A Practical Indirect Back Analysis Approach for Geomechanical Parameters Identification

Zhao, Hongbo; Zhang, Ru; Yin, Shunde

doi:10.1080/1064119x.2013.836258

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…Several researchers built proxy models to replace numerical simulations in their inverse analysis problems using distinct machine learning methods: artificial neural network, 22,25,26 genetic programming, 34 and support vector machine. 27,28,35 In the literature, numerous parameter identification problems of petroleum geomechanics applications considered artificial neural networks and genetic algorithms as proxy models and stochastic optimizers, respectively. Zhang and Yin 36 presented an inverse analysis approach based on both techniques to identify geomechanical parameters of petroleum reservoirs considering measured ground surface displacements.…”

Section: Introductionmentioning

confidence: 99%

“…If the mathematical model is not a closed‐form expression, the objective function evaluation of a parameter identification problem may be computationally expensive. Several researchers built proxy models to replace numerical simulations in their inverse analysis problems using distinct machine learning methods: artificial neural network, 22,25,26 genetic programming, 34 and support vector machine 27,28,35 …”

Section: Introductionmentioning

confidence: 99%

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Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Abreu

Mejia

Roehl

2022

Num Anal Meth Geomechanics

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In the last decades, the study of fluid flow through discontinuities such as fractures has received significant attention from the petroleum industry owing to the discovery of naturally fractured reservoirs. Predicting the behavior of this type of reservoir is challenging and includes, as a crucial step, determining its geomechanical parameters. Several in situ tests and empirical relationships have been established to estimate these parameters. Nevertheless, these procedures have important limitations. This work proposes an artificial intelligence-based methodology to identify geomechanical parameters from borehole injection pressure curves obtained during hydraulic fracturing tests. This methodology applies a genetic algorithm to minimize the sum of squared differences between the observed and predicted borehole pressure curves. Furthermore, an artificial neural network is adopted to build a proxy model that substitutes hydraulic fracturing numerical simulations, substantially reducing computational time. A proper recursive strategy is adopted to predict the borehole pressure curves.Additionally, a hyperparameter tuning technique selects appropriate neural network hyperparameters based on the multistep-ahead prediction error. Finally, the framework is applied in a KGD problem that models hydraulic fracture propagation. The benchmark confirms the capability of the proposed methodology to identify geomechanical parameters from fracturing tests.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Abreu

Mejia

Roehl

2022

Num Anal Meth Geomechanics

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“…The neural network method was utilized to construct the intelligent displacement back analysis model for identifying the mechanical property of the surrounding rock mass [12][13][14][15]. The support vector machine and the relevance vector machine were selected to build a displacement back analysis model to recognize the geomaterials parameters [16][17][18]. Machine learning provides an excellent tool for predicting the structural response and is selected as the surrogate model in the geotechnical back analysis.…”

Section: Introductionmentioning

confidence: 99%

Back Analysis of Geotechnical Engineering Based on Data-Driven Model and Grey Wolf Optimization

et al. 2022

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Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by combining a reduced-order model (ROM), grey wolf optimization (GWO), and numerical technology. The ROM was adopted to evaluate the response of the geotechnical structure based on a numerical model. GWO was used to search and identify the geomaterials properties based on the ROM. The developed back analysis framework was applied to a circular tunnel and a practical tunnel for determining the mechanical property of the surrounding rock mass. The results showed that the ROM could be an excellent surrogated model and replaced it with the numerical model. The obtained geomaterial properties were in excellent agreement with the actual properties. The deformation behavior captured by the developed framework was consistent with the theoretical solution in a circular rock tunnel. The developed framework provides a practical, accurate, and convenient approach for calibrating the geomaterial properties based on field monitoring data in practical geotechnical engineering applications.

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“…Seepage parameters could be calibrated with seepage monitoring data, and numerical simulation using the calibrated parameters would yield more realistic results. Typical calibration methods for geotechnical parameters include simulated annealing technique [9,10], particle swarm optimization [11][12][13], neural network and genetic algorithm [14][15][16][17], Nelder-Mead algorithm [13,18], response surface method (RSM) [19][20][21], and support vector machine [22].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Sluice Foundation Seepage Using Monitoring Data and Numerical Simulation

Fan

Liu

et al. 2019

Advances in Civil Engineering

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For sluices built on soil foundations, seepage safety of the foundation is one of the most concerns during operation of sluices. Monitoring data could reflect the real seepage behavior in the foundation, but of which the shortcoming is that generally only the local seepage states can be measured. The seepage field in the whole foundation can be analyzed by numerical simulation. The permeability coefficients of the foundation materials significantly affect the numerical simulation results; however, it is difficult to accurately determine the values of permeability coefficients. In this paper, an approach based on response surface method (RSM) for calibration of permeability coefficients was proposed, and the efficiency of parameter calibration is improved by constructing the response surface equation instead of time-consuming finite element calculation of foundation seepage. The seepage in a sluice foundation was analyzed using monitoring data and numerical simulation. The monitoring data showed that the seepage pressure in the foundation periodically varies with high value in flood season and low value in dry season. After calibration of the permeability coefficients of the foundation materials using the measured seepage pressure, the seepage fields in the foundation for different water levels were numerically simulated to investigate the cause for the periodical variation of the seepage pressure and the seepage safety of the foundation was assessed with the calculated seepage gradients. The methods adopted in this study could be applied to seepage analysis for sluice foundations with similar geologic conditions and antiseepage measures.

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A Practical Indirect Back Analysis Approach for Geomechanical Parameters Identification

Cited by 18 publications

References 26 publications

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Back Analysis of Geotechnical Engineering Based on Data-Driven Model and Grey Wolf Optimization

Analysis of Sluice Foundation Seepage Using Monitoring Data and Numerical Simulation

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