Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

This paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and est...

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Published in:	Optimal control applications & methods Vol. 45; no. 3; pp. 1166 - 1181
Main Author:	Yan, Wei
Format:	Journal Article
Language:	English
Published:	Glasgow Wiley Subscription Services, Inc 01.05.2024
Subjects:	Genetic algorithms Integer programming Mixed integer Nonlinear equations Optimization models Support vector machines
ISSN:	0143-2087, 1099-1514
Online Access:	Get full text
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Abstract	This paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and establishes a nonlinear multi‐objective mixed integer programming portfolio model for oil and gas. The weights of multiple objectives are determined using support vector machines. The optimization model incorporates the displacement transfer concept of particle swarm optimizer and the mutation operation of genetic algorithm using the transfer strategy of Gaussian particle swarm. The effectiveness of the model and algorithm is demonstrated through two examples.
AbstractList	This paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and establishes a nonlinear multi‐objective mixed integer programming portfolio model for oil and gas. The weights of multiple objectives are determined using support vector machines. The optimization model incorporates the displacement transfer concept of particle swarm optimizer and the mutation operation of genetic algorithm using the transfer strategy of Gaussian particle swarm. The effectiveness of the model and algorithm is demonstrated through two examples.
Author	Yan, Wei
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Title	Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets
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