Binary well placement optimization using a decomposition-based multi-objective evolutionary algorithm with diversity preservation
In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as...
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| Vydáno v: | Computational geosciences Ročník 27; číslo 5; s. 765 - 782 |
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01.10.2023
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| Abstract | In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these methods are known to generate a high number of duplicated strategies in discrete problems. Duplicated strategies negatively impact the optimization process since they: (i) degrade the efficiency of recombination operators in evolutionary algorithms; (ii) slow the convergence speed as they require more iterations to find a well-distributed set of strategies; and (iii) perform unnecessary re-evaluations of previously seen strategies through reservoir simulation. To perform multi-objective well placement optimization while avoiding duplicated strategies, this paper investigates the application of a newly proposed algorithm named MOEA/D-NFTS, with a modified diversity preservation mechanism that incorporates prior knowledge of the problem, on a multi-objective well placement optimization problem. The proposed methodology is evaluated on the UNISIM-II-D benchmark case, a synthetic carbonate black-oil simulation model in a well placement optimization problem using a binary strategy representation, indicating the presence or absence of a given candidate well position in the final strategy. The objective functions are the maximization of the Net Present Value, the maximization of the Cumulative Oil Production, and the minimization of Cumulative Water Production. The modified MOEA/D-NFTS performance is compared with a baseline algorithm without diversity preservation, and the evidence shows that the MOEA/D-NFTS produces statistically significant superior results, and is suitable for binary multi-objective well placement optimization. |
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| AbstractList | In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these methods are known to generate a high number of duplicated strategies in discrete problems. Duplicated strategies negatively impact the optimization process since they: (i) degrade the efficiency of recombination operators in evolutionary algorithms; (ii) slow the convergence speed as they require more iterations to find a well-distributed set of strategies; and (iii) perform unnecessary re-evaluations of previously seen strategies through reservoir simulation. To perform multi-objective well placement optimization while avoiding duplicated strategies, this paper investigates the application of a newly proposed algorithm named MOEA/D-NFTS, with a modified diversity preservation mechanism that incorporates prior knowledge of the problem, on a multi-objective well placement optimization problem. The proposed methodology is evaluated on the UNISIM-II-D benchmark case, a synthetic carbonate black-oil simulation model in a well placement optimization problem using a binary strategy representation, indicating the presence or absence of a given candidate well position in the final strategy. The objective functions are the maximization of the Net Present Value, the maximization of the Cumulative Oil Production, and the minimization of Cumulative Water Production. The modified MOEA/D-NFTS performance is compared with a baseline algorithm without diversity preservation, and the evidence shows that the MOEA/D-NFTS produces statistically significant superior results, and is suitable for binary multi-objective well placement optimization. |
| Author | Schiozer, Denis José Santos, Antonio Alberto S. de Moraes, Matheus Bernardelli Coelho, Guilherme Palermo |
| Author_xml | – sequence: 1 givenname: Matheus Bernardelli orcidid: 0000-0002-9485-0334 surname: de Moraes fullname: de Moraes, Matheus Bernardelli email: m121214@dac.unicamp.br organization: School of Technology (FT), University of Campinas (UNICAMP) – sequence: 2 givenname: Guilherme Palermo orcidid: 0000-0002-4641-0684 surname: Coelho fullname: Coelho, Guilherme Palermo organization: School of Technology (FT), University of Campinas (UNICAMP) – sequence: 3 givenname: Antonio Alberto S. surname: Santos fullname: Santos, Antonio Alberto S. organization: Center for Energy and Petroleum Studies (CEPETRO), University of Campinas (UNICAMP) – sequence: 4 givenname: Denis José orcidid: 0000-0001-6702-104X surname: Schiozer fullname: Schiozer, Denis José organization: Center for Energy and Petroleum Studies (CEPETRO), University of Campinas (UNICAMP) |
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