Stochastic Programming for Crude Oil Procurement
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Solution Overview
Problem
Petroleum refineries face challenges in producing final products that meet stringent quality specifications due to uncertainties in crude oil qualities and refinery operations, leading to off-spec products, resource waste, and increased costs.
Innovation Solution
The implementation of stochastic programming and chance-constrained programming models to optimize crude oil procurement and refinery operations by representing uncertain parameters with probability distributions, ensuring that quality specifications are met while minimizing waste and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deterministic optimization methods are used for crude oil procurement, then the procurement process is simple and fast, but the solution is not robust to uncertainties in crude oil quality and operations, leading to off-spec products
Solution Approach 1:
The patent applies preliminary action by generating multiple scenarios representing uncertain parameters (crude oil quality, operational parameters) before making procurement decisions. The stochastic programming model is solved in advance to obtain a procurement plan that is robust across all scenarios, ensuring reliability without requiring complex real-time adjustments.
Solution Approach 2:
The patent introduces dynamics by transitioning from a deterministic single-point optimization to a dynamic stochastic programming framework that considers multiple possible futures. The model dynamically adjusts procurement decisions to account for uncertainties in crude oil quality and refinery operations, making the solution adaptable rather than static.
2Reliability
If stochastic programming models are used to account for uncertainties, then the robustness of decisions is improved, but the computational complexity and time required to solve the model increases
Solution Approach 1:
The patent applies segmentation by dividing the uncertain parameter space into discrete scenarios. Each scenario represents a specific realization of uncertain parameters (crude oil quality, operational conditions), allowing the complex stochastic problem to be broken down into multiple manageable deterministic sub-problems that can be solved more efficiently.
3Reliability
If conservative procurement strategies are used to ensure quality specifications are met, then off-spec products are reduced, but procurement costs and resource waste increase
Solution Approach 1:
The patent applies parameter changes by transforming the optimization objective from minimizing immediate procurement costs to minimizing expected total costs including potential penalties for off-spec products. This parameter transformation allows the model to balance quality compliance with resource efficiency, avoiding overly conservative decisions while maintaining reliability.
Data Source
AI summary
A method for selecting one or more crude oils from a plurality of crude oils. In some embodiments, a plurality of scenarios may be generated, each scenario comprising a plurality of values corresponding, respectively, to a plurality of uncertain parameters, the plurality of uncertain parameters comprising at least one uncertain parameter relating to a quality of a crude oil of the plurality of crude oils. In some embodiments, a stochastic programming model may be solved to obtain a solution that optimizes an objective function, and one or more crude oils may be procured based on respective procurement amounts in the solution of the stochastic programming model. In some embodiments, a chance-constrained programming model may be solved to obtain a solution that optimizes an objective function, and a plurality of feedstocks may be blended into a final product based on the solution of the chance-constrained programming model.