A Surrogate Model for Fast Land Subsidence Prediction and Uncertainty Quantification

2021 ◽  
pp. 943-950
Author(s):  
Claudia Zoccarato ◽  
Massimiliano Ferronato ◽  
Pietro Teatini
Keyword(s):  
Uncertainty Quantification ◽  
Land Subsidence ◽  
Surrogate Model ◽  
Subsidence Prediction

scholarly journals Blending measurements and numerical models: a novel methodological approach for land subsidence prediction with uncertainty quantification

2020 ◽  
Vol 382 ◽  
pp. 457-462
Author(s):  
Laura Gazzola ◽  
Massimiliano Ferronato ◽  
Matteo Frigo ◽  
Pietro Teatini ◽  
Claudia Zoccarato ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Land Subsidence ◽  
Ad Hoc ◽  
Numerical Models ◽  
Methodological Approach ◽  
Hydrocarbon Reservoirs ◽  
Subsidence Prediction ◽  
New Information ◽  
Deep Rock ◽  
Synthetic Models

Abstract. The use of numerical models for land subsidence prediction above producing hydrocarbon reservoirs has become a common and well-established practice since the early '90s. Usually, uncertainties in the deep rock behavior, which can affect the forecast capability of the models, have been taken into account by running multiple simulations with different constitutive laws and mechanical properties. Then, the most uncertain parameters were calibrated to reproduce available subsidence measurements. The objective of this work is to propose a novel methodological approach for land subsidence prediction and uncertainty quantification by integrating the available monitoring information in numerical models using ad hoc Data Assimilation techniques. The proposed approach allows to: (i) train the model with the available data and improve its accuracy as new information comes in, (ii) quantify the prediction uncertainty by providing confidence intervals and probability measures instead of deterministic outcomes, and (iii) identify the most appropriate rock constitutive model and geomechanical parameters. The methodology is tested in synthetic models of production from hydrocarbon reservoirs. The numerical experiments show that the proposed approach is a promising way to improve the effectiveness and reliability of land subsidence models.

scholarly journals Land subsidence prediction using recurrent neural networks

2021 ◽  
Author(s):  
Sunil Kumar ◽  
Dheeraj Kumar ◽  
Praveen Kumar Donta ◽  
Tarachand Amgoth
Keyword(s):  
Neural Networks ◽  
Land Subsidence ◽  
Recurrent Neural Networks ◽  
Subsidence Prediction

Efficient Uncertainty Quantification of Wharf Structures under Seismic Scenarios Using Gaussian Process Surrogate Model

2018 ◽  
Vol 25 (1) ◽  
pp. 117-138 ◽  
Author(s):  
Lei Su ◽  
Hua-Ping Wan ◽  
You Dong ◽  
Dan M. Frangopol ◽  
Xian-Zhang Ling
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Surrogate Model ◽  
Seismic Scenarios

Uncertainty Quantification of Time-Dependent Reliability Analysis in the Presence of Parametric Uncertainty

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan ◽  
Xiaoping Du
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Reliability Index ◽  
Integration Method ◽  
Epistemic Uncertainty ◽  
Random Variables ◽  
Time Dependent ◽  
Data Uncertainty ◽  
State Function

Limited data of stochastic load processes and system random variables result in uncertainty in the results of time-dependent reliability analysis. An uncertainty quantification (UQ) framework is developed in this paper for time-dependent reliability analysis in the presence of data uncertainty. The Bayesian approach is employed to model the epistemic uncertainty sources in random variables and stochastic processes. A straightforward formulation of UQ in time-dependent reliability analysis results in a double-loop implementation procedure, which is computationally expensive. This paper proposes an efficient method for the UQ of time-dependent reliability analysis by integrating the fast integration method and surrogate model method with time-dependent reliability analysis. A surrogate model is built first for the time-instantaneous conditional reliability index as a function of variables with imprecise parameters. For different realizations of the epistemic uncertainty, the associated time-instantaneous most probable points (MPPs) are then identified using the fast integration method based on the conditional reliability index surrogate without evaluating the original limit-state function. With the obtained time-instantaneous MPPs, uncertainty in the time-dependent reliability analysis is quantified. The effectiveness of the proposed method is demonstrated using a mathematical example and an engineering application example.

scholarly journals Resampling strategies to improve surrogate model-based uncertainty quantification: Application to LES of LS89

2018 ◽  
Vol 87 (12) ◽  
pp. 607-627 ◽  
Author(s):  
Pamphile T. Roy ◽  
Luis Miguel Segui ◽  
Jean-Christophe Jouhaud ◽  
Laurent Gicquel
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Model ◽  
Model Based

FAST AND FLEXIBLE UNCERTAINTY QUANTIFICATION THROUGH A DATA-DRIVEN SURROGATE MODEL

2018 ◽  
Vol 8 (2) ◽  
pp. 175-192 ◽  
Author(s):  
Felix Dietrich ◽  
Florian Künzner ◽  
Tobias Neckel ◽  
Gerta Köster ◽  
Hans-Joachim Bungartz
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Model ◽  
Data Driven

Modeling Regional Faults for Land Subsidence Prediction

2006 ◽  
Author(s):  
M. Ferronato ◽  
G. Gambolati ◽  
P. Teatini
Keyword(s):  
Land Subsidence ◽  
Subsidence Prediction
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