Joint inversion of time-lapse crosswell electromagnetic, seismic, and production data for reservoir monitoring and characterization

Liang, Lin; Abubakar, Aria; Habashy, Tarek M.

doi:10.1190/segam2012-0261.1

Cited by 3 publications

(7 citation statements)

References 31 publications

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“…However, when compared with Figure 4(e) in Liang et al (2012), the permeability image from current inversion is worse. This is fair because the porosity in this study is also to be inverted while the one used in Liang et al (2012) is from the true model. The distribution of water saturation has also been well reconstructed.…”

Section: Numerical Examplementioning

confidence: 77%

“…The vertical cross sections of the electromagnetic and acoustic models and the survey settings are shown in Figure 2. Liang et al (2012) has shown the results from inversion of each measurement separately, as well as the results from joint inversion of time-lapse seismic, time-lapse electromagnetic, and production data. However, only permeability was inverted in all those cases, assuming that porosity has been accurately obtained from other sources or previous study.…”

Section: Numerical Examplementioning

confidence: 99%

“…The data misfit cost function and the Jacobian matrix J J J λ k = ∂ s/∂ λ k has been detailed in Liang et al (2012). The derivation of the Jacobian matrix for parameter λ ϕ is slightly different to λ k .…”

Section: Modeling and Inversionmentioning

confidence: 99%

“…Hoversten et al (2006), Chen et al (2007), and Gao et al (2012) presented another joint inversion approach that links the seismic properties and resistivity using a variety of petrophysical relations. Liang et al (2012) presented a work jointly interpreting timelapse seismic, time-lapse electromagnetic, and production data using a fully integrated approach, in which the different physical models are linked using appropriate petrophysical relations. This work inverted only permeability assuming that porosity has been obtained from other sources.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

Liang

Abubakar

Habashy

2013

SEG Technical Program Expanded Abstracts 2013

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We present an approach for the simultaneous estimation of permeability and porosity from time-lapse seismic, time-lapse electromagnetic, and production data. Permeability and porosity are some of the most important parameters for reservoir characterization because of their significant influence over oil recovery. Both permeability and porosity has a big footprint on the distribution of saturation and pressure in the reservoir during production, which can be snapshot by the time-lapse seismic and time-lapse electromagnetic measurements. Production data is the traditional source used for reservoir model conditioning but only provides limited constraint to the reservoir model that is very localized to the near-wellbore regions. In contrast, time-lapse seismic and time-lapse electromagnetic can impose additional constraints to the reservoir model conditioning problem covering the inter-well regions. We have developed a reservoir centric workflow to allow jointly invert these multiphysical data. The non-uniqueness of this inverse problem is hence effectively reduced. We use a synthetic case to demonstrate that the proposed approach can significantly improve the estimation of permeability and porosity as well as the fluid-front movement monitoring.

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Section: Numerical Examplementioning

confidence: 77%

Section: Numerical Examplementioning

confidence: 99%

Section: Modeling and Inversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

Liang

Abubakar

Habashy

2013

SEG Technical Program Expanded Abstracts 2013

Self Cite

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“…Despite these significant advances, linking the retrieved electromagnetic data to the reservoir models has been a challenge. Significant research has been carried out to invert the electromagnetic data to resistivity data (see, e.g., Habashy and Abubakar, 2004;Abubakar et al, 2008;Habashy et al, 2012;Liang et al, 2012). However the nonuniqueness of the solution (Menke, 2012), and the significant computational requirement, in addition to the necessity of manual fine tuning, limits the applicability of these techniques for efficient reservoir history matching and forecasting.…”

Section: Introductionmentioning

confidence: 98%

EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs

Katterbauer

Hoteit

Sun

2014

Journal of Petroleum Science and Engineering

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Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Asnaashari

Brossier

Garambois

et al. 2014

Geophysical Prospecting

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Full‐waveform inversion is an appealing technique for time‐lapse imaging, especially when prior model information is included into the inversion workflow. Once the baseline reconstruction is achieved, several strategies can be used to assess the physical parameter changes, such as parallel difference (two separate inversions of baseline and monitor data sets), sequential difference (inversion of the monitor data set starting from the recovered baseline model) and double‐difference (inversion of the difference data starting from the recovered baseline model) strategies. Using synthetic Marmousi data sets, we investigate which strategy should be adopted to obtain more robust and more accurate time‐lapse velocity changes in noise‐free and noisy environments. This synthetic application demonstrates that the double‐difference strategy provides the more robust time‐lapse result. In addition, we propose a target‐oriented time‐lapse imaging using regularized full‐waveform inversion including a prior model and model weighting, if the prior information exists on the location of expected variations. This scheme applies strong prior model constraints outside of the expected areas of time‐lapse changes and relatively less prior constraints in the time‐lapse target zones. In application of this process to the Marmousi model data set, the local resolution analysis performed with spike tests shows that the target‐oriented inversion prevents the occurrence of artefacts outside the target areas, which could contaminate and compromise the reconstruction of the effective time‐lapse changes, especially when using the sequential difference strategy. In a strongly noisy case, the target‐oriented prior model weighting ensures the same behaviour for both time‐lapse strategies, the double‐difference and the sequential difference strategies and leads to a more robust reconstruction of the weak time‐lapse changes. The double‐difference strategy can deliver more accurate time‐lapse variation since it can focus to invert the difference data. However, the double‐difference strategy requires a preprocessing step on data sets such as time‐lapse binning to have a similar source/receiver location between two surveys, while the sequential difference needs less this requirement. If we have prior information about the area of changes, the target‐oriented sequential difference strategy can be an alternative and can provide the same robust result as the double‐difference strategy.

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Joint inversion of time-lapse crosswell electromagnetic, seismic, and production data for reservoir monitoring and characterization

Cited by 3 publications

References 31 publications

Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs

Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

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