Case Study: How Advanced Sensing and Big Data Improve Pipeline Cleaning and Flow Assurance

_ 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.