2015
DOI: 10.1016/j.petrol.2015.07.012
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Development of an adaptive surrogate model for production optimization

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Cited by 98 publications
(34 citation statements)
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“…60 The radial basis function (RBF) surrogate model was used instead of the simulation model to express the relationship between the design variables and the responses. 61,62 Uniformly, random sampling was carried out within the range of each design variable according to Hammersley Design. A total of 100 sample points were selected to fit the RBF surrogate model of each response.…”
Section: Surrogate Modelmentioning
confidence: 99%
“…60 The radial basis function (RBF) surrogate model was used instead of the simulation model to express the relationship between the design variables and the responses. 61,62 Uniformly, random sampling was carried out within the range of each design variable according to Hammersley Design. A total of 100 sample points were selected to fit the RBF surrogate model of each response.…”
Section: Surrogate Modelmentioning
confidence: 99%
“…The simulators are not necessarily optimized for fast iterative computations. Lighter modeling approaches such as surrogate models [17][18][19] are therefore needed in practical online applications.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, kinds of surrogate model-based approaches, including Response Surface Method (RSM) [10], Neural Networks (NNs) [11], Support Vector Machine (SVM) [12], Kriging meta-model [13]- [15] and Radial Basis Function (RBF) [16], have been increasingly favored due to their significant efficiency in reliability analysis. The basic principle of these methods is to construct a surrogate model as an alternative to the implicit performance function, and then complete the reliability assessment.…”
Section: Introductionmentioning
confidence: 99%