Choosing the direct or indirect path

Pipeline leak detection has become a critical aspect of modern infrastructure management, with far-reaching implications for safety, environmental protection, and economic stability. With over 2.5 million miles of oil and gas pipelines across the United States alone, there is an ongoing need for the safe operation and monitoring of these systems. Leaks can occur for various reasons, and they are often the result of a combination of factors, including corrosion, mechanical damage, material defects, or third-party damage. As of June 2022, over 2600 hazardous gas pipeline leaks in the United States caused more than US$4 billion in damages and emergency services, killed 122 people, and released 26.6 billion f3 of fuel such as methane or carbon dioxide into the atmosphere. These only included detectable major leaks reported to the government, while minor leaks can go undetected and unrepaired for years. Without proper leak detection technologies, these minor leaks will go unnoticed until it is too late to repair them.

Detecting and responding to pipeline leaks immediately upon their occurrence is imperative to reducing the risk of catastrophic consequences such as environmental disasters, financial losses, and public safety. Since most of the pipelines are buried underground, it is also critically important to rapidly and accurately locate the leak and take swift action before it develops into a catastrophically large leak. The on-line real-time methods to detect and locate a leak generally fall under one of two different types of systems: indirect (intrinsic) systems which use mathematical models based on the fundamental laws of conservation and fluid dynamics, or direct (extrinsic) systems which directly detect the released fluid or measure signals created by the leak. Additionally, leak detection technology can also be classified either as internal, meaning they detect signals inside the pipeline pressure boundary, or external, which detect signals outside of the pipeline.

Direct leak detection technology

Acoustic leak detection

Acoustic leak detection operates by detecting sound waves generated by the leak due to the breakdown of a pressure boundary. These acoustic signals are picked up by highly sensitive sensors and identified by various data processing techniques and advanced filters. The most effective data processing and acoustic leak identification technique so far is the acoustic fingerprint matching method, which requires not only a large database of leak acoustic signals to generate the matching mask, but also knowledge of the variation of mask as the acoustic signals propagate along the pipelines.2 The system continuously monitors the pipeline at an extremely high scanning rate. The data is processed within a local site processor using advanced algorithms to analyse the sound patterns to detect anomalies or matching acoustic fingerprint that suggest a leak. The central processor is responsible for confirming and locating the leak with multilayer, multi-iteration algorithms and time-of-flight computation based on GPS time stamps from two or more local processors. This method is commonly used in both liquid, gas, and multiphase pipelines due to its ability to identify subtle changes in acoustic signatures.

One of the main strengths of acoustic leak detection is its high reliability. Unlike other methods that may depend on dynamic relationships between pressure and flow, acoustic leak detection systems (ALDS) can detect leaks solely on the acoustic pressure wave generated and propagate within the pipeline guided and protected by the pipe walls. The physics-driven advanced data processing algorithms with unique leak acoustic fingerprint are effective and reliable in identifying even small leaks. Additionally, the speed of detection is another significant advantage. Acoustic systems provide near-instantaneous alerts, allowing pipeline operators to respond swiftly to leaks, minimising environmental damage and loss of product.

One limitation of ALDS is that while it can rapidly and reliably detect a leak, it can only estimate the leak rate by the signal strength and is unable to accurately quantify the size or rate of the leak. One major challenge for ALDS is positive identification of leak generated acoustic signals under extremely noisy environments. One advanced technology involving acoustic fingerprint matching against mask developed from large databases for various transporting fluids has been proven to be the most effective and reliable approach. Unlike other acoustic leak detection methods that rely solely on passive background noise filtering such as frequency analysis, this approach allows positive identification of the leak based on its unique fingerprint. This method is superior as it reduces the likelihood of false positives by allowing for more precise identification of leaks, even in noisy environments or under varying operational conditions.

Fibre optic leak detection

Fibre optics has recently emerged as an alternative technology in the field of leak detection mainly due to its combined use for communication. This technology leverages the unique properties of optical fibres, which are thin strands of glass or plastic capable of transmitting light over long distances with minimal signal loss. The most commonly used fibre optic leak detection technology is distributed fibre optic sensing (DFOS) which uses distributed sensors, detecting various environmental changes in real-time by analysing the light signals transmitted through the fibre. Fibre optic cables (FBC) transmit light signals that are scattered or reflected back due to small changes in the environmental conditions such as temperature, strain, vibration, and acoustics. Several algorithms can be used to analyse the backscattered light, including Raman, Rayleigh, Brillouin, Fibre Optic Bragg (FBG) each providing unique advantages in sensing different types of changes along the fibre optic cable including light intensity, wavelength changes, frequency shifts, or phase changes.

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