Enhancing Geoscience Analysis: AI-Driven Imputation of Missing Data in Well Logging Using Generative Models

The integrity of well logging data is paramount in geophysical explorations for accurate subsurface analysis, notably in the North Sea Dutch region known for its extensive hydrocarbon exploration. Addressing the common challenge of missing data in well logs, our study introduces an AI-driven methodology employing generative models. These models utilize machine learning to analyze existing data patterns and generate realistic imputations for missing values. The approach has shown to not only enhance the quality of geological interpretations but also to streamline the workflow in hydrocarbon exploration. This integration of AI signifies a substantial move towards more precise and efficient geoscience data analysis. A qualitative comparison using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) was conducted to evaluate the results. The PCA comparison demonstrates the synthetic data’s alignment with real data in principal component space, effectively capturing the variance. The t-SNE analysis further validates the model's fidelity, with the synthetic data exhibiting clustering behaviors analogous to real data. Together, these results showcase the transformative potential of machine learning in geosciences, providing a robust framework for enhancing data reliability in geophysical studies.