Selection criteria for pipeline leak detection methods using distributed fibre optic sensing. By Alex de Joode
Special optical technologies and software can transform fibre optic cables into sensing cables, solving the main challenge of monitoring long assets such as pipelines, power cables, tunnels, and train lines. With a fibre optic sensor cable, the sensing capability is always close to the leak event or external threat. Figure 1 illustrates a distributed fibre optic sensing (DFOS) interrogator sending a laser pulse through the fibre optic cable and the associated backscattered light traveling back to the interrogator. The backscattered light brings back acoustic/vibration and thermal information.
Distributed fibre optic sensing (DFOS) is well known as an external pipeline leak detection method that detects changes in temperature, noise or vibration. However, leak-related events occurring inside the pipe can also be sensed, such as negative pressure waves (NPW) and other internal acoustic signals. Similarly, DFOS detects internal events, including PIG/scrapper tracking, liquid accumulations in gas pipelines, slugs and flow constrictions caused by waxing or hydrate formation.
Today, DFOS-based pipeline leak detection software is well established, covering a wide variety of pipeline applications. LDS systems using distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) are already installed in hundreds of projects across tens of thousands of kilometres of pipelines.
DFOS technologies and fibre optic types
Standard fibre optic cables are available with suitable DFOS compatible single mode (SM) and multi mode (MM) fibres or with a combination of SM + MM fibres in a single cable.
DFOS pipeline leak detection focuses on the detection of thermal and acoustic leak signatures, and therefore DFOS technologies are grouped below according to thermal and acoustic detection capabilities.
Detection of thermal signals:
• Raman DTS. Measures accurate temperature values with no cross-talk between temperature and strain. Raman DTS uses (SM) or (MM) fibres. MM fibres provide a stronger scattering signal
• Brillouin DTS. Measures strain and temperature values with possible cross-talk between temperature and strain. Brillouin DTS has a stronger backscatter signal than Raman. It is often used in a loop configuration requiring a return fibre halving the monitoring range. Special cables should be considered to avoid cross-talk between strain and temperature. Brillouin DTS uses SM fibres
• Distributed temperature gradient sensing (DTGS) – as part of quantitative DAS (also known as phase-based DAS) or distributed fibre bragg gratings (DFBG). Measures temperature gradients very quickly over finite timespans with very high resolution (0.001K) without measuring absolute temperatures. DTGS, when part of quantitative DAS, uses SM fibres. When part of DFBG, DTGS uses specially treated FBG fibres
• Enhanced distributed temperature sensing (eDTS) – uses DTGS quantitative DAS + Raman DTS. eDTS uses temperature measurements from the Raman DTS with fast temperature variation measurements of DAS-DTGS to provide more sensitive and faster LDS. eDTS uses SM fibres or a combination of SM and MM fibres.
Detection of acoustic/vibration signals:
• Non-quantitative DAS (amplitude-based). These are simpler DAS systems that detect the presence of vibration signals, but not the true signal amplitude or phase of the acoustic signal. Amplitude DAS is mainly used for perimeter protection, does not provide DAS-DTGS, and has difficulty in classifying acoustic. Non-quantitative DAS uses SM fibres
• Quantitative DAS (phase-based). Quantitative DAS is suitable for applications that require DAS-DTGS and delivers the high-quality acoustic signal (low–fading, quantifiable, high repeatability) required for event classification. Quantitative DAS provides the true signal amplitude or phase of the acoustic signal. In addition to multi-LDS detection methods, quantitative DAS includes additional functionalities such as PIG tracking and third-party interference monitoring. AP Sensing's quantitative DAS uses two polarisations, enabling quantitative measurement with superior quality over extended distances of the sensor cable. Quantitative DAS uses SM fibres
• Distributed Vibration Sensing (DVS). Unlike DAS that is using a "coherent-optical time domain reflectometry" concept, DVS is based on a hybrid interferometer technology. Similar to non-quantitative DAS, DVS is often used for simpler applications such as perimeter protection. It does not provide DTGS and has difficulty in classifying acoustic events. DVS uses SM fibres
• Distributed fibre bragg grating sensing (DFBGS). Some types of DFBG are described as DAS but must use special types of DFBG fibres. DFBG can provide DTGS.
From the summary above, the selection of Raman DTS and quantitative DAS results in a better fit for most pipeline leak detection applications. Brillouin DTS could be considered a compromise in specific circumstances where higher signal strength or strain monitoring is required.
The selection process of a DFOS-based pipeline LDS technology must consider their suitability to the specific application and performance requirements. Some of the main aspects to evaluate are:
• DFOS role in the LDS programme. DFOS can be used as a primary LDS, secondary or as one component of a multi-LDS system, depending on pipeline characteristics. Industry best practices recommend that a combination of internal and external LDSs should be considered to improve sensitivity, detection time, and location accuracy
• Fibre optic cable considerations. Standard fibre optic cables using single-mode fibres are suitable for DAS and DTS. Standard fibre optic cable using multi-mode fibres can enhance the performance of Raman DTS
• Type of DAS or DTS technology: quantitative DAS and Raman DTS are preferred choices in most cases
• Correlation between leak signature type and DFOS technology. Thermal: If the leak causes a significant temperature change in the external environment, the LDS selection should consider DTS, quantitative DAS-DTGS, and eDTS. LDS software incorporating machine learning should also be considered to improve performance. Acoustic: If the pipeline is pressurised, a leak will generate acoustic and negative pressure wave (NPW) signals that can be detected and classified by quantitative DAS and suitable LDS software. Thermal + acoustic: If both leak signals are present, quantitative DAS with DAS-DTGS should be the primary choice. Often Raman DTS is selected in conjunction with DAS for redundancy and independent confirmation.
Alex de Joode is head of pipelines & terminals at AP Sensing