More than three billion litres of water leak from pipes in England and Wales every day. In the oil and gas sector, unplanned outages, including those caused by pipeline failures, can cost operators over £100,000 per hour in lost production
What links both is the same systemic strain: infrastructure built for a different era, now operating under modern pressures. From climate volatility to tighter regulatory controls, pipeline networks are being forced to perform with greater reliability, efficiency and transparency, yet without fundamental redesign, many can’t.
Faced with these operational and structural pressures, the sector is transforming towards smarter, data-led operations. Real-time monitoring, autonomous inspections and predictive analytics are replacing reactive maintenance and routine checks. Pipeline management is being reengineered around data, automation and machine learning, across oil, gas and water networks alike.
AI IS MOVING DECISION-MAKING CLOSER TO THE PROBLEM
Pipeline operators now deal with thousands of data points every second, from flow rates to vibration patterns, pressure fluctuations to acoustic anomalies. Traditionally, much of that data was stored but rarely used, but AI is changing that.
Where AI is making the biggest impact isn’t in replacing engineers, but in accelerating decision-making. Shell, for example, has deployed AI-driven predictive maintenance across more than 10,000 assets, including pumps, compressors and control valves, analysing billions of sensor readings to detect early signs of mechanical failure. This helped cut unplanned downtime by 20% and reduced maintenance costs by 15%.
But AI’s applications extend far beyond equipment uptime. In pipeline integrity programmes, it is increasingly being used to detect leaks, corrosion, and structural fatigue. According to recent research, machine learning models trained on pressure, flow, and acoustic data can pinpoint the location and severity of leaks with high accuracy in controlled test environments, demonstrating their potential for real-world deployment.
ROBOTICS ENABLES INSPECTION WITHOUT DISRUPTION
For the oil and gas sector, drone inspections now replace foot patrols in remote areas, and submersible crawlers inspect internal welds and detect corrosion at depths that were once inaccessible. As inspection becomes automated, operators can increase frequency, improve resolution, and reduce exposure to confined spaces, all while cutting operational costs.
IOT IS TURNING PIPELINES INTO LIVE NETWORKS
Digital transformation doesn’t work without live data, and the proliferation of low-power, high-precision IoT sensors has changed how pipelines are monitored, not every few days, but every few seconds.
UK trials have already shown the value, with Ovarro’s EnigmaREACH system, tested across five utilities in 2024–25, reducing average leak detection time by 50%. By deploying wireless acoustic loggers overnight, utilities identified 5-6 hidden leaks per session, many of which would have gone unnoticed using conventional methods.
This is particularly significant in the context of Ofwat’s 2050 target to halve leakage. While legacy SCADA systems provide bulk data at fixed intervals, modern IoT infrastructure enables near-continuous condition monitoring. And when that data feeds into machine learning platforms, it becomes a foundation for faster, risk-based decisions across the network.
DIGITAL TWINS ARE CHANGING SYSTEMS DESIGN
Rather than relying on static models or assumptions, pipeline engineers are increasingly working with digital twins – dynamic, real-time virtual replicas that simulate how an asset is performing under current conditions. Digital twins are now being used to model stress points in ageing steel pipelines, optimise compressor loads, and test emergency shutdown scenarios. The benefit isn’t just better oversight, it is operational confidence. Operators can see in advance how the network would respond under strain, rather than waiting to find out the hard way.
EMBEDDING PREDICTIVE MAINTENANCE
Until recently, most maintenance was either reactive – fix it when it fails, or preventive – fix it on a schedule, even if nothing’s wrong. Both approaches come with cost inefficiencies and risk. Predictive maintenance changes the model entirely. By using sensor data and historical performance patterns, operators can forecast when components are likely to fail, and intervene just before they do.
Equipment failures cost oil and gas firms an average of £115 million per year, with worst-case scenarios exceeding £400,000 per hour. Predictive maintenance systems, increasingly being adopted on offshore platforms in the North Sea, are helping to reduce this by extending the lifespan of valves, pumps, and control systems.