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Digital Twin Technology for Enhancing GOSP Performance and Oil Production Efficiency
Subject area: Science,Engineering and Technology · Area of research: GOSP Performance and Oil Production
Abstract
Gas-oil separation plants (GOSPs) are tightly coupled production systems in which well behaviour, multiphase flow, separator performance, rotating equipment, instrumentation, pipelines, utilities and environmental constraints interact continuously. A digital twin can create operational value only when it connects these physical dependencies to traceable measurements, validated engineering models and accountable field decisions. This paper presents a practice-informed technical review and an implementation framework for applying digital-twin technology to GOSP performance and oil production efficiency. The work is informed by the author's current involvement in a digital-twin initiative and by AVEVA-oriented digital training, while deliberately excluding confidential project information. The proposed reference architecture integrates field and control-system data, AVEVA PI System for operational data management and contextualisation, AVEVA Process Simulation for online first-principles modelling, equipment-performance analytics, engineering information, and controlled operator or maintenance workflows. Technical depth is developed around three high-value use cases: three-phase separator stability, pump and compressor reliability, and production-deferment diagnosis. The paper defines the minimum model logic required for these applications, including phase mass balances, residence time, equipment-curve residuals, uncertainty-weighted data reconciliation, anomaly thresholds and constrained production optimisation. It also proposes a validation workflow based on historical events, shadow-mode operation, prediction error, false-alarm rate, missed-event rate and engineering acceptance. The review uses only sources that were traceable through a working DOI or an official institutional or vendor page at the time of preparation. The resulting framework positions the digital twin as a governed engineering decision system rather than a visual dashboard, and provides a technically defensible route for staged deployment in Saudi smart oil-field projects without overstating unverified benefits.
Keywords
digital twin; GOSP; AVEVA PI System; process simulation; data reconciliation; separator performance; predictive maintenance; production optimisation; Saudi oil fields.
How to cite this paper
@article{1722537,
author = {Muhammed Asharaf T V},
title = {Digital Twin Technology for Enhancing GOSP Performance and Oil Production Efficiency},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {2668-2681},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722537.pdf},
abstract = {Gas-oil separation plants (GOSPs) are tightly coupled production systems in which well behaviour, multiphase flow, separator performance, rotating equipment, instrumentation, pipelines, utilities and environmental constraints interact continuously. A digital twin can create operational value only when it connects these physical dependencies to traceable measurements, validated engineering models and accountable field decisions. This paper presents a practice-informed technical review and an implementation framework for applying digital-twin technology to GOSP performance and oil production efficiency. The work is informed by the author's current involvement in a digital-twin initiative and by AVEVA-oriented digital training, while deliberately excluding confidential project information. The proposed reference architecture integrates field and control-system data, AVEVA PI System for operational data management and contextualisation, AVEVA Process Simulation for online first-principles modelling, equipment-performance analytics, engineering information, and controlled operator or maintenance workflows. Technical depth is developed around three high-value use cases: three-phase separator stability, pump and compressor reliability, and production-deferment diagnosis. The paper defines the minimum model logic required for these applications, including phase mass balances, residence time, equipment-curve residuals, uncertainty-weighted data reconciliation, anomaly thresholds and constrained production optimisation. It also proposes a validation workflow based on historical events, shadow-mode operation, prediction error, false-alarm rate, missed-event rate and engineering acceptance. The review uses only sources that were traceable through a working DOI or an official institutional or vendor page at the time of preparation. The resulting framework positions the digital twin as a governed engineering decision system rather than a visual dashboard, and provides a technically defensible route for staged deployment in Saudi smart oil-field projects without overstating unverified benefits.},
keywords = {digital twin; GOSP; AVEVA PI System; process simulation; data reconciliation; separator performance; predictive maintenance; production optimisation; Saudi oil fields.},
month = {August},
}