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[2021-03-01]


[2021-03-01]

Sparse Pseudo-sources in Volcanic Stressing

Check out my talk given at the Hildebrand Department of Petroleum and Geosystems Engineering of the University of Texas at Austin. ...display full article text



[2019-12-02]


[2019-12-02]

Machine Learning in Time-lapse Inverse Theory

Check out my TLE paper on using neural networks for kinematic time-lapse analysis in combination with robust time-lapse full-waveform inversion. ...display full article text


Time-lapse FWI and Machine Learning


[2019-09-07]


[2019-09-07]

Integrated Kinematic Time-lapse Inversion Workflow Leveraging Full-waveform Inversion and Machine Learning

Check out my presentation in the post-convention workshop on Machine Learning and Data Analytics at the annual SEG 2019 convention. ...display full article text



[2018-05-01]


[2018-05-01]

Monitoring of cyclic steam stimulation by inversion of surface tilt measurements

Inversion of induced reservoir pore-pressure changes from deformation measurements may provide a potentially powerful reservoir-monitoring tool if the issues of measurement noise, uncertainty in model parameterization, and numerical accuracy and stability can be resolved. We discuss inverting injection-induced reservoir pressure changes from observable surface deformations using a linear poroelastostatic model of a subsurface reservoir. ...display full article text


Monitoring of cyclic steam stimulation by inversion of surface tilt measurements


[2017-08-17]


[2017-08-17]

Multiscale time-domain time-lapse full-waveform inversion with a model-difference regularization

Another one of my upcoming SEG 2017 talks in Houston.


Presentation Date: Wednesday, September 27, 2017
Start Time: 10:10 AM
Location: 361F

We present a multiscale time-lapse full-waveform inversion (4D FWI) technique based on a cascaded time-domain simultaneous inversion of multiple surveys with a model-difference regularization. In our cascaded approach, different model scales are recovered using different objective functions and regularization penalties. We apply our method to a synthetic example, and demonstrate a robust recovery of production-induced velocity changes in the presence of repeatability issues and errors in the amplitude information.


Multiscale time-domain time-lapse full-waveform inversion with a model-difference regularization (full article PDF)


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