Pietro Teatini - Articles in Int. Journals

Data assimilation of surface displacements to improve geomechanical parameters of gas storage reservoirs

C. Zoccarato, M. Ferronato, G. Gambolati, P. Teatini
Dept. of Civile, Environmental and Architectural Engineering, University of Padova, Padova, Italy

D. Bau'
Dept. Civil and Structural Engineering, University of Sheffield, Sheffield, UK

F. Bottazzi, S. Mantica
Development, Operations and Technology, eni S.p.A., San Donato Milanese, Italy

ABSTRACT
The geomechanical analysis of a highly compartmentalized reservoir is performed to simulate the seafloor subsidence due to gas production. The available observations over the hydrocarbon reservoir consist of bathymetric surveys carried out before and at the end of a 10-yr production life. The main goal is the calibration of the reservoir compressibility c_M, that is, the main geomechanical parameter controlling the surface response. Two conceptual models are considered: in one (i) c_M varies only with the depth and the vertical effective stress (heterogeneity due to lithostratigraphic variability); in another (ii) c_M varies also in the horizontal plane, that is, it is spatially distributed within the reservoir stratigraphic units. The latter hypothesis accounts for a possible partitioning of the reservoir due to the presence of sealing faults and thrusts that suggests the idea of a block heterogeneous system with the number of reservoir blocks equal to the number of uncertain parameters. The method applied here relies on an ensemble-based data assimilation (DA) algorithm (i.e. the ensemble smoother, ES), which incorporates the information from the bathymetric measurements into the geomechanical model response to infer and reduce the uncertainty of the parameter c_M. The outcome from conceptual model (i) indicates that DA is effective in reducing the cM uncertainty. However, the maximum settlement still remains underestimated, while the areal extent of the subsidence bowl is overestimated. We demonstrate that the selection of the heterogeneous conceptual model (ii) allows to reproduce much better the observations thus removing a clear bias of the model structure. DA allows significantly reducing the c_M uncertainty in the five blocks (out of the seven) characterized by large volume and large pressure decline. Conversely, the assimilation of land displacements only partially constrains the prior c_M uncertainty in the reservoir blocks marginally contributing to the cumulative seafloor subsidence, that is, blocks with low pressure.