A Modified Differential Evolution Optimization Algorithm with Random Localization for Generation of Best-Guess Properties in History Matching

Rahmati, Hossein; Nouri, Alireza; Pishvaie, Mahmoud Reza; Bozorgmehri, Ramin

doi:10.1080/15567030903261832

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…In the study, the gradient-based method was only able to find local minima due to proximity to the initial guess. The differential algorithm was found to converge with nearly half the simulations of the GA [47]. Park et al [48] extended a Pareto-based, Multi-Objective Evolutionary Genetic Algorithm (MOEGA) developed by Deb et al [49] to handle conflicting multiple objectives to history matching [48].…”

Section: History Matching For Waterflood and Enhanced Oil Recoverymentioning

confidence: 99%

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Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

et al. 2017

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This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed.

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Section: History Matching For Waterflood and Enhanced Oil Recoverymentioning

confidence: 99%

“…Most current research developments involve modifications on these algorithms to improve accuracy, reliability, or computation time. Rahmati et al [47]. enhanced the differential evolution optimization algorithm to improve convergence rates and robustness.…”

Section: History Matching For Waterflood and Enhanced Oil Recoverymentioning

confidence: 99%

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

et al. 2017

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A latent space method with maximum entropy deep reinforcement learning for data assimilation

Zhang,

Wang

et al. 2024

Geoenergy Science and Engineering

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A new framework for geostatistics-based history matching using genetic algorithm with adaptive bounds

Maschio

Davólio

Correia

et al. 2015

Journal of Petroleum Science and Engineering

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A Modified Differential Evolution Optimization Algorithm with Random Localization for Generation of Best-Guess Properties in History Matching

Cited by 6 publications

References 8 publications

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

A latent space method with maximum entropy deep reinforcement learning for data assimilation

A new framework for geostatistics-based history matching using genetic algorithm with adaptive bounds

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