Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Framework for integrated oil pipeline monitoring and incident mitigation systems

Wireless Sensor Nodes (motes) have witnessed rapid development in the last two decades. Though the design considerations for Wireless Sensor Networks (WSNs) have been widely discussed in the literature, limited investigation has been done for their application in pipeline surveillance. Given the increasing number of pipeline incidents across the globe, there is an urgent need for innovative and effective solutions for deterring the incessant pipeline incidents and attacks. WSN pose as a suitable candidate for such solutions, since they can be used to measure, detect and provide actionable information on pipeline physical characteristics such as temperature, pressure, video, oil and gas motion and environmental parameters. This paper presents specifications of motes for pipeline surveillance based on integrated systems architecture. The proposed architecture utilizes a Multi-Agent System (MAS) for the realization of an Integrated Oil Pipeline Monitoring and Incident Mitigation System (IOPMIMS) that can effectively monitor and provide actionable information for pipelines. The requirements and components of motes, different threats to pipelines and ways of detecting such threats presented in this paper will enable better deployment of pipeline surveillance systems for incident mitigation. It was identified that the shortcomings of the existing wireless sensor nodes as regards their application to pipeline surveillance are not effective for surveillance systems. The resulting specifications provide a framework for designing a cost-effective system, cognizant of the design considerations for wireless sensor motes used in pipeline surveillance

INCREASING THE ROBUSTNESS OF CLASSIFICATION ALGORITHMS TO QUANTIFY LEAKS THROUGH OPTIMIZATION

Leaks in water pipeline networks have cost billions of dollars each year. Robust leak quantification (to detect and to localize) methods are needed to minimize the lost. We quantify leaks by classifying their locations using machine learning algorithms, namely Support Vector Machine and C4.5. The algorithms are chosen due to their high performance in classification. We simulate leaks at different positions at different sizes and use the data to train the algorithms. We tune the algorithm by optimizing the algorithms' parameters in the training process. Then, we tested the algorithms' models against real observation data. We also experimented with noisy data, due to sensor inaccuracies, that often happen in real situations. Lastly, we compared the two algorithms to investigate how accurate and robust they localize leaks with noisy data. We found that C4.5 is more robust against noisy data than SVM

Quantifying Acoustic and Pressure Sensing for In-Pipe Leak Detection

Experiments were carried out to study the effectiveness of using inside-pipe measurements for leak detection in plastic pipes. Acoustic and pressure signals due to simulated leaks, opened to air, are measured and studied for designing a detection system to be deployed inside water networks of 100 mm (4 inch) pipe size. Results showed that leaks as small as 2 l/min can be detected using both hydrophone and dynamic pressure transducer under low pipe flow rates. The ratio between pipe flow rate and leak flow rate seems to be more important than the absolute value of leak flow. Increasing this ratio resulted in diminishing and low frequency leak signals. Sensor location and directionality, with respect to the leak, are important in acquiring clean signal.King Fahd University of Petroleum and Mineral

Feasibility Evaluation of a Vibration-Based Leak Detection Technique for Sustainable Water Distribution Pipeline System Monitoring

Conventional water pipeline leak-detection surveys employ labor-intensive acoustic techniques, which are usually expensive and less useful for continuous monitoring of distribution pipelines. Based on a comprehensive review of literature and available commercial products, it has been recognized that despite previous studies and products attempting to address the limitations of the conventional surveys by proposing and evaluating a myriad of leak-detection techniques (LDTs), they lacked extensive validation on complex looped systems. Additionally, they offer limited compatibility with some pipe materials such as those made of plastic and may even fail to distinguish leaks from other system disturbances. A novel LDT that addresses some of these limitations is developed and evaluated in the current study using an experimental set-up that is representative of a real-world pipeline system and made of Polyvinyl Chloride (PVC) pipe. The studied LDT requires continuous monitoring of the change in the cross spectral density of surface vibration measured at discrete locations along the pipeline. This vibration-based LDT was hypothesized to be capable of not only detecting the onset of leakage, but also determining its relative severity in complex pipeline systems. Findings based on a two-phase, controlled experimental testing revealed that the proposed LDT is capable of detecting leakages and estimating their relative severities in a real-size, multi-looped pipeline system that is comprised of multiple joints, bends and pipes of multiple sizes. Furthermore, the sustainability merits of the proposed LDT for a representative application scenario are estimated. Specifically, life cycle costs and energy consumption for monitoring the large diameter pipelines in the water distribution system of the Charleston peninsula region in South Carolina are estimated by developing conceptual prototypes of the sensing, communication and computation schemes for practically employing the proposed LDT. The prototype designs are informed by the knowledge derived from the two-phase experimental testing campaign. Overall, the proposed study contributes to the body of knowledge on water pipeline leak detection, specifically to non-intrusive vibration-based monitoring, applications on plastic pipelines, and smart and sustainable network-wide continuous monitoring schemes

WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline Monitoring

One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classicalwireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, wepresent WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design

Leak localization in water distribution networks using pressure and data-driven classifier approach

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.Peer ReviewedPostprint (published version

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate

Magnetic Flux Leakage techniques for detecting corrosion of pipes

Oil and gas pipelines are subjected to corrosion due to harsh environmental conditions as in refinery and thermal power plants. Interesting problems such as internal and external corrosion, emerging from the increasing demand for pipeline protection have prompted this study. Thus, early detection of faults in pipes is essential to avoid disastrous outcomes. The research work presented in this thesis comprises investigations into the use of magnetic flux leakage (MFL) testing for pipe in extreme (underwater and high temperature) conditions. The design of a coil sensor (ferrite core with coil) with a magnetic circuit is carried out for high temperature conditions. The sensor thus developed lays the ground for non-destructive evaluation (NDE) of flaws in pipes through the MFL technique. The research focusses on the detection and characterization of MFL distribution caused by the loss of metal in ferromagnetic steel pipes. Experimental verifications are initially conducted with deeply rusted pipe samples of varying thicknesses in air. AlNiCo magnets are used along with Giant Magneto Resistance (GMR) sensor (AA002-02). The experiment is further repeated for saltwater conditions in relation to varying electrical conductivity with radio frequency identification (RFID) technique. A further study carried out in the research is the correlation between magnetic and underwater data communication. The study has resulted in the development and experimental evaluation of a coil sensor with its magnetic response at room and high temperatures. This makes the system effective under high temperature conditions where corrosion metal loss needs to be determined

Magnetic Flux Leakage techniques for detecting corrosion of pipes

Oil and gas pipelines are subjected to corrosion due to harsh environmental conditions as in refinery and thermal power plants. Interesting problems such as internal and external corrosion, emerging from the increasing demand for pipeline protection have prompted this study. Thus, early detection of faults in pipes is essential to avoid disastrous outcomes. The research work presented in this thesis comprises investigations into the use of magnetic flux leakage (MFL) testing for pipe in extreme (underwater and high temperature) conditions. The design of a coil sensor (ferrite core with coil) with a magnetic circuit is carried out for high temperature conditions. The sensor thus developed lays the ground for non-destructive evaluation (NDE) of flaws in pipes through the MFL technique. The research focusses on the detection and characterization of MFL distribution caused by the loss of metal in ferromagnetic steel pipes. Experimental verifications are initially conducted with deeply rusted pipe samples of varying thicknesses in air. AlNiCo magnets are used along with Giant Magneto Resistance (GMR) sensor (AA002-02). The experiment is further repeated for saltwater conditions in relation to varying electrical conductivity with radio frequency identification (RFID) technique. A further study carried out in the research is the correlation between magnetic and underwater data communication. The study has resulted in the development and experimental evaluation of a coil sensor with its magnetic response at room and high temperatures. This makes the system effective under high temperature conditions where corrosion metal loss needs to be determined