Manuscript Number : GISRRJ225411

A Root Cause Analytics Model for Diagnosing Offshore Process Failures Using Live Operational Data

Authors(4) :-Andrew Tochukwu Ofoedu, Joshua Emeka Ozor, Oludayo Sofoluwe, Dazok Donald Jambol

In offshore oil and gas operations, process failures can lead to significant production losses, environmental risks, and safety hazards. Traditional diagnostic approaches often rely on manual analysis and post-event investigations, which are time-consuming and reactive. This proposes a Root Cause Analytics (RCA) model designed to diagnose offshore process failures in real time using live operational data. The model integrates data streams from distributed sensors, control systems, and equipment logs to identify anomalies and trace fault propagation paths. By leveraging a hybrid approach that combines statistical analysis, rule-based inference, and machine learning algorithms, the RCA model isolates root causes with high accuracy and minimal latency. Key components include anomaly detection using time-series clustering, causal inference to map interdependencies between system variables, and a dynamic decision engine that updates fault hypotheses as new data arrive. A case study involving a floating production storage and offloading (FPSO) unit demonstrates the model’s effectiveness in diagnosing pressure surges and valve malfunctions, reducing mean time to diagnosis (MTTD) by over 40% compared to baseline methods. The model also provides visual fault trees and impact assessments to aid operator decision-making. Live validation on a digital twin platform confirmed its robustness under varying operational scenarios, including equipment degradation and sensor drift. This RCA model represents a shift from reactive to proactive offshore operations by enabling real-time diagnostics, thus improving asset reliability and operational safety. Future enhancements will focus on integrating maintenance history and human-in-the-loop feedback for adaptive learning. Overall, the proposed framework underscores the value of combining live data analytics with intelligent root cause reasoning to support timely, data-driven interventions in complex offshore environments.

Authors and Affiliations

Andrew Tochukwu Ofoedu
Shell Nigeria Exploration and Production Company, Nigeria
Joshua Emeka Ozor
First Hydrocarbon, Nigeria
Oludayo Sofoluwe
TotalEnergies Nigeria
Dazok Donald Jambol
Shell Petroleum Development Company of Nigeria Ltd

Root Cause, Analytics Model, Diagnosing Offshore, Process Failures, Live Operational Data

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Publication Details

Published in : Volume 5 | Issue 4 | July-August 2022
Date of Publication : 2022-07-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 99-117
Manuscript Number : GISRRJ225411
Publisher : Technoscience Academy

ISSN : 2582-0095

Cite This Article :

Andrew Tochukwu Ofoedu, Joshua Emeka Ozor, Oludayo Sofoluwe, Dazok Donald Jambol, "A Root Cause Analytics Model for Diagnosing Offshore Process Failures Using Live Operational Data", Gyanshauryam, International Scientific Refereed Research Journal (GISRRJ), ISSN : 2582-0095, Volume 5, Issue 4, pp.99-117, July-August.2022
URL : https://gisrrj.com/GISRRJ225411

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