Title : Real-time monitoring and data driven enterprise prognostics health management framework for oilfield assets
Abstract:
Modern oilfield operations demand maintenance strategies that improve equipment reliability while maximizing asset availability and minimizing operating costs across diverse asset classes. This paper presents an enterprise-scale Prognostics Health Management (PHM) framework that enables the transition from preventive and reactive maintenance to condition-based and predictive maintenance for both surface and downhole oilfield assets. The framework integrates Industrial Internet of Things (IIoT), automated data acquisition, physics-based prognostics, advanced analytics, artificial intelligence (AI), and centralized remote surveillance to establish a scalable digital reliability ecosystem supporting hydraulic fracturing, coiled tubing, and wireline operations.
For stimulation and coiled tubing fleets, an in-house IIoT ecosystem acquires real-time operational data from field equipment into a centralized data platform supporting edge analytics, cloud-hosted PHMs, and live remote surveillance. The framework incorporates OEM-based threshold alarms, multi-parameter physics-based prognostic models, AI-driven oil sampling analysis, fluid-end consumable degradation calculations, and fleet-level pump risk profiling. Real-time equipment health is continuously evaluated using engine, transmission, power-end, fluid-end, and coiled tubing operational parameters to identify degradation trends and hidden failure signatures before conventional alarm thresholds are exceeded. A structured intervention workflow integrates remote surveillance with computerized maintenance management systems (CMMS), enabling timely field interventions and maintenance planning.
For wireline operations, equipment health data are automatically collected from field acquisition systems through dedicated in-house application. Data is centrally processed using physics-based PHMs developed from historical failure datasets. Advanced statistics based diagnostic models continuously assess telemetry cartridges and mechanical sidewall coring tools by monitoring voltage characteristics, telemetry behaviour, pressure relationships, and hydraulic system responses. The models identify degradation mechanisms including electronic component deterioration, solder crack development, transformer failures, fluid bypass, hydraulic leaks, low-pressure conditions, and free-spinning events.
The framework has been deployed across 142 IIoT devices supporting 96 coiled tubing and 46 hydraulic fracturing fleets, and for global wireline operations, enabling continuous monitoring of more than 500 surface equipment units worldwide, while simultaneously providing centralized PHM monitoring for downhole wireline tools. During 2025, remote surveillance generated more than 2,500 CMMS deficiency reports, and PHM-driven insights enabled corrective actions across more than 160 assets before functional failure occurred. Across all applications, the framework improved fault detection, reduced troubleshooting time, optimized maintenance prioritization, and increased asset availability by identifying degradation mechanisms well before functional failures occurred.
This work demonstrates a unified enterprise framework capable of supporting predictive maintenance across multiple oilfield service lines using asset-specific data acquisition architectures within a common reliability strategy. The proposed methodology provides a practical roadmap for implementing scalable digital reliability programs that enhance operational efficiency and asset performance throughout the oilfield lifecycle.
