Integration of Support Vector Regression With Distributed Gauss-Newton Optimization Method and Its Applications to the Uncertainty Assessment of Unconventional Assets

2018 ◽  
Vol 21 (04) ◽  
pp. 1007-1026 ◽  
Author(s):  
Zhenyu Guo ◽  
Chaohui Chen ◽  
Guohua Gao ◽  
Richard Cao ◽  
Ruijian Li ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Optimization Method ◽  
Uncertainty Assessment ◽  
Support Vector

Enhancing the Performance of the Distributed Gauss-Newton Optimization Method by Reducing the Effect of Numerical Noise and Truncation Error With Support-Vector Regression

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2428-2443 ◽  
Author(s):  
Zhenyu Guo ◽  
Chaohui Chen ◽  
Guohua Gao ◽  
Jeroen Vink
Keyword(s):  
Objective Function ◽  
Support Vector Regression ◽  
Optimization Method ◽  
Real Field ◽  
Support Vector ◽  
Training Procedure ◽  
Sensitivity Matrix ◽  
Numerical Noise ◽  
Convergence Performance ◽  
Simulation Results

Summary Numerical optimization is an integral part of many history-matching (HM) workflows. However, the optimization performance can be affected negatively by the numerical noise existent in the forward models when the gradients are estimated numerically. As an unavoidable part of reservoir simulation, numerical noise refers to the error caused by the incomplete convergence of linear or nonlinear solvers or truncation errors caused by different timestep cuts. More precisely, the allowed solver tolerances and allowed changes of pressure and saturation imply that simulation results no longer smoothly change with changing model parameters. For HM with linear-distributed Gaussian-Newton (L-DGN), caused by the discontinuity of simulation results, the sensitivity matrix computed by linear interpolation might be less accurate, which might result in slow convergence or, even worse, failure of convergence. Recently, we have developed an HM workflow by integrating the support-vector regression (SVR) with the distributed-Gaussian-Newton (DGN) method optimization method referred to as SVR-DGN. Unlike L-DGN that computes the sensitivity matrix with a simple linear proxy, SVR-DGN computes the sensitivity matrix by taking the gradient of the SVR proxies. In this paper, we provide theoretical analysis and case studies to show that SVR-DGN can compute a more-accurate sensitivity matrix than L-DGN, and SVR-DGN is insensitive to the negative influence of numerical noise. We also propose a cost-saving training procedure by replacing bad-training points, which correspond to relatively large values of the objective function, with those training-data points (simulation data) that have smaller values of the objective function and are generated at most-recent iterations for training the SVR proxies. Both the L-DGN approach and the newly proposed SVR-DGN approach are tested first with a 2D toy problem to show the effect of numerical noise on their convergence performance. We find that their performance is comparable when the toy problem is free of numerical noise. As the numerical-noise level increases, the performance of the L-DGN degrades sharply. By contrast, the SVR-DGN performance is quite stable. Then, both methods are tested using a real-field HM example. The convergence performance of the SVR-DGN is quite robust for both the tight and loose numerical settings, whereas the performance of the L-DGN degrades significantly when loose numerical settings are applied.

scholarly journals Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhaoyang Qu ◽  
Miao Li ◽  
Zhenming Zhang ◽  
Mingshi Cui ◽  
Yuguang Zhou
Keyword(s):  
Transmission Line ◽  
Support Vector Regression ◽  
Dynamic Optimization ◽  
Optimization Method ◽  
Operating Conditions ◽  
Support Vector ◽  
Influence Coefficient ◽  
Grey Prediction ◽  
Loop Network ◽  
Grid Operation

Aiming at the problem of insufficient accuracy and timeliness of transmission line parameters in the grid energy management system (EMS) parameter library, a dynamic optimization method of transmission line parameters based on grey support vector regression is proposed. Firstly, the influence of operating conditions and meteorological factors on the changes of parameters is analyzed. Based on this, the correlation quantification method of transmission line parameters is designed based on Pearson coefficient, and the influence coefficient value is obtained. Then, with the influence coefficient as the constraint condition, a method for selecting strong influence characteristics of line parameters based on improved Elastic Net is proposed. Finally, based on the grey prediction theory, a grey support vector regression (GM-SVR) parameter optimization model is constructed to realize the dynamic optimization of line parameter values under the power grid operation state. The effectiveness and feasibility of the proposed method is verified through the commissioning of the reactance parameters of the actual local loop network transmission line.

scholarly journals Optimization of the Dewatering Process for Concentrate Pressure Filtering by Support Vector Regression

2021 ◽  
Author(s):  
Huizhon LIU ◽  
Keshun YOU
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Optimization Methods ◽  
Optimization Method ◽  
Filter Press ◽  
Laboratory Simulation ◽  
Support Vector ◽  
Dehydration Process ◽  
Control Variables ◽  
The Stability

Abstract In order to better improve the efficiency of the concentrate filter press dehydration operation, this paper studies the mechanism and optimization methods of the filter press dehydration process. Machine learning models of RBF-OLS, RBF-GRNN and support vector regression (SVR) are constructed respectively, and Perform laboratory simulation and industrial simulation separately. SVR achieves the best accuracy in industrial simulation, the simulated mean relative error (MRE) of moisture and processing capacity are respectively 1.57% and 3.81%. Finally, a simulation model of the filter press dehydration process established by SVR, and the optimtical simulation results Obtained by optimization method based on control variables. The results show that the machine learning method of SVR and optimization methods based on control variables are applied to industry, which can not only ensure the stability of expected production indicators, but also shorten the filter press dehydration cycle to less than 85% of the original.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
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