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

<p>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 obser...

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Bibliographic Details
Main Authors: M. Frigo, M. Ferronato, L. Gazzola, P. Teatini, C. Zoccarato, M. Antonelli, A. A. I. Corradi, M. C. Dacome, M. De Simoni, S. Mantica
Format: Article
Language:English
Published: Copernicus Publications 2020-04-01
Series:Proceedings of the International Association of Hydrological Sciences
Online Access:https://www.proc-iahs.net/382/449/2020/piahs-382-449-2020.pdf
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Summary:<p>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 <span class="inline-formula"><i>χ</i><sup>2</sup></span>-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.</p>
ISSN:2199-8981
2199-899X