"PIG" Detection with Pressure Transducers

Freitas, Victor C. G.; Lima, Gustavo Fernandes de; Salazar, Andrés O.; Maitelli, André Laurindo

doi:10.15662/ijareeie.2016.0509051

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Figure 33 and Figure 34 show the top-view drawing and an aerial photo of the pipeline. Further details of the development and operation of the pipeline are presented in [ 23 ].…”

Section: System Implementationmentioning

confidence: 99%

“…In [ 22 ], pipeline inspection using a device called a pipeline inspection gauge (PIG) was found to be safe and reliable when the PIG operated at low speeds during an inspection. According to [ 23 ], pipelines are a key component of an oil and gas supply system, so their maintenance is essential. Among available maintenance techniques, the use of PIGs has been successfully applied in many situations, such as cleaning, product separation and integrity inspection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

Freitas

Araujo

Crisóstomo

et al. 2022

Sensors

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A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

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“…Figure 33 and Figure 34 show the top-view drawing and an aerial photo of the pipeline. Further details of the development and operation of the pipeline are presented in [ 23 ].…”

Section: System Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

Freitas

Araujo

Crisóstomo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In [ 16 ], the authors present a PIG testing pipeline and its supervisory system. The results demonstrate how the use of pressure sensors can be used to detect the passage of PIG along the pipe as well as its average speed.…”

Section: Bibliography Reviewmentioning

confidence: 99%

PIG’s Speed Estimated with Pressure Transducers and Hall Effect Sensor: An Industrial Application of Sensors to Validate a Testing Laboratory

Lima

Freitas

Araújo

et al. 2017

Sensors

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The pipeline inspection using a device called Pipeline Inspection Gauge (PIG) is safe and reliable when the PIG is at low speeds during inspection. We built a Testing Laboratory, containing a testing loop and supervisory system to study speed control techniques for PIGs. The objective of this work is to present and validate the Testing Laboratory, which will allow development of a speed controller for PIGs and solve an existing problem in the oil industry. The experimental methodology used throughout the project is also presented. We installed pressure transducers on pipeline outer walls to detect the PIG’s movement and, with data from supervisory, calculated an average speed of 0.43 m/s. At the same time, the electronic board inside the PIG received data from odometer and calculated an average speed of 0.45 m/s. We found an error of 4.44%, which is experimentally acceptable. The results showed that it is possible to successfully build a Testing Laboratory to detect the PIG’s passage and estimate its speed. The validation of the Testing Laboratory using data from the odometer and its auxiliary electronic was very successful. Lastly, we hope to develop more research in the oil industry area using this Testing Laboratory.

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“…Pipelines are critical components of the oil and gas delivery system, hence they must be maintained. Pipeline Inspection Gauge (PIG) has been effectively used as a maintenance approach in scenarios such as cleaning, product separation, and pipe integrity inspection [3]. PIGs are the sole way to see damage in pipelines because seeing the inside surface of the pipeline using any other method is usually difficult [4].…”

Section: Introductionmentioning

confidence: 99%

Pipeline inspection using a low-cost Wi-Fi based intelligent pigging solution

Olugboji¹,

Abolarin²,

Adedipe³

et al. 2022

Nig. J. Tech.

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This study presents a low-cost smart pipeline inspection gauge (PIG) designed for pipeline defect (leakage) detection, and for quick data access and recovery for the purpose of analysis, utilizing locally sourced materials and off-the-shelf sensors and electronics. The PIG’s electronic circuit is designed to house the sensors and allow for easy reception and transfer of pressure measurements to the pipeline manager’s laptop via a WiFi module. A pressure sensor, a motion sensor, a wireless communicator, and an Arduino Microcontroller are utilized in the development of this PIG. The PIG was tested experimentally by being put through stationary, no load-no defect, and no load-defect tests respectively. The PIG was kept still during the stationary tests but was conveyed within a 160 mm diameter Polyvinyl Chloride (PVC) conduit with a length of 6.7 m for both the no load-no defect and no load-defect tests, using a 0.125 Hp direct current (DC) motor, with a gearbox attachment to pull from one end to the other. Using a WiFi module and the PuTTY program, the pressure values were retrieved. The test results revealed that P1 (front pressure) values were higher than P2 (rear pressure) values for the no load tests. P1 readings ranged from 1213 to 1214 Pa, with an average of 1213.86 Pa for the no load-no defect tests. The average P2 value was 1094.24 Pa, with a range of 1094.24 Pa to 1094.75 Pa. The pressure values for the no load-defect cases began at 1226.8 Pa and steadily decreased for the first 1.5 minutes, then remained at an average of 1214.2 Pa for the next 20 seconds until they arrived at the first defected point, where a value of 1216.1 Pa was recorded. The PIG traversed the pipeline until it had caught all of the pressure pulses at the defective sites. The higher pressure pulses (spikes) observed at the points of defects created along the pipeline in the experimental results from the no load-defect tests indicates that the Smart PIG was capable of detecting the created defects and demonstrated that the low-cost Smart PIG can be used to detect leakages on a pipe and can also be deployed in real life situations.

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"PIG" Detection with Pressure Transducers

Cited by 3 publications

References 5 publications

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

PIG’s Speed Estimated with Pressure Transducers and Hall Effect Sensor: An Industrial Application of Sensors to Validate a Testing Laboratory

Pipeline inspection using a low-cost Wi-Fi based intelligent pigging solution

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