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

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.
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