Ensuring proper flow conditions in pipelines, especially in aging ones, is a problematic aspect of asset management. This is true from a technical and operational point of view.
As systems get older, deposits inside and changes in flow conditions often lead to less effective pipeline cleaning even if regular pigging operations are performed on schedule.
Traditional indicators such as pressure trends at the surface, or the amount of debris collected, provide an indirect measure of how well pigs are performing. They also provide little insight into what the pig is actually doing inside the pipeline.
Additionally, a suboptimal cleaning schedule, or protocol, poses serious threats regarding inline inspections (ILI) through smart pigs. Tools used during ILI analysis, such as magnetic flux leakage or an ultrasonic inspection pig, require a clean asset to perform the internal analysis and avoid blockage of the tool caused by deposits or debris.
If blockage occurs, it may lead to financial losses resulting from a shutdown of the asset and costly recovery operations. This may also lead to stress for field operators. With this in mind, a tool that can reduce the negative impact of these critical issues has the potential to become very valuable in pipeline inspection.
This case study presents a new approach to assess the cleanliness of the pipeline with noninvasive and cost-effective sensors, allowing for a swift adjustment of pigging schedule or correction of any tool mismatch.
Smart Sensors for Pipeline Inspection
The use of compact sensors represents an emerging technology for low-impact pipeline inspection. New monitoring systems involving small sensors can be directly fitted on traditional cleaning pigs.
The system is considered plug-and-play and does not require any changes to the pipeline, launcher, or receiver. These aspects are important in making the system fully integrated with standard pigging operations.
Each package records high-frequency axial acceleration, pressure, and temperature. When fitted at different locations along the pig, the system can also capture differential pressure across the pipeline. Together, these measurements allow the pig’s motion to be reconstructed using software, and the hydraulic resistance the pig is facing can then be assessed in detail.
Axial acceleration provides data on the stability of the motion and the nature of contact between the pig and the pipe wall. Differential pressure is a measure of the loading of deposits and the resistance to flow across the pig.
Recording these parameters simultaneously enables pig-deposit interaction to be assessed on physical response rather than inferred from indirect surface indicators.
The data downloaded after pig recovery produces time-synchronized dynamic and hydraulic signatures for quick evaluation. An illustration of the sensor that is used in this operation is shown in Fig. 1.
Applicative Context
The use of the sensing technology was assessed through almost 4,000 km of pipelines, both onshore and offshore, in different countries and operating conditions.
The case history involved a critical asset for the pipeline operator. The subject pipeline was a 12-km-long, 6-in. subsea pipeline carrying paraffinic crude oil. The line was operated on a fixed pigging schedule, about once every week.
Despite consistent pigging operations, recurring pressure fluctuations and rapid reaccumulation of debris suggested that cleaning was not being performed effectively. In a cleaning context, such behavior usually points to uneven mechanical action caused by nonuniform deposit distribution or lack of sufficient wall contact.
The unstable differential pressure hinted at fluctuating hydraulic resistance and partial circumvention of deposits. Instead of continually scraping and conveying deposits, the pig would be “flying” over areas where it would not be in contact with the wall for a sufficient duration. The response measured suggested that the selected pig was not well suited to the profile of internal deposition.
The first assessment run was performed using a simple one-module brush pig, and the sensor was attached to the mandrel.
After collecting the data from the sensor, the software generated a graph, as shown in Fig. 2. Key parameters include:
- Temperature trend across the whole pipeline (yellow line).
- Pressure on the pig’s front (black line) and back (blue line), which can be represented also as a Δp (purple line).
- Acceleration on all three axes, which is translated as acceleration magnitude of the pig (green line).
From the first run, some considerations were ruled out.
- The acceleration magnitude in the first 4,000 m of the pipeline is significantly higher than in the rest of the pipeline, meaning that, in that section of the pipeline, the pig could be moving in a “start-and-stop” manner rather than a smooth and constant transition.
- The pressure trend between the front and the back of the pig is highly unstable, and in more than one section of the pipeline, the front pressure is higher than the back. Under normal conditions, the back pressure should be 2 to 4 bars higher than the front pressure.
Based on the interpretation of the first run data, the pig was reconfigured to improve wall contact and the quality of scraping. Another cleaning run was performed under similar operating conditions.
The dynamic response was quite different in this subsequent run. Axial acceleration levels were lower, and the motion was smooth and continuous. Differential pressure across the pig increased in a gradual and sustained manner and the high-frequency component was significantly attenuated, as shown in Fig. 3.
The difference between the two runs allowed the diagnostic company to rule out earlier inefficiencies that would have been caused by a hydraulic bottleneck. Instead, the issues were confirmed to be due to a mismatch between the pig type and the deposits.
The monitored data provided direct evidence that validated the optimization of the operation. Following the optimization effort, the cleaning efficiency became more consistent, and the need to run the pig multiple times was eliminated. The flow conditions were normalized, and the pigging program was trusted again. In Fig. 4 the complete progression of pigging between different runs is shown.
Through a long period of continuous operation, the improved pigging campaign and significantly reduced pigging frequency resulted in major cost savings. The savings were largely due to lower vessel utilization and improved production continuity. Besides financial gains, another significant result was the return of predictable cleaning behavior.
The operator no longer reactively repeated pig runs and instead adjusted deployment procedures based on measured internal response. This facilitated a major shift for the operator from schedule-based pigging to condition-based intervention, in which the frequency and configuration of cleaning are fine-tuned according to observed mechanical interaction rather than simply the passage of time.
Conclusion
This case study highlights that receiver debris volume, along with surface pressure response, may not necessarily provide a comprehensive description of cleaning effectiveness throughout the entire line. Internal pig dynamics can vary drastically in different pipeline sections, depending on local deposition patterns and hydraulic conditions.
The study shows that a combination of axial acceleration and differential pressure can effectively characterize the contact mechanics and flow resistance.
The presence of oscillatory acceleration, along with unstable engagement and potential bypassing, may be inferred from noisy differential pressure, whereas smooth motion with a maintained pressure differential points to continuous deposit mobilization and controlled transport.
Adding a sensing function to standard cleaning instruments facilitates the gathering of valuable diagnostic information without additional complexity. Such a system offers a better understanding of the internal conditions, a more effective way of pig configuration optimization, and a reduced risk of flow restrictions building up over time.
Rafal Damian Wolicki, SPE, is the technical sales and business developer at Sentris, where he supports the commercial development and technical positioning of new solutions in the energy sector, applied mostly to hydrocarbons and new energy vectors. He holds a PhD in chemistry applied to energy transition, with a focus on catalyst design and catalytic reaction modeling, CO2 conversion into synthetic fuels, and technologies for hydrogen production and storage. In his current role, Wolicki combines technical expertise with business-oriented activities, contributing to technological development and industrial applications. Thanks to experience gathered on both the technical and commercial sides, his work is positioned within the broader context of energy innovation and sustainable solutions.