Abdullah Alali

PhD holder

King Abdullah University of Science and Technology (KAUST)

Biography

I am a Ph.D. holder specializing in Artificial Intelligence (AI) within the field of Geophysics. I received my Ph.D. degree in 2023 in the Earth Science and Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. I was part of the seismic wave analysis group (SWAG) supervised by Tariq Alkhalifah. I was also a member of deepwave consortium, which focuses on machine (deep) learning applications in geophysics. My Ph.D. research focuses on applying machine learning techniques in full-waveform inversion and in processing 4D seismic data. I received my M.S. degree in Earth Science and Engineering at KAUST in 2018 and my B.S. degree in Geophysics in 2016 at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia.

Interests

Artificial intellegence
Machine learning
Full-wavefrom inversion
4D (time-lapse) seismic
Seismic imaging and velocity analysis

Education

PhD in Earth Science and Engineering, 2023
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
M.S in Earth Science and Engineering, 2018
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
BSc in Geophysics, 2016
King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia

News

2023

[2023-11-08] Ph.D dissertation defense.
[2023-06-08] Attending and presenting in EAGE annual meeting
[2023-02-20] Attending and presenting in MEOS-GEO
[2023-01-20] Created this website

Featured Publications

Integrating U-net with full-waveform inversion for an efficient salt body construction

Inversion workflow for salt bodies from poor inital model, limited offsets and with frequencies starting from 5 Hz. Multistage flooding and unflooding is implimented using U-net networks within the multiscale approach in the FWI.

Abdullah Alali, Tariq Alkhalifah

Integrating U-net with full-waveform inversion for an efficient salt body construction

Time-lapse data matching using a recurrent neural network approach

Conventionally time-lapse surveys have discripency in the signal due to the overburden changes. A data matching between different surveys is performed using recurrent neural network.

Abdullah Alali, Vladimir Kazei, Bingbing Sun, Tariq Alkhalifah

Deep learning unflooding for robust subsalt waveform inversion

Automatic unflooding the salt body in full-waveform inversion by utilizing deep learning tools. We analyze three scenarios in creating the training datasets and their effects in the subsalt inversion.

Abdullah Alali, Vladimir Kazei, Mahesh Kalita, Tariq Alkhalifah

Deep learning unflooding for robust subsalt waveform inversion

The effectiveness of a pseudo inverse extended born operator to handle lateral heterogeneity for imaging and velocity analysis applications

Analysis of the application of a pseudo inverse extended Born operator in a complex hetrogenious models. The operator is also tested in a DSO optimization to obtained a smooth velocity model for imaging and full-waveform inversion.

Abdullah Alali, Bingbing Sun, Tariq Alkhalifah

The effectiveness of a pseudo inverse extended born operator to handle lateral heterogeneity for imaging and velocity analysis applications

Recent Publications

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Abdullah Alali, Tariq Alkhalifah (2022). Integrating U-net with full-waveform inversion for an efficient salt body construction. In Second International Meeting for Applied Geoscience & Energy.

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Abdullah Alali, Vladimir Kazei, Bingbing Sun, Tariq Alkhalifah (2022). Time-lapse data matching using a recurrent neural network approach. Geophysics.

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Abdullah Alali, Tariq Alkalifah (2022). Learning to Unflood the Salt in Full-Waveform Inversion, Application on Vintage GOM Data. In 83rd EAGE Annual Conference & Exhibition.

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Abdullah Alali, Vladimir Kazei, Mahesh Kalita, Tariq Alkhalifah (2022). Deep learning unflooding for robust subsalt waveform inversion. Geophysical prospecting.

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Abdullah Alali, Fatai Anifowose (2021). Seismic velocity modeling in the digital transformation era: a review of the role of machine learning. Journal of Petroleum Exploration and Production Technology.

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