The midstream phase of energy operations is all about storing and processing gas and oil. With AI on the rise across the industry, there are numerous opportunities for energy firms to use this technology even further.
But what are the applications for AI in the midstream energy sector today? And what long-term impact is it sure to have?
A timeline of AI in midstream energy
Artificial intelligence didn’t start with ChatGPT and similar Large Language Models, though it’s undeniable that they’ve transformed AI as a whole.
Similar automation systems have actually been in place across the energy industry for over ten years. Companies like CruxOCM, VarIntel, and Imubit, however, are taking this to the next level.
Here’s a quick breakdown of how the midstream energy sector has used AI in the 21st century:
- The 2000s: SCADA systems became widespread in pipelines, and companies started to use rule-based automation, but with minimal actual AI development.
- The 2010s: Midstream operators began introducing machine learning models to identify anomalies; this is also when IoT sensors started to gain popularity.
- The 2020s: The sudden rise in LLMs has led to domain-specific AI models across the oil and gas industry, along with real-time digital twins and simulations.
The impact of AI on midstream energy
AI’s market size in the global gas and oil industry was $3.14 billion in 2024 and is predicted to more than double to $6.7 billion by 2029. The sector is readily adopting its potential, even with fears that the “AI bubble” might soon burst.
Shell’s head of AI, Amy Challen, has warned that AI implementation is more complex than many believe, noting “frontline processes will need to change” first. AI can only have a positive impact if companies change their strategies.
Ongoing midstream energy AI trends
AI has already found a home in the midstream energy sector, and certain topics come up again and again when looking at the industry’s major solutions. Each one serves as a good case study of AI’s effects on oil and gas as a whole.
The biggest AI trends that the midstream energy sector seems to be adopting include:
Digital twins
A digital twin is effectively a virtual facsimile of an existing piece of equipment, usually a pipeline or compressor/pumping station. They’ve been present in the sector for over a decade, but AI’s recent growth has made them even better.
There are two major advances to be aware of: real-time data ingestion and edge integration.
Real-time data ingestion relies on legacy IoT and SCADA sensors, which feed their data to your digital twin and flag any anomalies. For example, if it spots a drop in a pipeline’s pressure, it can run simulations to find the cause.
Edge integration, meanwhile, combines digital twins with edge computing, meaning it doesn’t send the data to a centralized server. This improves the software’s response times, which could be crucial in an emergency.
Predictive analytics
This gives companies comprehensive details of potential upcoming equipment failures, which is already great for anticipating and reducing emergency maintenance. However, some AI systems take this even further by incorporating prescriptive analytics.
Prescriptive analytics informs you about possible equipment failures, but also provides detailed recommendations on what to do next. Agentic AIs, such as those used by ADNOC, can then act independently to correct the problem, but it’s best to keep a human-in-the-loop approach.
Here are just some of the potential applications for predictive analytics:
- Balancing compression stations across vast distances to optimize fuel use
- Setting up schedules in real time to reflect shifts in the oil and gas market
- Notifying operators or closing valves if the system detects a potential leak
- Suggesting operational changes that could reduce the system’s emissions
Pipeline monitoring
AI also makes it easier for oil and gas firms to track thousands of miles of pipeline infrastructure, and not just for predictive maintenance or leak detection. Here are the ways companies are now (or will soon be) using AI to monitor their pipe networks:
- Computer vision: Drones and stationary cameras with LIDAR and thermal imaging can spot cracks, corrosion, dents, and more, including issues the human eye can’t spot.
- Fiber optic: Pipelines may also have fiber optic cables that act as “nerve endings”. They can “feel” for vibrations or strain, and an attached AI will report any anomalous data.
- IoT/Sensor data: Other IoT sensors can also detect pressure, temperature, flow speed, corrosion rate, and more. AI helps to reduce false positives and wasted shutdowns.
The future of AI in midstream energy
It’s likely that these trends will continue growing steadily. Digital twins might fully adopt edge computing, while predictive analytics may make the jump to prescriptive, possibly with help from agentic AIs.
According to an IBM report, 56% of energy executives think that AI will “enable new technology capabilities that fundamentally transform their business model.” This same report also says that 23% of midstream firms are aiming to use agentic agents.
However, the future of the midstream energy sector isn’t one that cuts out the human element entirely. The most effective solutions are those that still involve the human, simply because AIs can make the wrong call. Human staff should at least be available to validate any emergency actions.
Long-term, we’ll also likely see a bigger focus on ESG concerns, especially with AI itself using a lot of energy. This could manifest as companies selling decarbonisation models as a service, or using real-time data to prove their environmental compliance.
Final thoughts
The midstream energy sector, and oil and gas as a whole, are welcoming AI more and more by the day. Soon, it’s possible that even energy startups will need to add digital twins and predictive maintenance to their strategy to keep up with competitors. Only time will tell.
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