A Novel Surrogate-Assisted Multi-objective Optimization Method for Well Control Parameters Based on Tri-Training

Lian, Wang; Yao, Yuedong; Wang, Kongjie; Adenutsi, Caspar Daniel; Zhao, Guoxiang; Lai, Fengpeng

doi:10.1007/s11053-021-09948-9

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…7a we observe that the distribution of parameter #71 ( F n (i = 71, l) , associated with maximum carbonate mud production at 90 Ma [m/My] is concentrated close to the right boundary. Hence, the parameter range is enlarged from the interval [5,25] to [5,35]. Figure 7b depicts the distribution F n (i = 107, l) , for parameter #107 which is slope driven marine silt transport coefficient [km 2 /Ky].…”

Section: Resultsmentioning

confidence: 99%

“…Model reduction techniques are implemented to reduce the computational cost required to predict the key outputs of the problem. Surrogate models are typically constructed using data-driven approach via the response of the full simulator to selected locations in the parameter space 34,35 . Here, the surrogate model is built on the Polynomial Chaos Expansion (PCE) technique with a reduced number of parameters selected through the screening step [36][37][38] .…”

Section: Methodsmentioning

confidence: 99%

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Modelling parametric uncertainty in large-scale stratigraphic simulations

Mahmudova

Civa

Caronni

et al. 2023

Sci Rep

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We combine forward stratigraphic models with a suite of uncertainty quantification and stochastic model calibration algorithms for the characterization of sedimentary successions in large scale systems. The analysis focuses on the information value provided by a probabilistic approach in the modelling of large-scale sedimentary basins. Stratigraphic forward models (SFMs) require a large number of input parameters usually affected by uncertainty. Thus, model calibration requires considerable time both in terms of human and computational resources, an issue currently limiting the applications of SFMs. Our work tackles this issue through the combination of sensitivity analysis, model reduction techniques and machine learning-based optimization algorithms. We first employ a two-step parameter screening procedure to identify relevant parameters and their assumed probability distributions. After selecting a restricted set of important parameters these are calibrated against available information, i.e., the depth of interpreted stratigraphic surfaces. Because of the large costs associated with SFM simulations, probability distributions of model parameters and outputs are obtained through a data driven reduced complexity model. Our study demonstrates the numerical approaches by considering a portion of the Porcupine Basin, Ireland. Results of the analysis are postprocessed to assess (i) the uncertainty and practical identifiability of model parameters given a set of observations, (ii) spatial distribution of lithologies. We analyse here the occurrences of sand bodies pinching against the continental slope, these systems likely resulting from gravity driven processes in deep sea environment.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modelling parametric uncertainty in large-scale stratigraphic simulations

Mahmudova

Civa

Caronni

et al. 2023

Sci Rep

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“…Above all, the objects of prediction and optimization for blasting effectiveness are multifaceted [14]. As the research of intelligent algorithms in mining and natural resources is intensifying, the prediction and optimization of multiple objects by intelligent algorithms are receiving more attention [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo

Zhao

Ping-feng³

2023

Applied Sciences

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Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

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An adding‐points strategy surrogate model for well control optimization based on radial basis function neural network

Chen,

Xu,

et al. 2024

Can J Chem Eng

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This work introduces a new adding points strategy for augmenting the accuracy of reservoir proxy model and improving the effect of well control optimization. The method is based on the optimization process of a radial basis function neural network and genetic algorithm (GA), which aids in identifying the more important points to be included in the sample space. Notably, the uniqueness of this method lies in selecting the points of higher importance for subsequent optimization processes across the entire sample space. These selected points are then added to the surrogate model. The surrogate model is updated for each generation until the termination condition is satisfied, enabling the surrogate model to achieve improved accuracy. The results show that the new method is more effective, superior, and converges faster than the traditional method.

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A Novel Surrogate-Assisted Multi-objective Optimization Method for Well Control Parameters Based on Tri-Training

Cited by 14 publications

References 47 publications

Modelling parametric uncertainty in large-scale stratigraphic simulations

Modelling parametric uncertainty in large-scale stratigraphic simulations

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

An adding‐points strategy surrogate model for well control optimization based on radial basis function neural network

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