scholarly journals Correction to: A novel methodological approach for land subsidence prediction through data assimilation techniques

2021 ◽  
Author(s):  
Laura Gazzola ◽  
Massimiliano Ferronato ◽  
Matteo Frigo ◽  
Carlo Janna ◽  
Pietro Teatini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Subsidence ◽  
Methodological Approach ◽  
Subsidence Prediction

scholarly journals A novel methodological approach for land subsidence prediction through data assimilation techniques

2021 ◽  
Author(s):  
Laura Gazzola ◽  
Gazzola Ferronato ◽  
Matteo Frigo ◽  
Carlo Janna ◽  
Pietro Teatini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Subsidence ◽  
Numerical Models ◽  
Methodological Approach ◽  
Constitutive Behavior ◽  
Test Case ◽  
Combining Data ◽  
Physically Based ◽  
Synthetic Test ◽  
Adriatic Basin

AbstractAnthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and approximations are present, as in any other modeling activity of real-world phenomena. This study aims at combining data assimilation techniques with a physically-based numerical model of anthropogenic land subsidence in a novel and comprehensive workflow, to overcome the main limitations concerning the way traditional deterministic analyses use the available measurements. The proposed methodology allows to reduce uncertainties affecting the model, identify the most appropriate rock constitutive behavior and characterize the most significant governing geomechanical parameters. The proposed methodological approach has been applied in a synthetic test case representative of the Upper Adriatic basin, Italy. The integration of data assimilation techniques into geomechanical modeling appears to be a useful and effective tool for a more reliable study of anthropogenic land subsidence.

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

Modeling Regional Faults for Land Subsidence Prediction

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

A Coupled Mixed Finite Element Biot Model for Land Subsidence Prediction in the Beijing Area

2017 ◽  
Author(s):  
Massimiliano Ferronato ◽  
Laura Gazzola ◽  
Nicola Castelletto ◽  
Pietro Teatini ◽  
Lin Zhu
Keyword(s):  
Finite Element ◽  
Land Subsidence ◽  
Mixed Finite Element ◽  
Biot Model ◽  
Beijing Area ◽  
Subsidence Prediction

scholarly journals Numerical simulation of land subsidence above an off-shore Adriatic hydrocarbon reservoir, Italy, by Data Assimilation techniques

2020 ◽  
Vol 382 ◽  
pp. 449-455
Author(s):  
Matteo Frigo ◽  
Massimiliano Ferronato ◽  
Laura Gazzola ◽  
Pietro Teatini ◽  
Claudia Zoccarato ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Data Assimilation ◽  
Land Subsidence ◽  
Gas Field ◽  
Northern Adriatic ◽  
Geomechanical Properties ◽  
Hydrocarbon Reservoir ◽  
Preliminary Model ◽  
Deep Rock ◽  
Red Flag

Abstract. The numerical prediction of land subsidence above producing reservoirs can be affected by a number of uncertainties, related for instance to the deep rock constitutive behavior, geomechanical properties, boundary and forcing conditions, etc. The quality and the amount of the available observations can help reduce such uncertainties by constraining the numerical model outcome and providing more reliable estimates of the unknown governing parameters. In this work, we address the numerical simulation of land subsidence above a producing hydrocarbon field in the Northern Adriatic, Italy, by integrating the available monitoring data in the computational model with the aid of Data Assimilation strategies. A preliminary model diagnostic analysis, i.e. the χ2-test, allows for identifying the most appropriate forecast ensemble. Then, a Bayesian approach, i.e. the Red Flag technique, and a smoother formulation, i.e. the Ensemble Smoother, provide a significant reduction of the prior uncertainties. The experiment developed on a real-world gas field confirms that the integration of monitoring observations with classical geomechanical models is a valuable approach to improve the reliability of land subsidence predictions and to exploit in a systematic way the increasing amount of available measurement records.

scholarly journals Land subsidence prediction for a new urban mass rapid transit line in Hanoi

2020 ◽  
Vol 5 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Pham Huy Giao ◽  
Vu Thi Hue ◽  
Nguyen Dang Han ◽  
Nguyen Thi Hai Anh ◽  
Nguyen Ngoc Minh
Keyword(s):  
Land Subsidence ◽  
Rapid Transit ◽  
Subsidence Prediction ◽  
Mass Rapid Transit
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