A Machine Learning-Centric Taxonomy and Structured Characterization of Public Datasets for Upstream Oil and Gas

Upstream oil and gas operations generate large volumes of multivariate data from seismic surveys, well logs, production sensor networks, and reservoir simulation models. Advances in machine learning, artificial intelligence, and other Industry 4.0 technologies are increasingly enabling data-driven applications across exploration, reservoir characterization, drilling optimization, and production forecasting. However, publicly available upstream datasets vary substantially in data modality, labeling strategy, machine learning compatibility, and benchmark maturity. To date, no standardized framework or taxonomy exists to guide dataset selection, benchmark design, or cross-study comparison. This study addresses that gap by proposing a structured, machine learning-centric taxonomy that organizes upstream datasets according to properties that are directly relevant to machine learning requirements. The proposed taxonomy provides a shared reference framework to support consistent dataset description, informed selection, and reproducible benchmarking in upstream machine learning research and applications.