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[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.


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.


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)


[2017-08-17]


[2017-08-17]

Time-domain broadband phase-only full-waveform inversion with implicit shaping

My upcoming talk at SEG 2017 in Houston.


Presentation Date: Monday, September 25, 2017
Start Time: 3:30 PM
Location: 361F

We propose a new full-waveform inversion (FWI) method that approximates broadband tomographic inversion and has a reduced sensitivity to errors in the observed-data amplitude information. The method is based on fitting the observed-data phase spectrum while automatically shaping forward-modeled wave fields to the observed-data amplitude spectrum. This is achieved by using a phase-only objective function that allows broadband time-domain inversion of the observed-data phase information. We demonstrate our methodís reduced sensitivity to dynamic information under the traveltime approximation, and compare the new objective function to the normalized L2 FWI objective function in an experiment on synthetic data with a frequency-dependent attenuation.


Time-domain broadband phase-only full-waveform inversion with implicit shaping (full article PDF)


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