A Five-Level Operational Maturity Framework for Machine Learning in Midstream Oil and Gas

Machine learning is increasingly applied to midstream oil and gas pipelines, yet reported performance rarely establishes readiness for field use. This narrative review organizes peer-reviewed and industry sources from 2015–2025 using a five-level operational maturity framework (sensing and detection, diagnosis, prediction, decision and optimization, and human-supervised closed-loop automation), defined by a rubric of decision authority, operator role, and the validation evidence each level requires. A second axis records where each study’s performance was demonstrated, from simulation to closed-loop use. Applications include leak and anomaly detection, defect and corrosion assessment, hard spots, pump cavitation, predictive maintenance, failure-risk modeling, scheduling, and digital twins. Cross-classifying the coded sources on both axes shows that validation realism does not keep pace with claimed operational responsibility. Peer-reviewed evaluations are concentrated at Levels 1–3 and rely largely on laboratory, simulated, or retrospective data; the only operational Level 4 example is a practitioner case, and no coded source demonstrates Level 5 closed-loop control. The corpus thus shows a validation gap at the lower levels and an evidence-coverage gap at the upper ones; beyond the performance shown in controlled evaluations, validation, transferability, and governance remain major unresolved barriers to deployment. A ranked agenda led by graduated field validation follows.