Efficient global optimization of reservoir geomechanical parameters based on
synthetic aperture radar-derived ground displacements
F. Comola
School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne,
Lausanne, Switzerland and M3E S.r.l., Padova, Italy.
C. Janna, P. Teatini
Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy
and M3E S.r.l., Padova, Italy
A. Lovison
Department of Mathematics, University of Padova, Italy and M3E S.r.l., Padova, Italy
M. Minini, A. Tamburini
Tele-Rilevamento Europa, Milano, Italy
ABSTRACT
When large volumes of fluids are removed from or injected
into underground formations for, e.g., hydrocarbon and water
production, CO2 storage, gas storage, and geothermal energy
exploitation, monitoring of surface deformations coupled to
numerical modeling improves our understanding of reservoir
behavior. The ability to accurately simulate surface displacements,
however, is often impaired by limited information on reservoir
geometry, waterdrive strength, and fluid-geomechanical
parameters characterizing the geologic formations of interest.
We have investigated the ability of efficient global optimization
(EGO) to reduce the parameter uncertainties usually affecting
geomechanical modeling. EGO is used to identify the parameter
set that minimizes the difference in land displacements obtained
from synthetic aperture radar (SAR)-derived measurements and
3D geomechanical modeling. We have tested the approach on
the Tengiz giant oil field, Kazakhstan, where large uncertainties
affect our knowledge of geomechanical parameters
and pore pressure evolution. SqueeSAR on ENVISAT and
RADARSAT-1 images acquired between 2004 and 2007 provided
a set of high-precision, high-areal-density subsidence
measurements of the test site. Based on the available information,
a 3D geomechanical model of the reservoir has been developed
using the elastoplastic finite-element code GEPS3D.
Our results indicated that EGO efficiently identifies the global
optimum in the parameter space, yielding a significant reduction
in the difference between modeled and measured land
subsidence. The match between simulated and SAR-measured
horizontal displacements was developed as validation of the
EGO calibration, which thus proved an effective and rather
inexpensive method for the simultaneous management of several
uncertainties and the reliable quantification of the rock
properties.