Leak Detection Systems for Multiphase Flow: Moving Forward

2002 ◽  
Author(s):  
Renan Martins Baptista ◽  
Carlos Henrique Wildhagen Moura
Keyword(s):  
Multiphase Flow ◽  
Detection System ◽  
Field Tests ◽  
Volumetric Flow Rate ◽  
Leak Detection ◽  
Physical Contact ◽  
Thermal Mass ◽  
Water Emulsion ◽  
Detection Techniques ◽  
Report Data

Multiphase flow is one of the most difficult situations for leak detection in pipelines, due to several reasons: the existence of two different and independent flow rates at each phase, five or more possible flow patterns, different fluid velocities at the phases, and sometimes a non-Newtonian associated behavior, due to the formation of an oil-water emulsion. There are two main groups for leak detection techniques: the models (or CPM, as stated in [API_1130]) which monitor the flow in real time (CVB, RTTM, PPA, etc.) from inside the pipeline (the instrument sensor is actually in physical contact with the fluid), and try to model the flow using a state estimator; and those based on dedicated external sensors (thermal, mass dispersion, etc) along the pipeline. Most of the technologies at the first group rely entirely on volumetric flow rate measurements, which turn them quite ineffective for multiphase flow. It is also relevant to consider that in some multiphase flow pipelines, the flow pattern changes quite random and intensively, allowing from a bubble pattern, to a slug pattern. There is sometimes the situation where a gas slug is big enough to fill entirely a short line and allow it to behave similarly to a gas pipeline, during a certain time (in fact, this was the case of one of the field tests this work will describe). This will bring unpredictability to those lines, in opposition to a regular single-phase line. Within this frame, the systems based on prediction approaches (hydraulic, statistical, etc, i. e., CPM’s), will show a good probability to be unreliable, inaccurate and not sensitive. The acoustic system is an exception to those two groups of technologies previously mentioned. It has, on one hand, a sensor that really touches the fluid (which would suggest it to be within the first group), but there’s no flow model behind it, on the other hand, but an acoustic sign analysis algorithm, acting somewhat like a piece of hardware. This paper will describe, discuss and report data for tests using an acoustic leak detection system at three different multiphase flow pipelines in Brazil, managed by PETROBRAS Production & Exploration Department.

Leak Detection Method for Long Pipeline Based on Dynamic Pressure and Wavelet Analysis

2010 ◽  
Author(s):  
Bin Xu ◽  
Likun Wang ◽  
Hongchao Wang ◽  
Min Xiong ◽  
Dongliang Yu ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Pressure Sensor ◽  
Static Pressure ◽  
Detection Method ◽  
Detection System ◽  
Dynamic Pressure ◽  
Field Tests ◽  
Leak Detection ◽  
Analysis Method ◽  
System A

Architecture of the leak detection system is presented, and the leak detection method based on dynamic pressure and wavelet analysis is studied in this paper. The feature of dynamic pressure which is generated by the leakage of pipeline is analyzed. The dynamic pressure signal of pipeline internal pressure is acquired by dynamic pressure sensor when leakage occurs, and the signal is analyzed by wavelet analysis method to detect the singularity, and the singularity is used to recognize and locate the leak. From the comparison of analysis results between dynamic pressure and static pressure, in order to improve the sensitivity and stability of the leak detection system, a polling rule between dynamic and static pressure is implemented. Field tests of the leak detection system are presented and analyzed. The results of the field tests demonstrate that the leak detection method based on dynamic pressure and wavelet analysis can detect pipeline leak rapidly and locate the leak precisely. This leak detection system has been applied in 5000 km pipeline or so.

Oil Pipeline Leak Detection System Based on Acoustic Wave Technology

2012 ◽  
Vol 220-223 ◽  
pp. 1628-1632
Author(s):  
Li Kun Wang ◽  
Bin Xu ◽  
Hong Chao Wang ◽  
Shi Li Chen ◽  
Jia Yong Wu ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Acoustic Wave ◽  
Internal Pressure ◽  
Detection System ◽  
Dynamic Pressure ◽  
Field Tests ◽  
Leak Detection ◽  
Oil Pipeline ◽  
Pressure Signal ◽  
Pressure Transmitter

Principle of the pipeline leak detection system is presented, and the leak detection method based on acoustic wave and wavelet analysis is studied in this paper. The dynamic pressure transmitter based on piezoelectric dynamic pressure transducer is designed. The characteristic of dynamic pressure transmitter when pipeline leak happened is analyzed. The dynamic pressure signal is suitable for pipeline leak detection for quick-change of pipeline internal pressure, while the static pressure is suitable for slow-change of pipeline internal pressure. The signal is analyzed by wavelet analysis method to detect the singularity, and the singularity is used to recognize and locate the leak. This paper indicated that the dynamic pressure signal could be adjust to this detection that the pressure changes in the pipeline. Field tests in 68.2 km pipeline segment show that the method detects pipeline leak rapidly and precisely.

scholarly journals A new system for locating leaks in urban water distribution pipes

2006 ◽  
Vol 17 (4) ◽  
pp. 450-466 ◽  
Author(s):  
Osama Hunaidi ◽  
Alex Wang
Keyword(s):  
Developing Countries ◽  
Water Distribution ◽  
Detection System ◽  
Low Cost ◽  
Management Strategies ◽  
Leak Detection ◽  
Water Utilities ◽  
Content Type ◽  
Detection Techniques ◽  
Effective System

PurposeTo introduce a new, low‐cost and easy‐to‐use leak detection system to help water utilities improve their effectiveness in locating leaks. The paper also presents an overview of leakage management strategies including acoustic and other leak detection techniques.Design/methodology/approachThe design approach was based on the use personal computers as a platform and enhanced signal processing algorithms. This eliminated the need for a major component of the usual hardware of leak pinpointing correlators which reduced the system's cost; made it easy to use, and improved the effectiveness of locating leaks in all types of pipes.FindingsEffectiveness of the new leak detection system for pinpointing leaks was demonstrated using real world examples. The system has promising potential for all water utilities, including small and medium‐sized ones and utilities in developing countries.Practical implicationsThe leak detection system presented in the paper will help all water utilities, including small and medium‐sized ones and utilities in developing countries, to save water by dramatically improving their effectiveness in locating leaks in all types of pipes.Originality/valueThe paper presents information about a new effective system for locating leaks in water distribution pipes. Effective leak detection tools are needed by water utilities worldwide.

Leak Detection Program in Petrobras Transportation Company

2002 ◽  
Author(s):  
Sueli Tiomno Tolmasquim ◽  
Paulo de Tarso A. Correia ◽  
Douglas Robertson
Keyword(s):  
Detection System ◽  
Implementation Strategies ◽  
Leak Detection ◽  
Petroleum Products ◽  
Project Implementation ◽  
Detection Techniques ◽  
Detection Program ◽  
Oil Pipelines ◽  
Trial Basis ◽  
Transportation Company

The objective of this paper is to describe the process used by TRANSPETRO to select and implement a leak detection system. The process consists of three steps: selection of a leak detection system, evaluation on a trial basis, and project implementation, which includes the negotiation of the contract and project implementation strategies. PETROBRAS Transportation Company — TRANSPETRO — operates several networks of pipelines in Brazil, with a total length of 6,500 km transporting petroleum products and crude, and 3,500 km transporting natural gas. Over the last two years, two large leaks occurred in the pipelines operated by TRANSPETRO. After analyzing these incidents, TRANSPETRO came to the conclusion that the consequences of leaks could be minimized with a reliable leak detection system and a proper emergency response procedure. To ensure that the leak detection system was fit to purpose and represented the best fit of technology to requirements, TRANSPETRO used a plan that involved three steps. First, based on the leak detection system criteria, TRANSPETRO and PETROBRAS R&D Center engineers selected a software based leak detection system with a hydraulic model (CPM, according to the API 1155 [1]) from among various leak detection techniques and products commercially available in the international market. Second, the selected leak detection system was implemented on two pipeline segments on a trial basis to evaluate it against TRANPETRO’s selection criteria for oil pipelines under real world conditions. Finally, after the successful completion of the trial, the implementation project, including up to 150 pipeline segments, was contracted.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3052
Author(s):  
Mas Ira Syafila Mohd Hilmi Tan ◽  
Mohd Faizal Jamlos ◽  
Ahmad Fairuz Omar ◽  
Fatimah Dzaharudin ◽  
Suramate Chalermwisutkul ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Early Detection ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Analytics ◽  
Detection System ◽  
Imaging Techniques ◽  
Ganoderma Boninense ◽  
Machine Learning Classification ◽  
Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

scholarly journals Diagnosis of Herpes Simplex Virus: Laboratory and Point-of-Care Techniques

2021 ◽  
Vol 13 (2) ◽  
pp. 518-539
Author(s):  
Peuli Nath ◽  
Md Alamgir Kabir ◽  
Somaiyeh Khoubafarin Doust ◽  
Aniruddha Ray
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Point Of Care ◽  
Diagnostic Techniques ◽  
Nucleic Acid Amplification ◽  
Physical Contact ◽  
Detection Sensitivity ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Simplex Virus

Herpes is a widespread viral infection caused by the herpes simplex virus (HSV) that has no permanent cure to date. There are two subtypes, HSV-1 and HSV-2, that are known to cause a variety of symptoms, ranging from acute to chronic. HSV is highly contagious and can be transmitted via any type of physical contact. Additionally, viral shedding can also happen from asymptomatic infections. Thus, early and accurate detection of HSV is needed to prevent the transmission of this infection. Herpes can be diagnosed in two ways, by either detecting the presence of the virus in lesions or the antibodies in the blood. Different detection techniques are available based on both laboratory and point of care (POC) devices. Laboratory techniques include different biochemical assays, microscopy, and nucleic acid amplification. In contrast, POC techniques include microfluidics-based tests that enable on-spot testing. Here, we aim to review the different diagnostic techniques, both laboratory-based and POC, their limits of detection, sensitivity, and specificity, as well as their advantages and disadvantages.

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