Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.731484
Title: Development of a framework for Integrated Oil and gas Pipeline Monitoring and Incident Mitigation System (IOPMIMS)
Author: Johnson, Chukwuemeka Eze
ISNI:       0000 0004 6497 1789
Awarding Body: University of Wolverhampton
Current Institution: University of Wolverhampton
Date of Award: 2017
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Abstract:
The problem of Third Party Interference (TPI) on Oil and Gas Pipelines is on the rise across the world. TPI is not only common in developing countries but is now occasionally experienced in developed countries including Germany and the UK. The risks posed by these third-party activities on Oil and Gas pipelines are enormous and could be measured in terms of financial costs, environmental damages as well as health and safety implications. The quest for an end to these malicious activities has triggered a lot of studies into the root causes of pipeline TPI, other causes of pipeline failure, risks associated with pipeline failure and their mitigation measures. However, despite the significance of the effects of TPI, very little has been done to proffer an enduring solution through research. This research therefore aims at developing a framework for integrated oil and gas pipeline monitoring and incident mitigation system through integration of various wireless sensors for effective monitoring of oil and gas pipelines. Having identified the existing gaps in literature as lack of reliable, accurate and standard method for oil and gas pipeline risk assessment model, the study undertook a quantitative approach to develop an effective Quantitative Risk Assessment (QRA) model for pipelines. The QRA model developed benchmarks pipeline risk assessment and gives the parameters with which standard QRA could be measured. The research findings indicate that risk associated with Nigerian Pipeline system is in the intolerable region whereas TPI is an increasing menace across the globe. Further findings show that Support Vector Machine (SVM) gave the best performance with 91.2% accuracy while Neural Networks (NN) and Decision Tree (DT) gave 63% and 57% accuracies respectively in terms of pipeline failure mode prediction accuracies. It was recommended that operators should draw out Pipeline Integrity Management (PIM) programs and store pipeline data in a format that captures number of fatalities, property damages and costs as well as volume of oil or gas spilled to ensure that accurate data is obtainable for improved PIM. In conclusion, having achieved its aim and objectives evidenced by the framework, model developed, and the recommendations presented, the research has contributed in no small measure to providing a solution to pipeline incidences.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.731484  DOI: Not available
Keywords: Oil and Gas Pipeline ; Third Party Interference ; Pipeline Sabotage ; Pipeline Monitoring ; Pipeline Surveillance ; Pipeline Incidences ; Pipeline Incidents ; Pipeline Threat ; Pipeline Risk ; Risk Assessment
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