A real-time structural parametric identification system based on fiber optic sensing and neural network algorithms

2003 ◽  
Author(s):  
Zhishen Wu ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fiber Optic ◽  
Parametric Identification ◽  
Identification System ◽  
Network Algorithms ◽  
Fiber Optic Sensing

scholarly journals Distributed Fiber Optic Sensing for Real-Time Monitoring of Gas in Riser during Offshore Drilling

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 267 ◽  
Author(s):  
Giuseppe Feo ◽  
Jyotsna Sharma ◽  
Dmitry Kortukov ◽  
Wesley Williams ◽  
Toba Ogunsanwo
Keyword(s):  
Real Time ◽  
Fiber Optic ◽  
Full Scale ◽  
Flow Dynamics ◽  
Petroleum Engineering ◽  
Detection Methods ◽  
Marine Riser ◽  
Offshore Drilling ◽  
Well Control ◽  
Fiber Optic Sensing

Effective well control depends on the drilling teams’ knowledge of wellbore flow dynamics and their ability to predict and control influx. Unfortunately, detection of a gas influx in an offshore environment is particularly challenging, and there are no existing datasets that have been verified and validated for gas kick migration at full-scale annular conditions. This study bridges this gap and presents pioneering research in the application of fiber optic sensing for monitoring gas in riser. The proposed sensing paradigm was validated through well-scale experiments conducted at Petroleum Engineering Research & Technology Transfer lab (PERTT) facility at Louisiana State University (LSU), simulating an offshore marine riser environment with its larger than average annular space and mud circulation capability. The experimental setup instrumented with distributed fiber optic sensors and pressure/temperature gauges provides a physical model to study the dynamic gas migration in full-scale annular conditions. Current kick detection methods primarily utilize surface measurements and do not always reliably detect a gas influx. The proposed application of distributed fiber optic sensing overcomes this key limitation of conventional kick detection methods, by providing real-time distributed downhole data for accurate and reliable monitoring. The two-phase flow experiments conducted in this research provide critical insights for understanding the flow dynamics in offshore drilling riser conditions, and the results provide an indication of how quickly gas can migrate in a marine riser scenario, warranting further investigation for the sake of effective well control.

Method for tunnel cross-section deformation monitoring based on distributed fiber optic sensing and neural network

2021 ◽  
Vol 67 ◽  
pp. 102704
Author(s):  
Gong-yu Hou ◽  
Zi-xiang Li ◽  
Zhi-yu Hu ◽  
Dong-xing Feng ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Fiber Optic ◽  
Deformation Monitoring ◽  
Fiber Optic Sensing ◽  
Tunnel Cross Section

scholarly journals OC-0254: MR compatibility of fiber optic sensing for real-time needle tracking

2016 ◽  
Vol 119 ◽  
pp. S116-S117
Author(s):  
M. Borot de Battisti ◽  
B. Denise de Senneville ◽  
M. Maenhout ◽  
G. Hautvast ◽  
D. Binnekamp ◽  
...  
Keyword(s):  
Real Time ◽  
Fiber Optic ◽  
Fiber Optic Sensing

A surveillance system for urban buried pipeline subject to third-party threats based on fiber optic sensing and convolutional neural network

2020 ◽  
pp. 147592172093064
Author(s):  
Suzhen Li ◽  
Renzhu Peng ◽  
Zelong Liu
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Fiber Optic ◽  
Third Party ◽  
The Third ◽  
Time Space ◽  
Fiber Optic Sensing ◽  
Space Matrix

Third-party threats, such as construction activities and man-made sabotage, have become the main cause of pipeline accidents in recent years. This article proposes a surveillance system for protecting the buried municipal pipelines from third-party damage based on distributed fiber optic sensing and convolutional neural network (CNN). Due to the ability of detecting very small perturbation, the phase-sensitive optical time-domain reflectometry (φ-OTDR) is employed for distributed vibration measurements along the pipelines. A two-layer classifier based on CNN is developed: one layer is used to discriminate the third-party activities from the environmental disturbance; the other is to determine the specific type of the third-party events. Meanwhile, a time-space matrix is introduced to reduce the false alarm and correct possible errors by taking into account the continuity of the signals in time and space. Field tests are carried out to validate the effectiveness of the proposed surveillance system. The recognition results show that the CNN-based classifiers achieve the accuracy of over 97%, which is 14.8% higher than that of the traditional feature-based machine learning method using random forest (RF) algorithm. It also indicates that the time-space matrix can dramatically reduce the false alarm and enhance the recognition accuracy.

Concurrent Real-Time Distributed Fiber Optic Sensing of Casing Deformation and Cement Integrity Loss

2019 ◽  
Author(s):  
Qian Wu ◽  
Sriramya Nair ◽  
Eric van Oort ◽  
Artur Guzik ◽  
Kinzo Kishida
Keyword(s):  
Real Time ◽  
Fiber Optic ◽  
Fiber Optic Sensing ◽  
Cement Integrity ◽  
Casing Deformation
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