Smart Oil Field Automation for Pumps, Compressors, Pipelines, and Process Facilities
TL;DR
This paper compares equipment-specific sensing and failure signatures with the shared data, governance, and decision layers necessary for facility-wide automation and proposes a closed-loop architecture and maturity roadmap linking sensing, validation, diagnosis, decision, action, and learning.
Abstract
Smart oil-field automation is advancing from isolated supervisory control to integrated, asset-aware systems that coordinate pumps, compressors, pipelines, and process facilities. This review integrates research published between 2020 and 2025 on industrial Internet of Things architectures, condition monitoring, machine learning, digital twins, leak detection, advanced process control, cybersecurity, and human-centered operations. The objective is to develop an integrated review framework that clarifies how automation is able to enhance reliability, energy performance, production continuity, and process safety spanning interdependent oil-field assets. In contrast to reviews focused on a single machine or analytics method, this paper compares equipment-specific sensing and failure signatures with the shared data, governance, and decision layers necessary for facility-wide automation. A structured review methodology was applied, combining database-oriented search logic, relevance screening, thematic coding, and cross-asset synthesis. The results show that successful automation depends less on deploying a single algorithm and more on maintaining trustworthy instrumentation, contextualized time-series data, edge-cloud coordination, interpretable diagnostics, and bounded control authority. Pumps benefit from vibration, pressure, current, and hydraulic-efficiency analytics; compressors require thermodynamic, vibration, surge, and seal monitoring; pipelines require multi-modal leak and integrity surveillance; and process facilities require model-predictive coordination across interacting units. The review proposes a closed-loop architecture and maturity roadmap linking sensing, validation, diagnosis, decision, action, and learning. Key gaps include transferable models, rare-fault validation, cyber-physical assurance, lifecycle model maintenance, and evidence of long-term economic value. The paper concludes that smart automation should be implemented as a governed socio-technical capability rather than as a collection of disconnected digital projects.