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.

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.

Remote and Intelligent Diagnostic Methods on Leakage Fault for Crude Oil Pipelines

2006 ◽  
Author(s):  
Lai-Bin Zhang ◽  
Zhao-Hui Wang ◽  
Wei Liang
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Data Exchange ◽  
Detection System ◽  
Leak Detection ◽  
Diagnostic Methods ◽  
Oil Fields ◽  
Detection Methods ◽  
Development Environment ◽  
Oil Pipelines

Oil and gas transportation pipelines are the key equipment in petroleum and chemical industry. At present, with the increase of transportation task in oil fields, real-time leak detection system becomes a demand that petroleum companies need to safeguard routines. At the heart of the leakage monitoring and detection procedures are the report of leakage event timely and of leakage point precisely. This paper presents a more realistic approach for using rarefaction-pressure wave technique in oil pipelines, which aims to two targets, one is the improvement of remote and intelligent degree, and the other is the improvement of the leakage location ability. This paper introduces a new scheme to meet the requirements of real time and high data transferring necessary for remote monitoring and leak detection methods for pipelines. The scheme is based on SCADA framework for remote pipeline leakage diagnosis, in which the Dynamic Data Exchange technology is utilized to construct the data-acquiring component to acquire the real-time information that could perform remote test and analysis. It also introduces a basic concept and structure of the remote leak detection system. Primarily, an embedded leak-detection package is designed to exchange the diagnostic information with the RTU data package of Modbus protocol, and then via fiber network, the SCADA-based remote monitoring and leak detection system is realized. Existing data acquisition apparatus applied in oil fields and city underground water pipeline is used, without changing the structure of pipeline supervisory system. This paper introduces the method of constructing DDE-based hot links between servers and client terminals, using Borland C++ Builder 6.0 development environment, and also explains the universality and friendliness of the method. It can easily access similar Windows’ applications simply by modifying Service names, Topic options and data Items. System feasibility was tested using negative-pressure data from oil-fields. Additionally, the applied results show that the whole running status of pipeline can be monitored effectively, and a higher automation grade and an excellent leak location precision of the system can be obtained.

scholarly journals Detecting Pipeline Leaks

2017 ◽  
Vol 139 (11) ◽  
pp. 34-39
Author(s):  
Vicki Niesen ◽  
Melissa Gould
Keyword(s):  
Detection System ◽  
Leak Detection ◽  
American Petroleum Institute ◽  
False Alarms ◽  
Case Scenario ◽  
Detection Program ◽  
Computer Simulators ◽  
Improved Technology ◽  
The Impact ◽  
Do So

This article explores technological advancements for detecting pipeline leaks. An ideal leak detection system should not only quickly detect both small and large leaks, but also do so reliably and not trigger false alarms. Operations in gas pipelines can differ quite a bit from those for liquids, so the experience gained in one type of line may not be entirely applicable when changing jobs. Fortunately, computer simulators are increasingly sophisticated, enabling operators to become comfortable handling a variety of situations. In December 2015, the American Petroleum Institute released a set of guidelines (RP 1175), written by a representative group of hazardous liquid pipeline operators, that established a framework for leak detection management. The focus of the guidelines is getting pipeline operators to use a risk-based approach in their leak detection program, with the goal of uncovering leaks quickly and with certainty. The best-case scenario is for leaks to not occur at all, and the industry is making great strides to keep them from happening. The combination of improved technology and risk-based management should enable operators to keep leaks small and contained, and reduce the impact on the environment as much as possible.

Transient Leak Detection in Crude Oil Pipelines

2004 ◽  
Author(s):  
Rainer Beushausen ◽  
Stefan Tornow ◽  
Harald Borchers ◽  
Keefe Murphy ◽  
Jun Zhang
Keyword(s):  
Steady State ◽  
Crude Oil ◽  
Pressure Wave ◽  
Detection System ◽  
Leak Detection ◽  
Statistical System ◽  
Transient Conditions ◽  
Oil Pipeline ◽  
Detection Systems ◽  
Oil Pipelines

This paper addresses the specific issues of transient leak detection in crude oil pipelines. When a leak occurs immediately after pumps are switched on or off, the pressure wave generated by the transients dominates the pressure wave that results from the leak. Traditional methods have failed to detect such leaks. Over the years, NWO has developed and implemented various leak detection systems both in-house and by commercial vendors. These systems work effectively under steady-state conditions but they are not able to detect leaks during transients. As it is likely for a leak to develop during transients, NWO has decided to have the ATMOS Pipe statistical leak detection system installed on their pipelines. This paper describes the application of this statistical system to two crude oil pipeline systems. After addressing the main difficulties of transient leaks, the field results will be presented for both steady-state and transient conditions.

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.

Real time leak detection system applied to oil pipelines using sonic technology and neural networks

2009 ◽  
Author(s):  
Alvaro M. Avelino ◽  
Jose A. de Paiva ◽  
Rodrigo E. F. da Silva ◽  
Gabriell J. M. de Araujo ◽  
Fabiano M. de Azevedo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Oil Pipelines

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.

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