Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data

Jha, Birendra; Bottazzi, F.; Wójcik, Rafał; Coccia, M.; Bechor, N.; McLaughlin, Dennis K.; Herring, T. A.; Hager, Bradford H.; Juanes, Rubén

doi:10.3997/2214-4609.20141783

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…Honoring the timing of fault slip d obs then increases the likelihood of a lower dynamic friction coefficient μ d (which decreases the frictional strength of the fault) and a higher reservoir permeability k (which accelerates pore pressure increase at the fault from fluid injection). More generally, the fault slip is also affected by mechanical parameters such as the rock compressibility and Poisson's ratio (Jha et al, ), which in the present context we assume to be known. We plan to include uncertainties in mechanical parameters in our future studies.…”

Section: Numerical Resultsmentioning

confidence: 99%

Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection‐Induced Seismicity

Jagalur-Mohan

Jha

Wang

et al. 2018

Geophysical Research Letters

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Characterizing the rheological properties of faults and the evolution of fault friction during seismic slip are fundamental problems in geology and seismology. Recent increases in the frequency of induced earthquakes have intensified the need for robust methods to estimate fault properties. Here we present a novel approach for estimation of aquifer and fault properties, which combines coupled multiphysics simulation of injection‐induced seismicity with adaptive surrogate‐based Bayesian inversion. In a synthetic 2‐D model, we use aquifer pressure, ground displacements, and fault slip measurements during fluid injection to estimate the dynamic fault friction, the critical slip distance, and the aquifer permeability. Our forward model allows us to observe nonmonotonic evolutions of shear traction and slip on the fault resulting from the interplay of several physical mechanisms, including injection‐induced aquifer expansion, stress transfer along the fault, and slip‐induced stress relaxation. This interplay provides the basis for a successful joint inversion of induced seismicity, yielding well‐informed Bayesian posterior distributions of dynamic friction and critical slip. We uncover an inverse relationship between dynamic friction and critical slip distance, which is in agreement with the small dynamic friction and large critical slip reported during seismicity on mature faults.

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Section: Numerical Resultsmentioning

confidence: 99%

Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection‐Induced Seismicity

Jagalur-Mohan

Jha

Wang

et al. 2018

Geophysical Research Letters

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“…The outcome highlights that the measurement effectiveness in constraining the reservoir compressibility can be quite different for the different reservoir compartments, likely depending on the pressure change history and the influence of other surrounding reservoirs. An ES estimator with multiphase flow and geomechanics simulator as forward models was implemented by Jha et al [] to assimilate pressure and PSI data for reducing uncertainty in the prior distributions of rock properties of a UGS gas reservoir in northern Italy. The results indicate that the spread of the posterior displacement ensemble is significantly reduced after assimilation of fluid pressure and surface displacement data.…”

Section: Discussionmentioning

confidence: 99%

“…In the majority of the applications, such processes are characterized by variable time and space scales, e.g., tectonics, fluid withdrawal, and injection, target instability, differently contributing to the measured values, e.g., Tosi et al []. Hence, it is preferable to use a subset of “reliable” records, in the sense that they show a displacement clearly related to the investigated process at hand, rather than a larger data set with a higher noise level, as is done, for example, by Jha et al []. For other possible approaches to irregularly subsample large data sets, for example, taking into account data spatial correlations, see Sudhaus and Jónsson [].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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Although the beginning of reservoir geomechanics dates back to the late 1960s, only recently stochastical geomechanical modelling has been introduced into the general framework of reservoir operational planning. In this study, the ensemble smoother (ES) algorithm, i.e., an ensemble‐based data assimilation method, is employed to reduce the uncertainty of the constitutive parameters characterizing the geomechanical model of an underground gas storage (UGS) field situated in the upper Adriatic sedimentary basin (Italy), the Lombardia UGS. The model is based on a nonlinear transversely isotropic stress‐strain constitutive law and is solved by 3‐D finite elements. The Lombardia UGS experiences seasonal pore pressure change caused by fluid extraction/injection leading to land settlement/upheaval. The available observations consist of vertical and horizontal time‐lapse displacements accurately measured by persistent scatterer interferometry (PSI) on RADARSAT scenes acquired between 2003 and 2008. The positive outcome of preliminary tests on simplified cases has supported the use of the ES to jointly assimilate vertical and horizontal displacements. The ES approach is shown to effectively reduce the spread of the uncertain parameters, i.e., the Poisson's ratio, the ratio between the horizontal and vertical Young and shear moduli, and the ratio between the virgin loading (I cycle) and unloading/reloading (II cycle) compressibility. The outcomes of the numerical simulations point out that the updated parameters depend on the assimilated measurement locations as well as the error associated to the PSI measurements. The parameter estimation may be improved by taking into account possible model and/or observation biases along with the use of an assimilation approach, e.g., the Iterative ensemble smoother, more appropriate for nonlinear problems.

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“…Since this work mainly focuses on the mechanical response of the solid phase, the pore fluid pressure p is previously computed, by solving separately a fluid diffusion model. Subsequently, the results obtained by the fluid diffusion model are applied to the geomechanical model, following a 1‐way coupling strategy as is usually done in hydrogeological and petroleum engineering . Hence, the stresses appearing in the model, and therefore in the results of the numerical simulations in the paper, are always considered effective.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

A two‐invariant pseudoelastic model for reservoir compaction

Spiezia

Ferronato

Janna

et al. 2017

Num Anal Meth Geomechanics

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Summary Predicting the deformations of deep reservoirs due to fluid withdrawal/injection is a challenging task that could have important environmental, social, and economical impacts. Finite element models, if endowed with an appropriate constitutive law, represent a useful tool for computing the displacements, the deformations, and the stress distributions in reservoir applications. Several studies show that hypoelastic laws, based on a stress‐dependent vertical compressibility, are able to provide accurate results, confirmed by in situ and satellite measurements. On the other hand, such laws present some weaknesses related to the numerical implementation, in particular due to the nonsymmetry of the tangent operator. This paper presents a new constitutive model based on 2 invariants (the mean normal and deviatoric stresses), characterized by a variable pressure‐dependent bulk modulus K. This constitutive law allows for overcoming most shortcomings of the hypoelastic law, although preserving the same accuracy, reliability, and ease of use and calibration. This paper presents a procedure to identify the parameters of the new model, starting from the typically available data on the vertical compressibility. Numerical results show a good agreement between the 2 laws, suggesting the proposed approach as a valid alternative in reservoir applications.

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Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data

Cited by 10 publications

References 11 publications

Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection‐Induced Seismicity

Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection‐Induced Seismicity

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

A two‐invariant pseudoelastic model for reservoir compaction

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