Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection

The rise in energy demand highlights the importance of suitable subsurface storage, requiring detailed and accurate subsurface characterization often reliant on high-quality borehole well log data. However, obtaining complete well-log data is costly and time-consuming, with missing data being common due to borehole conditions or tool errors. While machine learning and deep learning algorithms have been implemented to address these issues, they often fail to capture the intricate, nonlinear relationships and long-term dependencies in complex well log sequences. Additionally, prior AI-driven models typically require retraining when introduced to new datasets and are constrained to deployment in the same basin. In this study, we explored and evaluated the potential of a time-series foundation model leveraging transformer architecture and a generative pre-trained approach for predicting and detecting anomalies in borehole well log data. Specifically, we fine-tuned and adopted the TimeGPT architecture to forecast key log responses and detect anomalies with high accuracy. Our proposed model demonstrated excellent performance, achieving R2 of up to 87% and a mean absolute percentage error (MAPE) as low as 1.95%. Additionally, the model's zero-shot capability successfully identified subtle yet critical anomalies, such as drilling hazards or unexpected geological formations, with an overall accuracy of 93%. The model represents a significant advancement in predictive accuracy and computational efficiency, enabling zero-shot inference through fine-tuning. Its application in well-log prediction enhances operational decision-making while reducing risks associated with subsurface exploration. These findings demonstrate the model's potential to transform well-log data analysis, particularly in complex geological settings.

ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis

Micropaleontology in geosciences focuses on studying the evolution of microfossils (e.g., foraminifera) through geological records to reconstruct past environmental and climatic conditions. This field heavily relies on visual recognition of microfossil features, making it suitable for computer vision technology, specifically deep convolutional neural networks (CNNs), to automate and optimize microfossil identification and classification. However, the application of deep learning in micropaleontology is hindered by limited availability of high-quality, high-resolution labeled fossil images and the significant manual labeling effort required by experts. To address these challenges, we propose a novel deep learning workflow combining hierarchical vision transformers with style-based generative adversarial network algorithms to efficiently acquire and synthetically generate realistic high-resolution labeled datasets of micropaleontology in large volumes. Our study shows that this workflow can generate high-resolution images with a high signal-to-noise ratio (39.1 dB) and realistic synthetic images with a Frechet inception distance similarity score of 14.88. Additionally, our workflow provides a large volume of self-labeled datasets for model benchmarking and various downstream visual tasks, including fossil classification and segmentation. For the first time, we performed few-shot semantic segmentation of different foraminifera chambers on both generated and synthetic images with high accuracy. This novel meta-learning approach is only possible with the availability of high-resolution, high-volume labeled datasets. Our deep learning-based workflow shows promise in advancing and optimizing micropaleontological research and other visual-dependent geological analyses.