scholarly journals Current Technologies and the Applications of Data Analytics For Crude Oil Leak Detection in Surface Pipelines

2021 ◽  
Author(s):  
Francis Idachaba ◽  
Minou Rabiei
Keyword(s):  
Crude Oil ◽  
Data Analytics ◽  
Leak Detection

The Design and Installation of the LEOS Leak Detection and Location System for the Northstar Project

2002 ◽  
Author(s):  
Peter W. Bryce ◽  
Peter Jax ◽  
Jie Fang
Keyword(s):  
Crude Oil ◽  
Detection System ◽  
Oil Production ◽  
Leak Detection ◽  
Army Corps ◽  
Production Facility ◽  
Plastic Tube ◽  
Pump Station ◽  
Crude Oil Production ◽  
Hydrocarbon Molecules

The Northstar project is the first crude oil production facility constructed offshore in the Beaufort Sea. Produced crude oil is transferred via a buried subsea pipeline to shore and overland to the Trans Alaska Pipeline Pump Station PS1 facility. During the permitting process, concern was expressed that a very small chronic leak in the subsea oil line would remain undetected during the winter months of continuous ice cover. Therefore, the US Army Corps of Engineers stipulated that a prototype leak detection system be installed that would capable of detecting a threshold leak less than 32 BOPD. This paper addresses the efforts to develop and install the LEOS leak detection system for arctic operations. The system is based on the well-established LEOS leak detection technology (manufactured by Framatome ANP, formerly by Siemens AG). The system comprises a perforated plastic tube with a thin water impermeable acetate outer sheath that allows hydrocarbon molecules to diffuse into the air filled tube. The air inside the tube is replaced periodically (every 24 hours) and is passed through a hydrocarbon-sensing module. The module contains resistors sensitive to the presence of very small concentrations of hydrocarbon molecules. The presence and location of a leak is determined by measuring the time taken for the localized concentration of hydrocarbon molecules associated with a leak to reach the end of the tube. LEOS components and materials were engineered to survive installation during arctic winter conditions. It was also necessary to protect the plastic LEOS sensor tube as it was lowered through the ice, attached to the pipeline, into a pre-excavated trench and then backfilled. The 10km long LEOS tube was delivered to site in 31-coiled 300m (1000-ft) bundles that were transported from Germany to Alaska. The LEOS sensing tube was preinstalled in a protective outer polyethylene tube which was unreeled through a reverse bending jig. Crude oil production started at the Northstar production facility in October 2001 and the LEOS system has been operational since then and is providing the highest degree of assurance that no oil is escaping from the pipeline.

scholarly journals The challenges and solution of implementing a leak detection system on a complex crude oil pipeline network in Romania

2021 ◽  
Vol 7 (1) ◽  
pp. 60-73
Author(s):  
Harry SMITH ◽  
Kirsty MCNEIL ◽  
Tom RECORD ◽  
Dan BUZATU ◽  
Georgian ILIESCU ◽  
...  
Keyword(s):  
Crude Oil ◽  
Detection System ◽  
Leak Detection ◽  
Pipeline Network ◽  
Oil Pipeline

High Speed Data Communications and High Speed Leak Detection Models: Impact of Thermodynamic Properties for Heated Crude Oil in Large Diameter, Insulated Pipelines — Application Pacific Pipeline System

2000 ◽  
Author(s):  
Travis Mecham ◽  
Galen Stanley ◽  
Michael Pelletier ◽  
Jim C. P. Liou
Keyword(s):  
Physical Properties ◽  
Crude Oil ◽  
High Speed ◽  
Detection System ◽  
Large Diameter ◽  
Leak Detection ◽  
San Joaquin Valley ◽  
Pipeline System ◽  
Oil Pipeline ◽  
Detection Model

Recent advances in SCADA and leak detection system technologies lead to higher scan rates and faster model speeds. As these model speeds increase and the inherent mathematical uncertainties in implicit method solutions are reduced, errors and uncertainties in measurement of the physical properties of the fluids transported by pipeline come to dominate the confidence calculations for computer generated leak alerts in the control center. The ability to collect more data must be supported by the need for better model data in order to achieve optimal leak detection system performance. This is particularly true when the products transported are non-homogeneous and have strong viscosity-vs-temperature relationships. These are characteristics of crude oils in California’s San Joaquin Valley where significant heating is required to pump these oils in an efficient manner. Proper characterization and correct mathematical expression of these physical properties in leak models has become critical. This paper presents these new developments in the context of an implementation of this new technology for the Pacific Pipeline System (PPS). PPS is a recently constructed and commissioned 209 km (130-mile), 50.8 cm (20″) diameter, insulated, hot crude oil pipeline between the southern portion of California’s San Joaquin Valley and refineries in the Los Angeles basin. Operational temperatures in this line vary from ambient to 82.2°C (180°F) with pressures ranging from 345 kPa (50 psi) to 11,720 kPa (1700 psi). Due to the unique geometry of the line, facilities along the route include pumping stations, metering stations and numerous “throttle-type” pressure reduction facilities. On PPS, a high-speed leak detection model is supported by a fiber optic (OC-1) communication backbone with data rate capacities in excess of 50 Megabits Per Second (MPS). Total scan times for the distributed communication system have been reduced to 1/4 second — each facility reports data to the SCADA host four times each second. A corresponding 1/4 second leak detection model cycle leads to selection of Methods of Characteristics segments on the order of 260 meters (850 feet). This resolution, in conjunction with the advanced instrumentation package of PPS, makes detection of very small leaks realizable. This paper starts with an overview of the system and combines a mix of the theoretical requirements imposed by the mathematical solutions with a practical description of the laboratory procedures and propagated experimental errors. The paper reviews temperature-related errors and uncertainties and their influence on leak detection performance.

Near Real-Time Automated Detection of Small Hazardous Liquid Pipeline Leaks Using Remote Optical Sensing and Machine Learning

2016 ◽  
Author(s):  
Maria S. Araujo ◽  
Shane P. Siebenaler ◽  
Edmond M. Dupont ◽  
Samantha G. Blaisdell ◽  
Daniel S. Davila
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Real Time ◽  
Optical Sensors ◽  
Environmental Conditions ◽  
Detection System ◽  
Leak Detection ◽  
False Alarms ◽  
Different Types ◽  
Pump Stations

The prevailing leak detection systems used today on hazardous liquid pipelines (computational pipeline monitoring) do not have the required sensitivities to detect small leaks smaller than 1% of the nominal flow rate. False alarms of any leak detection system are a major industry concern, as such events will eventually lead to alarms being ignored, rendering the leak detection system ineffective [1]. This paper discusses the recent work focused on the development of an innovative remote sensing technology that is capable of reliably and automatically detecting small hazardous liquid leaks in near real-time. The technology is suitable for airborne applications, including manned and unmanned aircraft, ground applications, as well as stationary applications, such as monitoring of pipeline pump stations. While the focus of the development was primarily for detecting liquid hydrocarbon leaks, the technology also shows promise for detecting gas leaks. The technology fuses inputs from various types of optical sensors and applies machine learning techniques to reliably detect “fingerprints” of small hazardous liquid leaks. The optical sensors used include long-wave infrared, short-wave infrared, hyperspectral, and visual cameras. The utilization of these different imaging approaches raises the possibility for detecting spilled product from a past event even if the leak is not actively progressing. In order to thoroughly characterize leaks, tests were performed by imaging a variety of different types of hazardous liquid constitutions (e.g. crude oil, refined products, crude oil mixed with a variety of common refined products, etc.) in several different environmental conditions (e.g., lighting, temperature, etc.) and on various surfaces (e.g., grass, pavement, gravel, etc.). Tests were also conducted to characterize non-leak events. Focus was given to highly reflective and highly absorbent materials/conditions that are typically found near pipelines. Techniques were developed to extract a variety of features across the several spectral bands to identify unique attributes of different types of hazardous liquid constitutions and environmental conditions as well as non-leak events. The characterization of non-leak events is crucial in significantly reducing false alarm rates. Classifiers were then trained to detect small leaks and reject non-leak events (false alarms), followed by system performance testing. The trial results of this work are discussed in this paper.

Real Time Statistical Leak Detection on the RRP Crude Oil Network, Holland

2002 ◽  
Author(s):  
Joep Hoeijmakers ◽  
John Lewis
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Field Application ◽  
Normal Operation ◽  
False Alarms ◽  
Pipeline Network ◽  
Oil Pipeline ◽  
Detection Systems

Prior to the year 2000, the RRP crude oil pipeline network in Holland and Germany was monitored using a dynamic leak detection system based on a dynamic model. The system produced some false alarms during normal operation; prompting RRP to investigate what advances had been made in the leak detection field before committing to upgrade the existing system for Y2K compliance. RRP studied the available leak detection systems and decided to install a statistics-based system. This paper examines the field application of the statistics based leak detection system on the three crude oil pipelines operated by RRP. They are the 177 km Dutch line, the 103 km South line, and the 86 km North line. The results of actual field leak trials are reported. Leak detection systems should maintain high sensitivity with the minimum of false alarms over the long term; thus this paper also outlines the performance of the statistical leak detection system over the last year from the User’s perspective. The leak detection experiences documented on this crude oil pipeline network demonstrate that it is possible to have a reliable real-time leak detection system with minimal maintenance costs and without the costs and inconvenience of false alarms.

scholarly journals Data analytics for leak detection in a subcritical boiler

Energy ◽  
2021 ◽  
Vol 220 ◽  
pp. 119667
Author(s):  
Natarianto Indrawan ◽  
Lawrence J. Shadle ◽  
Ronald W. Breault ◽  
Rupendranath Panday ◽  
Umesh K. Chitnis
Keyword(s):  
Data Analytics ◽  
Leak Detection

Commissioning a Real-Time Leak Detection System on a Large Scale Crude Oil Pipeline During Start Up

2006 ◽  
Author(s):  
Joanna Mabe ◽  
Keefe Murphy ◽  
Gareth Williams ◽  
Andrew Welsh
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Leak Detection ◽  
Oil Pipeline ◽  
Start Up ◽  
Environmental Requirements ◽  
The Moment ◽  
Operational Data

This paper describes the process of incremental pipeline filling and the phased commissioning of a real-time leak detection system for the 1768 km long BTC crude oil pipeline. Due to stringent environmental requirements, it is essential for the leak detection system to work from the moment that crude oil is introduced into the pipeline. Without any prior operational data and with the pipeline partially filled, it is challenging for the leak detection system to monitor the integrity of the pipeline throughout the whole filling process. The application of the pig tracking software to track the oil front as the crude displaces nitrogen is also discussed.

Sign in / Sign up

Export Citation Format

Share Document