Name: Marcus Vinicius de Oliveira Saraiva
Type: MSc dissertation
Publication date: 27/10/2022

Namesort descending Role
Thomas Walter Rauber Advisor *

Examining board:

Namesort descending Role
Antônio Cosme Del Rey External Examiner *
Avelino Forechi Silva External Examiner *
Marcos de Carvalho Machado External Examiner *
Renato Antônio Krohling Internal Examiner *
Thomas Walter Rauber Advisor *

Summary: In the oil and gas industry, estimating an accurate seismic velocity model is an essential step in seismic
processing, reservoir characterization, and hydrocarbon volume calculation.
Full Waveform Inversion (FWI) velocity modeling is an iterative advanced technique that provides a high-resolution
seismic interval velocity model, although at a very high computational cost due to the wave-equation-based numerical
simulations required at each FWI iteration. This study proposes a method of generating seismic velocity models, as
detailed as those obtained through FWI, however using a fraction of the time and computational resources spent in
traditional FWI velocity modeling. To do so, a 3D conditional Generative Adversarial Network (cGAN), known as pix2pix
was used. The cGAN training was performed using three conditional inputs, Seismic Amplitude, Tomografic Average
Velocity, and Two Way Time Grid, the desired output was the high-resolution seismic interval velocity model from the
FWI modeling. Real-world data were used to train and validate the proposed network architecture, and three
evaluation metrics (percent error, structural similarity index measure, and visual analysis) were adopted as quality
criteria. Based on these metrics, the results evaluated upon the validation set suggest that the cGAN was able to
accurately match real FWI generated outputs, enabling it to extract from input data the main geological structures
and lateral velocity variations. Experimental results indicate that the proposed method, when deployed, has the
potential to increase the speed of geophysical reservoir characterization processes, saving time and computational

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