Smart oil field management system using evolutionary intelligence

Renata Garcia Oliveira, Luciana Faletti Almeida, Juan G. Lazo Lazo, Aline Gesualdi Manhães, Milena Faria Pinto

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

1 Cita (Scopus)


This research proposes a new flexible intelligent system that manages the inflow control valve to improve oil production. For the efficient management of the smart oil field, the use of optimization algorithms is required. Traditional optimization methods tend to be inefficient in solving such problems due to many variables and the numerous locally optimal solutions, besides the effort of reservoir simulation. Therefore, this work presents the development of a methodology that allows optimizing both the control and the positioning of the valves, maximizing the reservoir Net Present Value obtained through the operation management, and analyzing the deployment cost of intelligent wells and their operational returns. Decisions of inflow control valve placement and its operation, flow control, throughout the reservoir’s life cycle are simulated to verify the efficiency of the methodology. In order to evaluate and validate the proposed intelligent system, the methodology was tested by building a new model with three evolutionary algorithms, allowing the placement and control of the flow (valve) as a single problem. The results demonstrated that the proposed approach has significant gains in the increased recovered oil volume and decreased water produced, indicating more efficient and sustainable oil production.
Idioma originalInglés
Páginas (desde-hasta)45798-45814
Número de páginas17
PublicaciónIEEE Access
Fecha en línea anticipada2 may. 2023
EstadoPublicada - 2023

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Funding Information:
This work was supported in part by the Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ); in part by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) under Grant 41/2013, Grant 15/2015, and Grant E-26/210.958/2021; in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); and in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) under Grant 001.


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