Reservoir Characterization by Integration of Time-lapse Seismic and Production Data

Huang, Xuri; Meister, Laurent; Workman, Rick

doi:10.2523/38695-ms

Cited by 12 publications

(13 citation statements)

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“…Time-lapse seismic methods have been successfully used for reservoir monitoring applications in the last two decades (Huang et al, 1997;Kretz et al, 2002;Gosselin et al, 2003;Stephen et al, 2006;Emerick et al, 2007;Dadashpour et al, 2010;Jin et al, 2011;Landa and Kumar, 2011). They are capable of detecting fluid movement in the reservoir with a high resolution.…”

Section: Introductionmentioning

confidence: 98%

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

2012

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We present a fluid-flow constrained inversion approach for integrating controlled-source electromagnetic data and production data. In this approach, we assumed that the reservoir model has been well defined from a priori knowledge obtained from other independent measurements such as seismic and/or well-logs. Our objective was to reconstruct the permeability distribution and the shape and location of the flooding front. A finite-difference reservoir simulator was used to model the water-flooding process to simulate the time-dependent production data as well as the temporal and spatial distributions of water saturation and salt concentration, which were then transformed into the reservoir resistivity distribution using a petrophysical relationship. A finite-difference frequency-domain electromagnetic forward-modeling code was then employed to simulate the controlled-source electromagnetic response. The permeability distribution was reconstructed using a multiplicativeregularized Gauss-Newton algorithm for jointly inverting controlled-source electromagnetic and production data. From the water saturation distribution, we can identify the shape and location of the fluid-front resulting from the recovery process.

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Section: Introductionmentioning

confidence: 98%

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

2012

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“…Then, one models seismic wave propagation in the reservoir as well as in the overburden. To our knowledge, only a couple of publications [20,34] have appeared where time-lapse seismic data are applied for history matching purposes.…”

Section: Introductionmentioning

confidence: 99%

Impact of time-lapse seismic data for permeability estimation

Feng¹,

Mannseth

2010

Comput Geosci

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We consider the impact of using time-lapse seismic data in addition to production data for permeability estimation in a porous medium with multiphase fluid flows, such as a petroleum reservoir under water-assisted production. Since modeling seismic wave propagation in addition to modeling fluid flows in the reservoir is quite involved, it is assumed that the time-lapse seismic data have already been inverted into fluid saturation differences (pseudoseismic data). Because an inversion process often leads to considerable error growth, we will consider pseudoseismic data with large uncertainties. The impact of pseudoseismic data is assessed through permeability estimation with and without such data and through application of some uncertainty measures for the estimated parameters. A multiscale algorithm is used for the parameter estimations, so that potential differences in attainable permeability resolution will be easily revealed. The numerical examples clearly indicate that the permeability estimation problem is stabilized at a higher level of resolution when pseudoseismic data are applied in addition to production data, even if the pseudoseismic data have large associated uncertainties. Use of the parameter uncertainty measures confirm these results.

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“…When limited geological and reservoir data were integrated in a stochastic model, studies that reduce their uncertainty had to be performed. Namely, reservoir characterization studies have been achieved by integrating static data, such as cores, logs, seismic, experiment, etc., and dynamic data, such as production, 4D seismic data (Huang et al, 1997;Xue and Datta-Gupta, 1996).…”

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confidence: 99%

Development and Application of Inverse Model Using Genetic Algorithm for Reservoir Characterization

et al. 2004

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Reservoir Characterization by Integration of Time-lapse Seismic and Production Data

Cited by 12 publications

References 0 publications

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

Impact of time-lapse seismic data for permeability estimation

Development and Application of Inverse Model Using Genetic Algorithm for Reservoir Characterization

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