier amid tariffs, weakening demand, and falling crude prices, the coming period may bring a winnowing of these efforts. It should also prompt greater focus on use cases that are production-ready, realise value from past investments and are closely aligned with organisational goals.
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One such use case is the use of AI to expand the capabilities and value drive from digital twins. In a recent survey by Hexagon, 47% of oil and gas executives say they plan to add AI functionality to their twins —one of the highest percentages among all industries surveyed.
A Legacy of Limited-Function Digital Twins
Understanding this appetite for greater AI functionality and the obstacle to overcome requires a short detour by the industry’s past experience with digital twins.
While they have been used in the oil and gas industry for decades, digital twins were often designed with a relatively narrow focus on monitoring, maintaining and simulating individual physical assets or major equipment. In this scenario, the digital twin typically monitors equipment health and performance, predicts failures and helps schedule repairs proactively.
This approach has proven successful in helping operators avoid costly downtime and safety issues. In fact, oil and gas is the industry where executives provide the highest estimation of the ROI of their digital twins, with a projected 29% return on investment each year, well ahead of other sectors such as chemicals (24%) or general manufacturing (19%).
Solving the Challenge of Accurate, Up-to-Date Data
However, this narrow focus on individual equipment or subsystem use cases was also the reflection of data challenges.
Oil and gas facilities produce vast volumes of data and documents that are challenging to consolidate and contextualise. Those challenges are still a top-of-mind priority today. As an example, 65% of refinery executives name data cleansing and standardisation as a top priority over the next 24 months, according to IDC.
But AI is playing a critical enabling role in ensuring timely and reliable data availability. For example, continuous services can now contextualise legacy unstructured data, digitise and extract tags from documents such as P&IDs, other drawings, and technical documents such as datasheets. It is also transforming the integration and contextualisation of 3D laser scans, bringing new possibilities to the spatial visualisation of information.
The result is a new depth, scale and interconnectedness with which digital twins can be implemented.
Harbour Energy, a major European oil and gas producer demonstrated this at scale. Within three months, the company digitised, cleansed and classified over five million control documents—tagged with metadata aligned to asset taxonomies. At the same time, it integrated over 20,000 laser scan points into its visualisation environment. This unlocked information that had been inaccessible for years and could now be leveraged to maintain compliance and optimise asset performance.
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Contextualisation as a Crucial Success Factor
This contextualisation is a key enabler for both human operators and AI tools.
When discussing with operations and maintenance professionals, they often report significant time lost searching for information. Yet many oil and gas companies have faced challenges implementing ChatGPT-style natural language agents due to issues with accuracy, reliability and veracity of insights.
But, when these agents access pre-contextualised data, they can navigate industrial data much more effectively, which significantly improves the quality and reliability of their responses establishing them as effective and dependable tools for practical use. With this data foundation, Generative AI learns how data points are interconnected. For example, this enables users to easily request all documents and data points related to a specific tag, compare data across multiple assets, and more.
Expanding the Capabilities of AI-Powered Digital Twins
The applications of AI-powered digital twins extend well beyond simple information queries. Consider an offshore oil and gas operator using a digital twin to optimise operations, reduce downtime, and enhance safety on a deepwater platform. The asset, which changed ownership three times, relies on multiple systems for process safety and asset integrity.
The operator was able to expand its AI capabilities across maintenance, process optimisation, remote monitoring, and compliance.
In predictive maintenance, the twin uses machine learning to analyse vibration and temperature trends from gas compressors and pumps. When early signs of bearing wear are detected, the system predicts failures up to two weeks in advance—allowing maintenance teams to act proactively and avoid costly shutdowns.
Beyond cost avoidance, AI drives revenue growth. The twin simulates operational scenarios and adjusts production parameters in real time—optimising separator pressures and flow rates to improve efficiency, reduce energy use, and maximise output.
For remote monitoring, offshore and onshore teams access the digital twin through a cloud-based platform. Engineers use natural language search and 3D visualisations to diagnose issues and simulate solutions.
In safety and compliance, the digital twin continuously monitors gas leaks, emissions, and safety-critical systems. AI detects anomalies near the flare stack and automatically triggers response protocols, enhancing regulatory compliance and minimising environmental risk.
Capabilities such as these show how the growth in AI-powered twins can transform oil and gas operations—delivering asset reliability, cost optimisation, revenue growth, and the agility and resilience the industry needs in volatile times.
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