Stochastic Programming with Economic and Operational Risk Management in Petroleum Refinery Planning under Uncertainty

• Main Author: Nguyen, Thi Huynh Nga
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi PETRONAS 2009
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/9241/1/2009%20Bachelor%20-%20Stochasting%20Programming%20With%20Economic%20And%20Operational%20Risk%20Management%20In%20Petroleu.pdf; http://utpedia.utp.edu.my/9241/

Rising crude oil price and global energy concerns have revived great interests in the oil and gas industry, including the optimization of oil refinery operations. However, the economic environment of the refining industry is typically one of low margins with intense competition. This state of the industry calls for a continuous improvement in operating efficiency by reducing costs through engineering strategies. These strategies are derived based on an understanding of the world energy market and business processes, with the incorporation of advanced financial modeling and computational tools. Regard to the matter, this work proposes the application of the two-stage stochastic programming approach with fixed recourse to effectively account for both economic and operational risk management in the planning of oil refineries under uncertainty. The scenario planning and analysis approach is adopted to consider uncertainty in three parameters: prices of crude oil and commercial products, market demand for products, and production yields. However, a large number of scenarios are required to capture the probabilistic nature of the problem. Therefore, to circumvent the problem posed by the resulting largescale model, a Monte Carlo simulation approach is implemented based on the sample average approximation (SAA) technique. The SAA technique enables the determination of the minimum number of scenarios required yet still able to compute the true optimal solution of the problem for a desired level of accuracy within the specified confidence intervals. Two measures of risk are considered, namely mean-absolute deviation (MAD) and Conditional Value-at-Risk (CVaR). A representative numerical example is presented to illustrate the proposed modeling approach using GAMS modeling language with the nonlinear solver CONOPT3. in