An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

Sanei, Manouchehr; Devloo, Philippe R.B.; Forti, Tiago; Durán, Omar; Santos, Erick Slis Raggio

doi:10.1007/s00603-021-02665-y

Cited by 6 publications

(7 citation statements)

References 37 publications

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“…Derivative-free based 3 Plasticity constitutive models For a comprehensive description of plasticity constitutive models, the reader is advised the references of (de Souza Neto et al, 2008;Aydan, 2019;Sanei et al, 2020;Duran et al, 2020;Sanei et al, 2021b;Sanei et al, 2022a;Sanei et al, 2022b). Only the concepts relevant to our work will be presented.…”

Section: Nlopt Optimization Algorithms Codementioning

confidence: 99%

“…The performance of classical optimization methods is really dependent on the initial data as a starting point for these methods. To overcome this problem, the same authors of this article, Sanei et al, (2021a) have proposed an innovative scheme to make an initial data for classical optimization methods to calibrate accurately the material parameters of strain hardening elastoplastic methods. Although the idea presented by Sanei et al (2021a) about the initial guess is very useful, this idea is presented for some specific constitutive models and the structure presented for other models is definitely different.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, the same authors of this article, Sanei et al, (2021a) have proposed an innovative scheme to make an initial data for classical optimization methods to calibrate accurately the material parameters of strain hardening elastoplastic methods. Although the idea presented by Sanei et al (2021a) about the initial guess is very useful, this idea is presented for some specific constitutive models and the structure presented for other models is definitely different. On the other hand, rock is anisotropic in nature, so in order to determine which constitutive model can best provide the behavior of porous medium, it is necessary to examine most of the existing models and for this, their parameters must be determined.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Sanei,

Devloo,

Forti

et al. 2023

Preprint

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Optimization is a branch of applied mathematics which is used extensively in almost all areas of decision-making. In particular, classical method is a kind of the optimization methods that can be applied to make decisions. The optimization method can only make a better decision if the algorithm starts with an appropriate initial data. The main purpose of this study is two-folds. First, the material parameters of multivariable plasticity models, such as Mohr-Coulomb and DiMaggio-Sandler are calibrated via the gradient based optimization method by considering the appropriate initial data from porous rock database. Moreover, the calibration of models is done by the methods presented in the Nlopt library with considering the same objective functions and the same initial guess for each parameter. Then, a home-made finite element simulator is used to evaluate the correctness of the proposed material parameters identification framework. The results show the capability of the proposed optimization framework to determine properly the material parameters of multivariable plasticity models.

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Section: Nlopt Optimization Algorithms Codementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Sanei,

Devloo,

Forti

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Huang and Nardin 14 , 15 established a qualitative relationship between microscopic and macroscopic parameters of viscous granular materials. Sanei et al 16 develop a new scheme to provide an initial guess for an iterative optimization method to accurately calibrate the material parameters of a strain-hardening elasto-plastic constitutive model based on test data. However, these newly proposed methods have certain disadvantages, such as complex and cumbersome processes, the need for a large number of sample data, etc., that is to say, they do not completely solve the disadvantages of the "trial and error method".…”

Section: Introductionmentioning

confidence: 99%

Research on calibration method of microscopic parameters of siltstone based on gray theory

Wu,

Hu,

Zhou

et al. 2023

Sci Rep

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When the Linear Parallel Bond Model (PBM) in Particle Flow Code is used to simulate intact rocks, a basic problem is how to determine the microscopic parameters that control the macroscopic properties of the modeled rocks. After simplifying the microscopic parameters of the PBM model, this study proposes a new method of inverse performance of the regression equations of the macroscopic parameters by the gray absolute correlation combined with regression analysis, which solves the drawbacks of the current manual trial parameter calibration method to a certain extent. When this method is applied to the calibration of the microscopic parameters of the siltstone, the numerical simulation results produce good agreement between the results derived from the finite element software (ABAQUS) both in terms of macroscopic parameters and damage forms.

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“…In general, the GM parameters are calculated using two general methods: laboratory tests and in-well measurements. In the laboratory, the strength and elastic properties are measured on the core using the necessary equipment (Sanei et al 2013(Sanei et al , 2015(Sanei et al , 2021aSanei and Faramarzi 2014). Furthermore, in-well measurements use wave velocity to compute GM characterization (Tiab and Donaldson 2015;Mavko et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Applied machine learning-based models for predicting the geomechanical parameters using logging data

Sanei,

Ramezanzadeh,

Delavar

2023

J Petrol Explor Prod Technol

Self Cite

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Geomechanical (GM) parameters play a significant role in geomechanical studies. The calculation of GM parameters by analyzing finite rock samples is very limited. The GM parameters show a nonlinear trend; thus, applying empirical relationships is unreliable to predict their quantities. Machine learning (ML) methods are generally used to improve the estimation of such parameters. Recent researches show that ML methods can be useful for estimating GM parameters, but it still requires analyzing different datasets, especially complex geological datasets, to emphasize the correctness of these methods. Therefore, the aim of this study is to provide a robust recombinant model of the ML methods, including genetic algorithm (GA)–multilayer perceptron (MLP) and genetic algorithm (GA)–radial basis function (RBF), to estimate GM parameters from a complex dataset. To build ML models, 48,370 data points from six wells in the complicated Norwegian Volve oil field are used to train GA–MLP and GA–RBF methods. Moreover, 20,730 independent data points from another three wells are used to verify the GM parameters. GA–MLP predicts GM parameters with the root-mean-squared error (RMSE) of 0.0032–00079 and coefficient determination (R2) of 0.996–0.999. It shows similar prediction accuracy when used to an unseen dataset. Comparing the results indicates that the GA–MLP model has better accuracy than the GA–RBF model. The results illustrate that both GA–MLP and GA–RBF methods perform better at estimating GM parameters compared to empirical relationships. Concerns about the integrity of the methods are indicated by assessing them on another three wells.

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An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

Cited by 6 publications

References 37 publications

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Research on calibration method of microscopic parameters of siltstone based on gray theory

Applied machine learning-based models for predicting the geomechanical parameters using logging data

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