Process Automation Systems’ design, selection, planning and implementation play a contributing role in achieving success in Industrial Megaprojects within the Oil and Gas Industry. Process Automation Systems represent only 8% - 10% of the total installed cost in capital projects, but the reliability and performance of Process Automation Systems are fundamental factors to ensure the operability and safety of new plants within the oil and gas industry.
Recent studies show an increasing number of Industrial Megaprojects in execution during the last decade, a better understanding of the real impact that these projects can bring to our societies, the complexity of these endeavors and the likelihood of having more megaprojects being approved during the next 20 years in the global market. It is pleasant to hear that there are favorable conditions present in the industry to promote and execute capital projects, but there is an alarming rate at which these capital projects overrun schedules and budgets.
Project execution key performance indicators such as cost growth, cost index, schedule index, schedule slippage and operability index often applied to measure the success of Megaprojects, should be carefully followed by project management teams, during the implementation of Process Automation Systems.
In the oil and gas industry megaprojects are executed in a stage gated work process typically divided into phases with a pause for assessment and decision about whether to proceed. The gate assessments examine both economic/business and technical aspects of the project, to make decisions to stop, recycle or proceed. The purpose of this research is to identify practices in a stage gated work process approach (FEL Front End Loading) to increase the probability of success in Process Automation Systems implementation given complexity factors in Industrial Megaprojects. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/23588 |
| Date | 19 March 2014 |
| Creators | Martínez-Alvernia, Luis Antonio |
| Source Sets | University of Texas |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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