Nonlinear interpolated Variational Autoencoder for generalized fluid content estimation

Generalizing machine learning models for petroleum applications, especially in scenarios with limited and less varied training data compared to real-world conditions, remains a persistent challenge. This study introduces a novel method combining interpolation mixup with a Variational Autoencoder (VAE) and adaptable interpolation loss for downstream regression tasks. By implementing this approach, we generate high-quality interpolated samples, yielding accurate estimations. Experimental validation on a real-world industrial dataset focused on fluid content measurement demonstrates the superior performance of our method compared to other interpolation and regularization techniques. Our approach achieves over a 15% improvement on generalized out-of-distribution datasets, offering crucial insights for fluid content estimation and practical implications for industrial applications. •DAS data enables accurate identification of fluid content in pipelines.•ML struggles to generalize when test data differs from training data.•Generative modeling aids water breakthrough and ratio estimation in DAS analysis.•Neural networks assess generative models for quality in downstream tasks.