[Editor's Note: Zainab Iyiola is a member of the TWA Editorial Board and the author of previous TWA articles.]
Artificial intelligence (AI) has become one of the most visible signs of digital transformation in the oil and gas industry. Across exploration, drilling, production, facilities management, and emissions monitoring, AI is increasingly being used to identify patterns, predict failures, automate decisions, and improve operational efficiency. This shift is part of a broader move toward intelligent operations, where AI, automation, and data analytics are used to improve decision-making across oil and gas assets (JPT 2025). For an industry under growing pressure to provide reliable energy while reducing its environmental footprint, the promise is attractive: smarter systems, fewer leaks, lower costs, safer operations, and better use of existing assets.
But there is a question the industry must ask more honestly: Is AI helping the oil and gas industry become cleaner, or is it simply adding another layer of complexity to already complex operations?
The answer depends less on the sophistication of the algorithm and more on the quality of the problem being solved. AI can support cleaner oil and gas operations, but only when it is tied to measurable outcomes, such as reduced methane emissions, lower energy consumption, improved equipment reliability, better carbon accounting, and safer infrastructure decisions. Without that link, AI risks becoming another digital buzzword that looks impressive on dashboards but produces limited sustainability value.
Where AI Can Make a Difference
One of the strongest sustainability use cases for AI in oil and gas is methane detection and management. Methane is a major climate concern because it has a stronger warming effect than carbon dioxide over the short term. According to the International Energy Agency (IEA), methane is responsible for around 30% of the rise in global temperatures since the Industrial Revolution, and the energy sector accounts for more than 35% of methane emissions from human activity (IEA, 2025a). Within the energy sector, fossil fuel operations remain a major source, with annual methane emissions from fossil fuels remaining above 120 million tonnes in recent years (IEA, 2025b). The US Environmental Protection Agency (EPA) has also identified the oil and gas sector as one of the largest industrial sources of methane emissions, reinforcing why methane detection and abatement remain central to cleaner operations (EPA, 2024). Because methane emissions are often intermittent, dispersed, and difficult to detect through manual inspection alone, researchers have increasingly explored AI-based approaches for leak detection and emissions monitoring.
For example, Wang et al. (2020) developed a computer vision approach using infrared optical gas imaging videos to automatically detect methane leaks from natural gas equipment. More recently, Rouet-Leduc and Hulbert (2024) used deep learning to detect methane plumes in multispectral satellite imagery, showing how AI can help identify emission sources across large geographic areas. These studies show that AI is already moving beyond theory and is being applied to real methane-detection challenges in oil and gas operations. This is where AI can be practical rather than theoretical.
Satellite missions and commercial monitoring platforms illustrate both the potential and the practical challenges of methane monitoring. MethaneSAT operated from 2024 to 2025 with an initial focus on measuring methane emissions from oil and gas regions. Although contact with the satellite was lost in June 2025 and it is considered unlikely to be recovered (MethaneSAT 2025), the mission demonstrated how advanced sensing, data processing, and analytical tools could identify and quantify methane emissions across wide areas. The data and algorithms developed through the program continue to support methane analysis and emissions-reduction efforts.
As a current operational example, GHGSat provides satellite-based and aerial remote-sensing services that help operators identify and quantify methane emissions across individual facilities and wider production regions. At the operator level, AI-enabled drones and optical gas imaging systems are also being tested in regions such as the Permian Basin to detect methane leaks, provide geolocation data, and support faster repair decisions. These examples show how AI can help process large volumes of methane-related data, detect anomalies, prioritize likely leak sources, and move emissions monitoring from a periodic compliance exercise toward a more continuous operational practice.
AI can also support predictive maintenance. Equipment failures do not only create production losses. They can also lead to flaring, venting, leaks, safety incidents, and inefficient energy use. By analyzing vibration data, pressure trends, temperature changes, flow rates, and maintenance history, machine-learning models can help identify early warning signs before failures occur. In this sense, AI contributes to sustainability not by replacing engineering judgment, but by helping engineers act earlier and with better information.
Another important area is production optimization. Many oil and gas assets operate under changing reservoir, facility, and market conditions. AI-enabled optimization can help operators reduce unnecessary energy use, improve lift performance, minimize downtime, and optimize chemical injection. These improvements may appear small at the equipment or well level, but across a large asset base, incremental efficiency gains can translate into meaningful reductions in fuel use and emissions intensity.
AI is also becoming relevant to carbon management. Carbon capture and storage (CCS) projects require careful site screening, reservoir characterization, plume monitoring, pressure management, and long-term risk assessment. Data-driven models can help integrate subsurface, operational, and monitoring data to support better decisions. As CCS moves from demonstration projects toward larger-scale deployment, the ability to manage complex data sets will become increasingly important.
Efficiency Is Not Automatically Sustainability
Still, the industry should be careful not to confuse efficiency with sustainability. An AI model that increases production may improve economics, but that does not automatically mean it reduces environmental impact. Similarly, a dashboard that tracks emissions does not reduce emissions unless it leads to action.
This distinction matters because digital transformation is often presented as inherently sustainable. In practice, the sustainability value of AI depends on what operators choose to measure, optimize, and reward. If the model is designed to maximize only production rate, then AI will optimize production rate. If the objective includes emissions intensity, energy use, equipment health, water handling, safety risk, and operational reliability, then AI can support a broader sustainability agenda.
In other words, cleaner oil and gas will not come from AI alone. It will come from engineers, data scientists, operators, and decision-makers defining better goals for AI systems.
The Hidden Footprint of AI
There is also a second side to the discussion: AI itself has an energy footprint. The IEA projects that global electricity consumption from data centers could more than double to around 945 TWh by 2030, as seen in Fig. 1, representing just under 3% of total global electricity consumption in its base case (IEA, 2025c). Put simply, the same digital infrastructure that makes AI useful also requires significant electricity for data processing, storage, and computing power.
For oil and gas companies, this does not mean AI should be avoided. Rather, it means AI should be used responsibly. Not every operational problem requires a large or complex model. In some cases, simpler statistical tools, physics-based models, or hybrid approaches may provide more-transparent and energy-efficient solutions. The best digital solution is not always the most complicated one. It is the one that is accurate enough, interpretable enough, and useful enough to improve decisions.
This is where engineering judgment remains essential. AI should complement domain expertise, not hide the lack of it. A model that predicts a methane leak, corrosion risk, or equipment failure must still be interpreted within the realities of field operations, sensor limitations, maintenance constraints, and economic tradeoffs.
From Digital Adoption to Measurable Impact
If efficiency alone is not enough, and AI itself has an energy footprint, then the next question is practical: How can companies make sure AI delivers measurable value? For AI to truly support cleaner oil and gas, companies need to move from digital adoption to measurable impact. This means asking clearer questions before deploying AI tools.
- Which emissions source is this model helping to reduce?
- What operational decision will change because of this prediction?
- How will the result be validated in the field?
- Can the model explain its recommendation clearly enough for engineers to trust it?
- Will the tool reduce waste, improve safety, lower emissions, or simply create another dashboard?
These questions shift the discussion from whether AI is impressive to whether it is useful, accountable, and connected to field-level action.
A useful example is methane abatement. The IEA has repeatedly emphasized that many methane-reduction options in oil and gas are already available, including leak detection and repair, equipment replacement, vapor recovery units, and better use of associated gas (IEA, 2025d). AI can strengthen these efforts by improving detection, prioritization, and response, but it cannot replace the physical work of repairing leaks, upgrading equipment, or changing operational practices.
The same logic applies to flaring reduction, produced-water management, drilling optimization, and carbon storage monitoring. AI is most valuable when it is connected to action.
What This Means for Young Professionals
For young professionals, the rise of AI in oil and gas creates both opportunity and responsibility. The opportunity is clear: The industry needs people who can work across disciplines. Petroleum engineers who understand data science, data scientists who understand field operations, and sustainability professionals who understand engineering constraints will be increasingly valuable. However, the conversation should not be framed only as moving away from oil and gas in the name of sustainability. Oil and gas will continue to play a role in meeting global energy demand, so the more practical question is how the industry can operate more responsibly while improving reliability, safety, and environmental performance. This is where digital technologies matter. AI can help operators detect methane leaks earlier, reduce unplanned downtime, optimize energy use, improve maintenance planning, and make emissions data more actionable.
The responsibility for young professionals is therefore not simply to accept every digital tool as progress, but to ask better questions. What problem does the tool solve? What data does it use? What assumptions does it make? How does it affect field decisions? Does it support measurable environmental performance? The future of oil and gas will not be shaped only by those who can code models or build dashboards. It will be shaped by those who can connect technology to responsible operations.
Conclusion
AI can help the oil and gas industry become cleaner, but only if the industry uses it with discipline. It can improve methane detection, predictive maintenance, production efficiency, carbon management, and operational decision-making. However, AI is not automatically sustainable. It consumes energy, depends on data quality, and can create complexity when deployed without clear objectives.
The real question is not whether the industry should use AI. It already is. The better question is whether AI is being used to solve the problems that matter most.
Cleaner oil and gas will require more than algorithms. It will require measurable targets, reliable data, field implementation, engineering judgment, and professionals who understand that digital transformation is only valuable when it leads to better decisions. For the next generation of oil and gas professionals, that may be the most important lesson: AI should not just make operations smarter. It should help make them more responsible.
For Further Reading
Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera by J. Wang, L. Tchapmi, and A. Ravikumar, Stanford University; et al.
Automatic Detection of Methane Emissions in Multispectral Satellite Imagery Using a Vision Transformer by B. Rouet-Leduc, Kyoto University, and C. Hulbert, Geolabe.
