Petroleum industry and modelling under uncertainty

Résumé du document

In the current paper, we present how the proposed Monte Carlo simulation algorithm can be used in the context of production optimization in the oil and gas industry. Petroleum refining begins with the distillation, or fractionation, of crude oils into separate hydrocarbon groups. The resultant products are directly related to the characteristics of the crude processed. Most distillation products are further converted into more usable products by changing the size and structure of the hydrocarbon molecules through cracking, reforming, and other conversion processes. These converted products are then subjected to various treatments and separation processes such as extraction, hydrotreating, and sweetening to remove undesirable constituents and improve product quality. Integrated refineries incorporate fractionation, conversion, treatment, and blending operations and may also include petrochemical processing. For example, formulating and blending is the process of mixing and combining hydrocarbon fractions, additives, and other components to produce finished products with specific performance properties.

Extraits

[...] This chapter provided the actual analysis and comparison of the two approaches used in decision-making under uncertainty. These are Monte Carlo simulation and fuzzy linear programming. This chapter has set out to look at the practicalities that are associated with the developed models (capital rationing and performance target models). The previous chapter considered the numerical application under certainty, and this chapter completes the numerical application under uncertainty, and the analysis of the results of the capital rationing and performance target models. [...]

[...] Experience from case studies using the Visual Basic Application tool has shown the feasibility of the Monte Carlo simulation and fuzzy linear programming approaches. Monte Carlo simulation is advantageous because it is a "brute force" approach that is able to solve problems for which no other solutions exist. Unfortunately, this also means that it is computer intensive and best avoided if simpler solutions are possible. The most appropriate situation to use Monte Carlo methods is when other solutions are too complex or difficult to use. It is a sensitivity analysis for uncertain models. [...]

[...] In the science and engineering communities, MC simulation is often used for uncertainty analysis, optimization, and reliability-based design. In manufacturing, MC methods are used to help allocate tolerances in order to reduce cost. There are certainly other fields that employ MC methods, and there are also times when MC is not practical (for extremely large problems, computer speed is still an issue). However, MC continues to gain popularity, and is often used as a benchmark for evaluating other statistical methods comparing to fuzzy programming. [...]

[...] Also, petroleum exploration is notoriously risky (Figure 2). Scarce resources are allocated to drilling opportunities with no guarantee that significant quantities of oil will be found. In the late 1980s and early 1990s the Phillips Petroleum Company was involved in oil and gas exploration along the eastern and southern coasts of the United States. In deciding how to allocate the annual exploration budget between drilling projects the company's managers faced two issues. For example, the company could adopt a strategy of having a relatively small involvement in a wide range of projects. [...]

[...] Petroleum industry and modelling under uncertainty Abstract In most real-world situations, the coefficients of mathematical models are not exactly known. In this context, it is convenient to consider an extension of traditional mathematical programming models incorporating their intrinsic uncertainty, without assuming the exactness of the model coefficients. The purpose of the current paper is to provide a broad overview of the field. We introduce the ways in which problems and their solutions are formulated. Subsequent sections consider the most appropriate methods for dealing with linear optimization, with emphasis placed on the formulation. [...]