Temporal integration of seismic traveltime tomography

Ajo‐Franklin, Jonathan; Urban, Jaime; Harris, Jerry M.

doi:10.1190/1.2148222

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Several techniques have been proposed for geophysical model reconstruction from time‐lapse sparse data. These include dynamic imaging techniques, for example DynaSIRT (Santos and Harris ), ensemble Kalman filter dynamic inversion (Quan and Harris ) and temporal regularization joint inversion (Ajo‐Franklin, Urban and Harris ). DynaSIRT is a dynamic iterative reconstruction technique that uses weighted data from other surveys together with the data from the survey of interest to iteratively reconstruct a geophysical model for the survey of interest.…”

Section: Introductionmentioning

confidence: 99%

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Quasi‐continuous reservoir monitoring with surface seismic data

Arogunmati

Harris

2013

Geophysical Prospecting

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A B S T R A C TWe present an approach that creates the possibility of reservoir monitoring on a quasi-continuous basis using surface seismic data. Current strategies and logistics for seismic data acquisition impose restrictions on the calendar-time temporal resolution obtainable for a given surface-seismic time-lapse monitoring program. One factor that restricts the implementation of a quasi-continuous monitoring program using conventional strategies is the time it takes to acquire a complete survey. Here quasicontinuous monitoring describes the process of reservoir monitoring at short-time intervals. Our approach circumvents the restriction by requiring only a subset of complete survey data each time an image of the reservoir is needed using surface seismic data. Ideally, the time interval between survey subset acquisitions should be short so that changes in the reservoir properties are small. The accumulated data acquired are used to estimate the unavailable data at the monitor survey time and the combined recorded and estimated data are used to produce an image of the subsurface for monitoring. We will illustrate the effectiveness of our approach using 2D and 3D synthetic seismic data and 3D field seismic data. We will explain the benefits and drawbacks of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

“…The joint inversion approach presented by Ajo‐Franklin et al . () relies on regularization along the slow‐time dimension to account for the sparsity of the data.…”

Section: Introductionmentioning

confidence: 99%

Quasi‐continuous reservoir monitoring with surface seismic data

Arogunmati

Harris

2013

Geophysical Prospecting

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“…Several techniques have been proposed for geophysical model construction from time-lapse sparse data. These include dynamic imaging techniques, for example, DynaSIRT (Santos and Harris, 2008), ensemble Kalman filter dynamic inversion (Quan and Harris, 2008), and temporal regularization joint inversion (Ajo-Franklin et al, 2005). DynaSIRT is a dynamic iterative reconstruction technique that uses weighted data from previous surveys together with the data from the time of interest to iteratively construct a geophysical model.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, dynamic inversion with ensemble Kalman filters updates the geophysical model using current sparse data. The joint inversion approach presented by Ajo-Franklin et al (2005) relies on regularization in slow time to account for the data sparsity.…”

Section: Introductionmentioning

confidence: 99%

An effective crosswell seismic traveltime-estimation approach for quasi-continuous reservoir monitoring

Arogunmati

Harris

2012

GEOPHYSICS

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We present an iterative approach for quasi-continuous time-lapse seismic reservoir monitoring. This approach involves recording sparse data sets frequently, rather than complete data sets infrequently. In other words, it involves acquiring a completely sampled baseline data set followed by sparse monitor data sets at short calendar-time intervals. We use the term "sparse" to describe a data set that is a small fraction of what would normally be recorded in the field to reconstruct a high-spatial-resolution image of the subsurface. Each monitor data set could be as little as 2% of a single, complete conventional data set. The series of recorded time-lapse data sets is then used to estimate missing, unrecorded data in the sparse data sets. The approach was tested on synthetic and field crosswell traveltime data sets. Results show that this approach can be effective for quasi-continuous reservoir monitoring. Also, the accuracy of the estimated data increases as more sparse data sets are added to the time-lapse data series. Finally, a moving estimation window can be used to reduce computational effort for estimating data.

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Inversion for non‐smooth models with physical bounds

Routh

Sen

et al. 2007

SEG Technical Program Expanded Abstracts 2007

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SummaryGeological processes produce structures at multiple scales. A discontinuity in the subsurface can occur due to layering, tectonic activities such as faulting, folding and fractures. Traditional approaches to invert geophysical data employ smoothness constraints. Such methods produce smooth models and thefore sharp contrasts in the medium such as lithological boundaries are not easily discernible. The methods that are able to produce non-smooth models, can help interpret the geological discontinuity. In this paper we examine various approaches to obtain non-smooth models from a finite set of noisy data. Broadly they can be categorized into approaches: (1) imposing non-smooth regularization in the inverse problem and (2) solve the inverse problem in a domain that provides multi-scale resolution, such as wavelet domain. In addition to applying non-smooth constraints, we further constrain the inverse problem to obtain models within prescribed physical bounds. The optimization with non-smooth regularization and physical bounds is solved using an interior point method. We demonstrate the applicability and usefulness of these methods with realistic synthetic examples and provide a field example from crosswell radar data.

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Temporal integration of seismic traveltime tomography

Cited by 10 publications

References 9 publications

Quasi‐continuous reservoir monitoring with surface seismic data

Quasi‐continuous reservoir monitoring with surface seismic data

An effective crosswell seismic traveltime-estimation approach for quasi-continuous reservoir monitoring

Inversion for non‐smooth models with physical bounds

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