scholarly journals Real Time Corrosion Monitoring in Lead and Lead-Bismuth Systems

2010 ◽  
Author(s):  
James F. Stubbins ◽  
Alan Bolind ◽  
Ziang Chen
Keyword(s):  
Real Time ◽  
Corrosion Monitoring

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

Remote Monitoring Digitizes Asset-Integrity Management

2021 ◽  
Vol 73 (01) ◽  
pp. 65-66
Author(s):  
Chris Carpenter
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Wall Thickness ◽  
Remote Monitoring ◽  
Ultrasonic Transducer ◽  
Abu Dhabi ◽  
Corrosion Monitoring ◽  
Electric Pulses ◽  
Ultrasonic Sound ◽  
Integrity Management

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197168, “Digitalize Asset-Integrity Management by Remote Monitoring,” by Mohamed Sahid, ADNOC, prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Monitoring of corrosion in process pipelines has always been of paramount importance in ensuring plant-asset integrity. Similarly, steam traps play an important role in ensuring steam quality and, thus, the integrity of critical assets in the plant. The complete paper discusses these two aspects of monitoring asset integrity - real-time corrosion monitoring and real-time steam-trap monitoring - as implemented by the operator. The authors highlight the importance of digitization by means of implementing wireless technology and making data available in remote work stations in real time. Real-Time Corrosion-Monitoring System Corrosion coupons and electrical resistance probes are among the most-tried and -tested methods to monitor corrosion, but the authors detail shortcomings of these systems, focusing their efforts on the option of using nonintrusive ultrasonic sensors for corrosion monitoring. Fixed ultrasonic thickness (UT) monitoring systems measure a localized thickness of vessel wall or pipe through the use of sound waves. They are the fastest method to measure wall thickness and wall loss reliably. The wall thickness is calculated from the reflection of the ultrasonic signal at both external and internal surfaces. UT systems normally include a transducer and a pulser/receiver. The type of transducer used for this application is the ultrasonic transducer, which can be either piezoelectric or variable-capacitive. The pulser generates short electric pulses of energy at a constant rate, which are converted by the transducer into short, high-frequency ultrasonic sound pulses. These pulses are then directed into the material. Any discontinuation or impurity in the path of the ultrasonic sound wave will be reflected and received by the transducer, transformed into an electric signal, and amplified by the receiver to be projected onto the display (in the case of portable UT instruments). Depending on the intensity shown on the display, information about the impurity or discontinuity, such as size, orientation, and location, can be derived accurately. The shortcomings of using portable UT sensors have been overcome by the introduction of permanent UT sensors, which provide wall-thickness measurement continuously at one location in real time. Because these sensors remain fixed at one location for years, it is possible to analyze corrosion at a single point over time, thus detecting early corrosion onset. Real-Time UT Gauging. The operator installed the real-time corrosion-monitoring system in its offshore associated gas (OAG) unit. A UK-based vendor provided UT sensors along with data-management and -viewing software to support data interpretation. Twenty locations were identified in various plants of the OAG unit on the basis of criticality and previously recorded corrosion levels.

Near Real-Time Corrosion Monitoring for Wellbore Integrity Management

2019 ◽  
Author(s):  
Amit Kumar ◽  
Tareq Al Daghar ◽  
Ali Al Shehhi ◽  
Brendon Keinath ◽  
Mohan Kulkarni ◽  
...  
Keyword(s):  
Real Time ◽  
Corrosion Monitoring ◽  
Wellbore Integrity ◽  
Integrity Management

scholarly journals A Review of In-Vivo and In-Vitro Real-Time Corrosion Monitoring Systems of Biodegradable Metal Implants

2020 ◽  
Vol 10 (9) ◽  
pp. 3141 ◽  
Author(s):  
Priscilla Yin Yee Chin ◽  
Quentin Cheok ◽  
Adam Glowacz ◽  
Wahyu Caesarendra
Keyword(s):  
Real Time ◽  
Review Paper ◽  
Modern Medicine ◽  
Monitoring Systems ◽  
Corrosion Monitoring ◽  
Biodegradable Metal ◽  
Metal Implants ◽  
Scientists And Engineers

In current modern medicine, biodegradable metal implants are still considered a work-in-progress between the collaborations of both scientists and engineers. As of now, one of the obstacles to this development is monitoring the corrosion rate of the implant. When a biodegradable metal implant (made of Mg, Zn, etc.) is introduced into the harsh environment of the human body, corrosion naturally occurs, causing metal ions to be released which may result in undesired health effects. The released products of the corroding implant can be used to monitor the implant condition. This paper discusses the current real-time corrosion monitoring systems (i.e., electrochemical-, microsensor-, and microdialysis-based) in-vivo and in-vitro. It is acknowledged that the progress in this sector still requires extensive research in order to obtain a desirable monitoring system and it is hoped that this review paper contributes to the research.

Surface pH Measurement during CO2 Corrosion by an IrOx pH Probe

2016 ◽  
Vol 1133 ◽  
pp. 381-385
Author(s):  
Saeid Kakooei ◽  
Mokhtar Che Ismail ◽  
Bambang Ari-Wahjoedi
Keyword(s):  
Real Time ◽  
Metal Surface ◽  
Ph Sensor ◽  
Temperature Monitoring ◽  
Ph Measurement ◽  
Financial Resources ◽  
Corrosion Monitoring ◽  
Material Damage ◽  
Sensor Design ◽  
Surface Ph

CO2 corrosion comprises the majority of material damage, which leads to shutdown in petroleum industries. This phenomenon depends on parameters such as pH and temperature. Monitoring and recording these parameters help industries minimize waste in terms of time and financial resources. The insufficiency of tools for this purpose is noticeable. This study proposed an potentiometric IrO2–pH microelectrode design in conjuction with a Ag/AgCl refrence electrode for real-time corrosion monitoring that can be used in various industries. Electrodeposition approach was used in the fabrication of a pH sensor. Electrochemical experiments were conducted to characterize the IrO2–pH sensor as well as monitor the pH on a metal surface in conjunction with the fabrication of a pH microelectrode-designed system. Results show that the proposed pH sensor design can be used for real-time corrosion monitoring by surface pH measurement.

A study of a real-time online monitoring system for the durability of concrete structures

2016 ◽  
Vol 63 (3) ◽  
pp. 184-189
Author(s):  
Mingming Xiao ◽  
Shilong Zhang ◽  
Yanbing Tang ◽  
Zhongmao Lin ◽  
Jiahong Chen
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Concrete Structures ◽  
Chloride Ion ◽  
Corrosion Current ◽  
Ion Concentration ◽  
Corrosion Monitoring ◽  
Content Type ◽  
System Data ◽  
Durability Of Concrete

Purpose This study aims to explore the effect of corrosion monitoring technology for ensuring concrete structure safety. Design/methodology/approach A new monitoring system scheme with unattended operation to evaluate the durability of concrete structures is presented, which includes four components, namely, a multi-function embedded sensor, a microprocessor data collecting module, a system data analysis and storage module, and a remote server module. Findings The system carries out monitoring of chloride ion concentration and pH in concrete, corrosion current density and of the self-corrosion potential of the reinforcing steel bar. Originality/value This system provides real-time, online, lossless monitoring for concrete structures.

scholarly journals Real-Time AFM and Impedance Corrosion Monitoring of Environmentally Friendly Ceria Films on AA7075

2020 ◽  
Vol 167 (10) ◽  
pp. 101503
Author(s):  
Sanja I. Stevanović ◽  
Maria Lekka ◽  
Alex Lanzutti ◽  
Nikola Tasić ◽  
Ljiljana S. Živković ◽  
...  
Keyword(s):  
Real Time ◽  
Environmentally Friendly ◽  
Corrosion Monitoring

Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach

2021 ◽  
Author(s):  
Subrata Bhowmik
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Processing Time ◽  
Learning Algorithm ◽  
Corrosion Monitoring ◽  
Digital Twin ◽  
Pipeline Inspection ◽  
Deep Learning Algorithm ◽  
Inspection Data ◽  
Pipeline Corrosion

Abstract Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In-Line Inspection images from some previous inspection data sets. During the corrosion monitoring through pipeline inspection, the digital twin system would be able to gather data and, at the same time, process and analyze the collected data. The analyzed data can be used to classify the corrosion type and determine the actions to be taken (develop predictive and prescriptive maintenance strategy). Convolution Neural Network, a well known Deep Learning algorithm, is used in the Tensorflow framework with Keras in the backend is used in the digital twin for corrosion inspection. CNN algorithm will first detect the corrosion and then the type of corrosion based on image classification. The deep-learning network training is done using 4000 images taken from the inspection video frames from a subsea pipeline inspection using ROV. The performances of both the methods are compared based on result accuracy as well as processing time. Deep Learning algorithm, CNN has approximately 81% accuracy for correctly identifying the corrosion and classify them based on severity through image classification. The processing time for the deep-learning method is significantly faster, and the digital twin generates the predictive or prescriptive strategy based on the inspection result in real-time. Deep-learning based digital twin for Corrosion inspection significantly improve current corrosion identification and reduce the overall time for offshore inspection. The inspection data loss due to the communication interference during real-time assessment can be eliminated using the digital twin. The image data can recover the required features based on other features available through the previous inspection. Furthermore, the system can adapt to the unrefined environment, making the proposed system robust and useful for other detection applications. The digital twin makes a recommended decision based on an expert system database during the real-time inspection. The complete corrosion monitoring process is performed in real-time on a cloud-based digital twin. The proposed pipeline corrosion inspection digital twin based on the CNN method will significantly reduce the overall maintenance cost and improve the efficiency of the corrosion monitoring system.

Validation of localised corrosion model using real time corrosion monitoring in a chemical plant

2005 ◽  
Vol 40 (1) ◽  
pp. 33-42 ◽  
Author(s):  
A. Anderko ◽  
N. Sridhar ◽  
L. T. Yang ◽  
S. L. Grise ◽  
B. J. Saldanha ◽  
...  
Keyword(s):  
Real Time ◽  
Chemical Plant ◽  
Corrosion Monitoring ◽  
Corrosion Model
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