Transformer vs Classical Machine Learning: Harnessing Normalization for Enhanced Asset Life Prediction

Abstract The advent of artificial intelligence (AI) and machine learning (ML) solutions enable detailed time-series data analysis previously thought impractical to impossible. This paper highlights the development and deployment of a Transformer-based Asset Life Model (ALM) which outperforms previously deployed classical machine-learning approaches for predicting drilling equipment component lifetimes. The use of advanced normalization methods enables consistent performance comparisons across a wide array of assets which may be deployed on different rig types and configurations, geologic basins, and varying operational conditions while offering a flexible framework to accommodate future expansion in data volume and diversity. Furthermore, factors such as manufacturer attributes, like make and model, can be efficiently incorporated and compared. Said features form the cornerstone of insights which influence supply chain and operations optimization efforts which resulted in a 40% reduction in operating costs. This paper presents a novel Context-Conditioned Normalization (CCN) layer, the supporting technical stack - from data governance through hyper-parameter optimization, ML Operations (MLOps), and field rollout - offering a blueprint for industrial-scale deep learning.