Musa Maharramov

updated August 4, 2021

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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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