472624 Minimizing Risk of Upgrading Projects on Existing Assets  

Wednesday, November 16, 2016: 9:58 AM
Union Square 3 & 4 (Hilton San Francisco Union Square)
Houssein A. Kheireddine, Risk Advisory Department, DNV GL, Houston, TX

As is the case with any upgrading projects, the addition of new units/equipment introduce uncertainties and unforeseen risk to the existing assets. In DNV GL’s work processes, methodologies incorporating cutting edge technology have been developed in order to measure risk and optimize performance. One of these methodologies that will be presented in this paper will quantify and minimize the risk of the upgrading project on the existing assets as well as the risk of the existing assets on the new units in order to maximize profit.

The main objective of this work is to apply reliability, availability, and maintainability (RAM) study to optimize the performance (from production standpoint) of the integrated project. Specifically, RAM analysis will be conducted to provide a quantitative assessment of the level of performance that may be expected from the system as a whole over its design life. DNV GL will use TARO software to execute the proposed methodology.

In order to illustrate the applicability of the proposed approach, a case study addressing the major risks of an upgrading projects will be evaluated. This case study will compare the as-is performance to the future performance by evaluating the availability and utilization of all the production-critical units and their impact on production. This analysis will assist in identifying the major units contributing to production losses. In addition, the case study will quantify the impact of the increase in utility system demand (Hydrogen, steam, power…etc.) on production and identify any bottlenecks. Finally, the case study will quantify the impact of holding tanks working volumes on production and existing assets.

The results of this study will improve stakeholder confidence in the proposed design and ensure that the integrated project yields maximum return on investment. The proposed methodology will provide managers with decision support in design optimization and integration, process debottlenecking, benchmarking of performance, and mitigation of unforeseen risks.

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