Optimization of subsurface models with multiple criteria using Lexicase Selection

Yifan He, Claus Aranha, Tony Hallam, Romain Chassagne

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Abstract

Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with Multi-Objective Evolutionary Algorithms (MOEAs). This group of algorithms optimize multiple objectives simultaneously, considering the trade-off between objectives. However, SHM requires the solutions that are good on all objectives rather than a trade-off. In this study, we propose a Differential Evolution algorithm using Lexicase Selection to solve the SHM problems. Unlike the MOEAs, this selection method pushes the solutions to perform well on all objectives. We compared this method with two MOEAs, namely Non-dominated Sorting Genetic Algorithm II and Reference Vector-guided Evolutionary Algorithm, on two SHM problems. The results show that this method generates more solutions near the ground truth.
Original languageEnglish
Article number100237
JournalOperations Research Perspectives
Volume9
Early online date26 Apr 2022
DOIs
Publication statusPublished - 2022

Keywords

  • Lexicase Selection
  • Multi-Objective Evolutionary Algorithm
  • Multi-objective optimization
  • Seismic History Matching

ASJC Scopus subject areas

  • Statistics and Probability
  • Strategy and Management
  • Management Science and Operations Research
  • Control and Optimization

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