Unraveling reservoir compaction parameters through the inversion of surface subsidence observations

Muntendam-Bos, A.G.; Fokker, P.A.

doi:10.1007/s10596-008-9104-z

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…A sensitivity analysis identified the peat oxidation rate to be most influential. Therefore, a full Monte Carlo analysis of the uncertainties of these parameters was performed [see Muntendam‐Bos et al , 2008; Muntendam‐Bos and Fokker , 2008]. The uncertainties of the remaining 60 parameters were mapped with a Latin Hypercube method [ Iman and Conover , 1982].…”

Section: Methodsmentioning

confidence: 99%

Unraveling shallow causes of subsidence

Muntendam-Bos¹,

Kleuskens²,

Bakr

et al. 2009

Geophysical Research Letters

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[1] The surface movement in the Krimpenerwaard polder in The Netherlands results from primary or hydrodynamic settlement/swelling, secondary or creep settlement/swelling, and peat oxidation. We used surface movement measurements in a Bayesian inversion scheme to disentangle the contribution of these three processes to the subsidence. The prior information, including spatial correlations, appeared to be crucial in our procedure. This prior information was derived from geological modeling incorporating the most important uncertainties. The inversion procedure allowed us to quantify the contributions of the three processes with unprecedented accuracy. Surface rise in the data was related to swelling of the clay layers, even though swelling was considered infeasible in the prior information. Despite this, the irreversible nature of peat oxidation was preserved. The improved subsurface description offers prospects for identification of incorrect information and for better assessments of the effects of water management. Citation: Muntendam-Bos, A. G.,

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Section: Methodsmentioning

confidence: 99%

Unraveling shallow causes of subsidence

Muntendam-Bos¹,

Kleuskens²,

Bakr

et al. 2009

Geophysical Research Letters

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“…Measurements of compaction and subsidence have long been recognised as a valuable source of information about reservoir properties and dynamics (Mobach & Gussinklo 1994; Marchina, 1996; Du & Olson, 2001; Fokker, 2002; Du et al, unpublished, 2005; Muntendam-Bos & Fokker, 2009; Vasco et al, 2010; Zoccarato et al, 2016). Extracting this information, however, is usually a highly ill-conditioned or even ill-posed problem.…”

Section: Introductionmentioning

confidence: 99%

The future of subsidence modelling: compaction and subsidence due to gas depletion of the Groningen gas field in the Netherlands

Thienen-Visser¹,

Fokker²

2017

Netherlands Journal of Geosciences

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The Groningen gas field has shown considerable compaction and subsidence since starting production in the early 1960s. The behaviour is understood from the geomechanical response of the reservoir pressure depletion. By integrating surface movement measurements and modelling, the model parameters can be constrained and understanding of the subsurface behaviour can be improved. Such a procedure has been employed to formulate new compaction and subsidence forecasts. The results are put into the context of an extensive review of the work performed in this field, both in Groningen and beyond. The review is used to formulate a way forward designed to integrate all knowledge in a stochastic manner.

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“…To date, data assimilation in geomechanics has been seldom used. Reservoir compaction was estimated through a Bayesian inversion scheme from subsidence observations in Muntendam‐Bos and Fokker []. In Wilschut et al [] subsidence data measured over nine leveling campaigns were used with static pressure data to estimate fault trasmissibilities via the EnKF.…”

Section: Introductionmentioning

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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Unraveling reservoir compaction parameters through the inversion of surface subsidence observations

Cited by 10 publications

References 28 publications

Unraveling shallow causes of subsidence

Unraveling shallow causes of subsidence

The future of subsidence modelling: compaction and subsidence due to gas depletion of the Groningen gas field in the Netherlands

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

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