A Data-estimation Based Approach for Quasi-continuous Reservoir Monitoring using Sparse Surface Seismic Data

Arogunmati, Adeyemi; Harris, Jerry M.

doi:10.3997/2214-4609.201401205

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Arogunmati () and Arogunmati and Harris () showed how the quasi‐continuous data‐estimation problem can be expressed as:

{boldd}_{c}^{k} = {boldd}_{s}^{k} + {boldd}_{u}^{k},

where

d_{s}^{k}

is the accumulated sparse data set at survey/monitor time k and

d_{u}^{k}

is the accumulated unrecorded data set at survey/monitor time k . An accumulated data set consists in the baseline and time‐lapse surveys merged along the slow‐time axis.…”

Section: Methodsmentioning

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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“…Arogunmati () and Arogunmati and Harris () showed how the quasi‐continuous data‐estimation problem can be expressed as:

{boldd}_{c}^{k} = {boldd}_{s}^{k} + {boldd}_{u}^{k},

where

d_{s}^{k}

is the accumulated sparse data set at survey/monitor time k and

d_{u}^{k}

is the accumulated unrecorded data set at survey/monitor time k . An accumulated data set consists in the baseline and time‐lapse surveys merged along the slow‐time axis.…”

Section: Methodsmentioning

confidence: 99%

“…Because our approach is estimation‐based and assumes no external influences, it is recommended that new baseline surveys be acquired when, for example, new injector or producer wells begin operation. The practicability of this approach has been described in Arogunmati () and Arogunmati and Harris ().…”

Section: Introductionmentioning

confidence: 99%

Quasi‐continuous reservoir monitoring with surface seismic data

Arogunmati

Harris

2013

Geophysical Prospecting

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“…Another potential application of image integration is quasi-continuous or real-time seismic monitoring. The concept was applied to missing data in an approach to quasi-continuous 4-D monitoring (Arogunmatic and Harris, 2009Harris, , 2010. In this situation we need to collect and process data much more frequently than in conventional 4-D seismic.…”

Section: Introductionmentioning

confidence: 99%

“…We demonstrate this application via a synthetic example. Arogunmati and Harris (2010) used a data-estimation-based method for quasi-continuous monitoring using sparse surface data.…”

Section: Introductionmentioning

confidence: 99%

Image integration with learned dictionaries and application to seismic monitoring

Quan

Zhu

Harris

et al. 2011

SEG Technical Program Expanded Abstracts 2011

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Sparse coding can be applied to train an overcomplete dictionary on time-lapse seismic data or images. The learned dictionary generally consists of sparse representations of one or more images. We then use such sparse representations, along with L 1-regularization techniques, to predict missing values in seismic images by solving an inverse problem. The practical outcome of the proposed methodology can be a significant reduction in field operational costs by requiring only sparse instead of dense surveys, and by integrating in the seismic images the information captured by the learned dictionary from previous time-lapse and baseline images. A synthetic example is presented to test the method.

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Super resolution of time-lapse seismic images

2011

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We present results of an on-going project to assess the applicability in reflection seismology of emerging super resolution techniques pioneered in digital photography. Our approach involves: (1) construction of a forward model connecting low resolution seismic images to high resolution ones, and (2) solution of a Tikhonov-regularized ill conditioned optimization problem to construct a high resolution image from several lower resolution counterparts; the high and low resolution images derived, respectively, from dense and sparse seismic surveys.

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A Data-estimation Based Approach for Quasi-continuous Reservoir Monitoring using Sparse Surface Seismic Data

Cited by 3 publications

References 5 publications

Quasi‐continuous reservoir monitoring with surface seismic data

Quasi‐continuous reservoir monitoring with surface seismic data

Image integration with learned dictionaries and application to seismic monitoring

Super resolution of time-lapse seismic images

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