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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 372
Proc. IAHS, 372, 351–356, 2015
https://doi.org/10.5194/piahs-372-351-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 372, 351–356, 2015
https://doi.org/10.5194/piahs-372-351-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 Nov 2015

12 Nov 2015

Estimate of a spatially variable reservoir compressibility by assimilation of ground surface displacement data

C. Zoccarato1, D. Baù2, F. Bottazzi3, M. Ferronato1, G. Gambolati1, S. Mantica3, and P. Teatini1 C. Zoccarato et al.
  • 1Department of Civil, Architectural and Environmental Engineering, University of Padova, Padova, Italy
  • 2Department of Civil & Structural Engineering, The University of Sheffield, Sheffield, UK
  • 3Development, Operations & Technology, eni S. p. A., San Donato Milanese, Italy

Abstract. Fluid extraction from producing hydrocarbon reservoirs can cause anthropogenic land subsidence. In this work, a 3-D finite-element (FE) geomechanical model is used to predict the land surface displacements above a gas field where displacement observations are available. An ensemble-based data assimilation (DA) algorithm is implemented that incorporates these observations into the response of the FE geomechanical model, thus reducing the uncertainty on the geomechanical parameters of the sedimentary basin embedding the reservoir. The calibration focuses on the uniaxial vertical compressibility cM, which is often the geomechanical parameter to which the model response is most sensitive. The partition of the reservoir into blocks delimited by faults motivates the assumption of a heterogeneous spatial distribution of cM within the reservoir. A preliminary synthetic test case is here used to evaluate the effectiveness of the DA algorithm in reducing the parameter uncertainty associated with a heterogeneous cM distribution. A significant improvement in matching the observed data is obtained with respect to the case in which a homogeneous cM is hypothesized. These preliminary results are quite encouraging and call for the application of the procedure to real gas fields.

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