Methodology of production strategy optimization based on genetic algorithms / Metodologia de otimização probabilistica de estrategias de produção baseada em algoritmos geneticos

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

High levels of uncertainty and associated risks in the exploration and production of some oil fields suggest the use of probabilistic optimized production strategies. Therefore, an appropriate production strategy should be chosen considering various geological and economic scenarios. In this work, a new approach for the optimization is proposed where the production strategy is optimized for selected geological representative models (GRM) and under selected economic scenarios simultaneously. Differently from conventional optimization methodologies where each representative geological model has a net present value (NPV) optimized under a specific economic model, this new approach considers all alternatives simultaneously, providing more information about the production performance of all scenarios, allowing a better decision-making process. Moreover, production strategy defined by the new approach tends to be more adaptable to geological and economic uncertainties. However, in the optimization process of wells quantity and placement, a very complex topology is normally produced. The potential of generation of local extreme values is high, therefore, it is appropriate to employ a robust search technique such as genetic algorithms. In this case, the solution space is better explored, yielding more confident results. However, random based techniques tend to be more expensive computationally than gradient based techniques. In this work, a methodology is proposed for production strategy optimization under uncertainties, based on genetic algorithms, that aims to reduce the number of simulations necessary to maximize the expected monetary value (EMV). The main idea is to control the size of the solution space through an appropriated conception of chromosomes structures and the implementation of specifics optimizations stages optimizing every GRM for every economic scenario simultaneously through a simulation technical developed for this purpose. The present work aims to provide an improvement with respect to uncertainty handling of the conventional optimization methodologies, yielding better results and providing a complete analysis of geological and economic uncertainties. Moreover, its intends to provide an advance with respect to the number of simulations necessary to optimize a production strategy through genetic algorithms, yielding faster results, speeding up the decision-making process

ASSUNTO(S)

production strategies algoritmos geneticos engenharia de reservatorio estrategia otimização matematica genetic algorithms optimization under uncertainties petroleum reservoir engineering

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