Articles | Volume 382
Pre-conference publication
22 Apr 2020
Pre-conference publication |  | 22 Apr 2020

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

Matteo Frigo, Massimiliano Ferronato, Laura Gazzola, Pietro Teatini, Claudia Zoccarato, Massimo Antonelli, Anna Antonia Irene Corradi, Maria Carolina Dacome, Michela De Simoni, and Stefano Mantica

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

Baù, D., Ferronato, M., Gambolati, G., and Teatini, P.: Basin-scale compressibility of the Northern Adriatic by the radioactive marker technique, Geotechnique, 52, 605–616, 2002. a, b, c
Baù, D., Alzraiee, A., Zoccarato, C., Gambolati, G., Ferronato, M., Bottazzi, F., Mantica, S., and Teatini, P.: Testing a data assimilation approach to reduce geomechanical uncertainties in modelling land subsidence, Environmental Geotechnics, 3, 386–396, 2016. a
Chen, Y. and Oliver, D.: Levenberg-Marquardt forms of the iterative ensemble smoother for efficient hystory matching and uncertainty quantification, Comput. Geosci., 17, 689–703,, 2013. a
De Souza Neto, E., Peric, D., and Owen, D.: Computational methods for plasticity, theory and application, Wiley, UK, 2008. a
Evensen, G.: The Ensemble Kalman Filer:theoretical formulation and pratical implementation, Ocean Dynam., 53, 343–367,, 2003. a
Short summary
The numerical prediction of land subsidence above producing reservoirs can be affected by a number of uncertainties due to several factors. In this work, we use a Bayesian approach to reduce the initial uncertainties about the mechanical parameters in order to improve the reliability of land subsidence predictions. The numerical results obtained in an experiment on a real-world gas field confirms that is a valuable and effective approach.