Mechanization of Qualitative Risk Based Inspection Analysis

The need for enhancing Risk-Based Inspection (RBI) strategies has received significant attraction of many researchers and practitioners in the offshore/onshore oil and gas. Qualitative RBI (QRBI) has many applications in risk assessment of the aging assets, screening of the asset based on their risk...

Full description

Saved in:
Bibliographic Details
Published in2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) pp. 401 - 406
Main Authors Eskandarzade, M., Chandima Ratnayake, R. M., Najafi Ershadi, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2020
Subjects
Artificial neural networks
Hydrogen
Indexes
Inspection
petroleum operation
pipeline
Pipelines
Risk based inspection
Risk management
Software
Online AccessGet full text

Cover

More Information
Summary:The need for enhancing Risk-Based Inspection (RBI) strategies has received significant attraction of many researchers and practitioners in the offshore/onshore oil and gas. Qualitative RBI (QRBI) has many applications in risk assessment of the aging assets, screening of the asset based on their risk level, and also in full risk assessment analysis of the items in the absence of proven quantitative RBI procedure. Traditionally, Subject Matter Engineers (SMEs) perform qualitative RBI and so the procedure is vulnerable to human biases and errors. Unreliability also causes due to the performer-to-performer output variation. Mechanization of the QRBI process improves the quality of the analysis by reducing the effects of human biases, enhancing the accuracy and speed of the calculations and increasing the repeatability. This manuscript first discusses the evolution of the QRBI process and presents recent trends in mechanization of the QRBI process. Then, the application of Gray Relational Analysis (GRA) method in mechanizing of the QRBI process is presented. In order to validate the results from GRA based QRBI, they compared by the results obtained from commercial software of RBLX.
DOI:10.1109/IEEM45057.2020.9309754