Beyond Raw Data: Implementing An Automated Multiphase Flow Meter (MPFM) Validation and Diagnostic Workflow for Enhanced Measurement Integrity

Abstract

Abstract In modern oil and gas operations, Multiphase Flow Meters (MPFMs) have become indispensable for applications ranging from single-well production metering to fiscal allocation. Yet the reliability of the high-frequency data they produce remains a persistent operational vulnerability. Engineers frequently receive well-test results without visibility into whether the meter operated inside its calibrated envelope, whether the test environment was truly stable, or whether the reported rates are physically consistent with the rest of the production system. This opacity erodes confidence in MPFM technology and introduces risk into reservoir management and production-optimization decisions. This paper presents an automated MPFM validation and diagnostic workflow developed as a foundational component of ADNOC's Artificial Intelligence Production System Optimization (AiPSO) initiative. Within the broader AiPSO engineering-driven feedback-loop architecture, the workflow continuously monitors meter health and operating conditions, identifies statistically stable test periods, and normalizes in-situ rates to a common Path-to-Surface reference. Only those tests that satisfy rigorous integrity criteria are released downstream, feeding automated well-test validation, model calibration, production allocation, and optimization workflows. By converting raw multiphase sensor streams into auditable, exception-based insights, the approach enables true "management by exception": engineers engage only with high-quality, validated well-test data. Field experience demonstrates faster detection of underperforming wells, earlier identification of meter or PVT-model drift, and a measurable reduction in the engineering effort required to qualify well-test data, establishing a practical foundation for higher-confidence digital-twin workflows across diverse asset environments.