Impact of Generative AI and Statistical Data Augmentation in Synthetic Well-Log Generation for Seismic Porosity Inversion: A Comparative Study

The characterization of reservoir properties from seismic data is often hindered by the scarcity and spatial bias of well-log data. To overcome these limitations, data augmentation (DA) has become essential for training robust Deep Learning models. This study presents a comparative analysis of three distinct DA approaches: Statistical Methods, Variational Autoencoders (VAE), and Generative Adversarial Networks (GAN. We synthesize well-log suites for training a UNet architecture dedicated to seismic-to-porosity prediction. Our workflow begins with real well logs, expanding the dataset through stochastic perturbations (Statistical), latent manifold sampling (VAE), and adversarial learning (GAN). To bridge the gap between 1-D well data and seismic volumes, we perform forward modeling on the augmented suites, generating synthetic seismograms via convolution with representative wavelets. A UNet-based convolutional neural network is then trained on these synthetic pairs to perform the non-linear mapping from seismic amplitudes to porosity. The performance of each method is evaluated through the geological plausibility of the generated logs and the inversion accuracy on a blind-test well. Preliminary results indicate that while statistical methods improve robustness against noise, generative models, particularly GANs, excel in capturing the multi-scale heterogeneity required for high-resolution reservoir characterization. This research demonstrates that the choice of DA is a critical geophysical decision; by integrating generative AI into the inversion workflow, we provide a scalable framework to improve porosity estimation in data-poor environments, ensuring that synthetic extensions remain grounded in petrophysical reality and stratigraphic consistency.