Smart Oil Field Management System Using Evolutionary Intelligence
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 probl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10113862/ |
| _version_ | 1797805945389056000 |
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| author | Renata Garcia Oliveira Luciana Faletti Almeida Juan G. Lazo Lazo Aline Gesualdi Manhaes Milena Faria Pinto |
| author_facet | Renata Garcia Oliveira Luciana Faletti Almeida Juan G. Lazo Lazo Aline Gesualdi Manhaes Milena Faria Pinto |
| author_sort | Renata Garcia Oliveira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:00:00Z |
| format | Article |
| id | doaj.art-46c83171bdb246a592a532603aebe477 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:00:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-46c83171bdb246a592a532603aebe4772023-06-12T23:01:46ZengIEEEIEEE Access2169-35362023-01-0111457984581410.1109/ACCESS.2023.327233510113862Smart Oil Field Management System Using Evolutionary IntelligenceRenata Garcia Oliveira0https://orcid.org/0000-0001-8226-3178Luciana Faletti Almeida1https://orcid.org/0000-0001-7149-8350Juan G. Lazo Lazo2https://orcid.org/0000-0001-7782-118XAline Gesualdi Manhaes3https://orcid.org/0000-0003-2802-412XMilena Faria Pinto4https://orcid.org/0000-0001-6916-700XElectrical Engineering Department, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, BrazilCentro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rio de Janeiro, BrazilElectrical Engineering Department, Universidad del Pacífico, Lima, PeruCentro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rio de Janeiro, BrazilCentro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rio de Janeiro, BrazilThis 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.https://ieeexplore.ieee.org/document/10113862/Intelligent fieldspositioning problemsevolutionary computingcontrol valve flow managementdecision support systems |
| spellingShingle | Renata Garcia Oliveira Luciana Faletti Almeida Juan G. Lazo Lazo Aline Gesualdi Manhaes Milena Faria Pinto Smart Oil Field Management System Using Evolutionary Intelligence IEEE Access Intelligent fields positioning problems evolutionary computing control valve flow management decision support systems |
| title | Smart Oil Field Management System Using Evolutionary Intelligence |
| title_full | Smart Oil Field Management System Using Evolutionary Intelligence |
| title_fullStr | Smart Oil Field Management System Using Evolutionary Intelligence |
| title_full_unstemmed | Smart Oil Field Management System Using Evolutionary Intelligence |
| title_short | Smart Oil Field Management System Using Evolutionary Intelligence |
| title_sort | smart oil field management system using evolutionary intelligence |
| topic | Intelligent fields positioning problems evolutionary computing control valve flow management decision support systems |
| url | https://ieeexplore.ieee.org/document/10113862/ |
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