A DDC-Based Integrated Electrical and Instrumentation Control System Architecture for Unconventional Oil Production Plants

Abstract

Jin-Hong Jung and Jeong-Hyeon Moon

This study proposes a distributed Direct Digital Control (DDC)-based integrated control architecture for unconventional oil production plants, addressing the limitations of conventional centralized control approaches in modular and distributed industrial environments. Unconventional oil production facilities are characterized by harsh thermodynamic conditions, multiphase flow behavior, and modular plant configurations, which require scalable, interoperable, and resilient control systems beyond traditional Distributed Control Systems (DCS) and Programmable Logic Controller (PLC)-based architectures. To address these challenges, a structured systems engineering methodology is adopted, incorporating requirement derivation, functional decomposition, architecture synthesis, and quantitative validation. The proposed architecture integrates electrical, instrumentation, control, and safety subsystems within a system-of-systems (SoS) framework, enabling distributed field-level autonomy and coordinated system-wide operation. This approach transforms conventional control systems into integrated system architectures capable of supporting modular expansion and subsystem interoperability. The architecture is implemented and validated in a 600 BPD pilot-scale unconventional oil production plant. Quantitative evaluation demonstrates measurable improvements in wiring reduction (from 12,500 m to 7,800 m), expansion effort (from 120 h to 66 h), maintenance downtime (from 85 h to 58 h), and system availability (from 98.2% to 99.4%) compared to a conventional centralized DCS-based configuration. The results indicate that the proposed architecture provides a reproducible and scalable framework for integrated control of complex industrial systems, enabling modular plant evolution without structural redesign. This study contributes a formalized architectural framework and quantitative validation approach for next-generation distributed industrial control systems.

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