Bayesian optimization of riser configurations

Elsas, J.H.; Casaprima, N.A.G.; Cardoso, P.H.S.; Menezes, Ivan F. M.

doi:10.1016/j.oceaneng.2021.109402

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…It is not recommended to minimize the objective function directly estimated by ȳ * , owing to insufficient information from the small number of sample points. 47 In this sense, an acquisition function typically designates new candidates for the global optimum. The acquisition function defines where to evaluate the objective function by balancing the tradeoff between exploration (global search) and exploitation (local search).…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

“…It is not recommended to minimize the objective function directly estimated by

{\bar{\bm{y}}}_{\ast}

, owing to insufficient information from the small number of sample points 47 . In this sense, an acquisition function typically designates new candidates for the global optimum.…”

Section: Artificial Intelligence Conceptsmentioning

confidence: 99%

“…46 Bayesian optimization is competitive against, and is generally faster than, other stochastic optimization algorithms. 47 An essential component of Bayesian optimization is the algorithm applied to build the probabilistic model of the objective function. A Gaussian process 44 is widely employed in this context and extensively applied in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…In the machine learning context, this algorithm is helpful to optimize model performance in a reduced computing time compared to classical approaches, such as grid and random searches 46 . Bayesian optimization is competitive against, and is generally faster than, other stochastic optimization algorithms 47 . An essential component of Bayesian optimization is the algorithm applied to build the probabilistic model of the objective function.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Parameter identification of minifrac numerical tests using a gradient boosting‐based proxy model and genetic algorithm

Abreu,

Mejia,

Roehl

et al. 2023

Num Anal Meth Geomechanics

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In recent years, the petroleum industry has devoted considerable attention to studying fluid flow inside fracture channels due to the discovery of naturally fractured reservoirs. The behavior prediction of these reservoirs is a well‐known challenging task, in which the initial stage consists of identifying reservoir hydromechanical parameters. This work proposes an artificial intelligence‐based approach to identify hydromechanical parameters from borehole injection pressure curves acquired through minifrac tests. This approach combines proxy modeling with a stochastic optimization algorithm to match observed and predicted borehole pressure curves. Therefore, a gradient boosting‐based proxy model is built to predict borehole pressure curves, considering a proper strategy to develop time series modeling. Moreover, a Bayesian optimization algorithm is applied to compute the gradient boosting hyperparameters. In this optimization scenario, this paper proposes an appropriate objective function established from the assumed time series prediction strategy and the k‐fold cross‐validation. Finally, a genetic algorithm is adopted to identify unknown hydromechanical parameters, solving an inverse problem. Based on the proposed workflow, a study of the importance of the hydromechanical parameters is developed. To assess the methodology applicability, the approach is employed to identify parameters in synthetic and field minifrac tests. The results present how this approach can adequately identify hydromechanical parameters of hydraulic fracturing problems.

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

“…It is not recommended to minimize the objective function directly estimated by

{\bar{\bm{y}}}_{\ast}

, owing to insufficient information from the small number of sample points 47 . In this sense, an acquisition function typically designates new candidates for the global optimum.…”

Section: Artificial Intelligence Conceptsmentioning

confidence: 99%