Manuscript Number : GISRRJ225410

A Machine Learning-Based Fault Forecasting Model for Subsea Process Equipment in Harsh Production Environments

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

Subsea process equipment operates in some of the most challenging environments in the oil and gas industry, characterized by high pressure, low temperatures, corrosive fluids, and limited accessibility. These harsh conditions significantly increase the likelihood of mechanical and operational failures, often resulting in costly unplanned downtime and safety hazards. Traditional fault detection techniques, such as threshold-based alarms or model-driven diagnostics, are limited in their ability to anticipate failures proactively, especially when data is noisy or sparse. This proposes a machine learning-based fault forecasting model tailored specifically for subsea process equipment deployed in extreme offshore environments. The proposed model utilizes historical sensor data, operational logs, and maintenance records to learn complex patterns associated with impending equipment faults. Key steps include robust data preprocessing, feature engineering sensitive to subsea dynamics, and the application of temporal models such as Long Short-Term Memory (LSTM) networks for time-series prediction. To enhance performance under data scarcity and imbalance, synthetic data augmentation and ensemble learning methods are employed. Extensive testing on both simulated datasets and real-world offshore operational data demonstrates the model’s ability to forecast failures with high precision and lead time, enabling proactive maintenance scheduling. Compared to traditional diagnostic systems, the machine learning model shows superior accuracy, recall, and robustness against environmental noise. Additionally, the system provides probabilistic forecasts that support risk-based decision-making. This work highlights the potential of AI-driven solutions to revolutionize asset integrity management in offshore energy production. By forecasting faults before they manifest, operators can reduce downtime, lower maintenance costs, and improve safety outcomes. Future research will focus on integrating digital twin technologies and transfer learning techniques to further generalize the model across various subsea platforms and equipment types. This represents a significant step toward intelligent, autonomous monitoring systems in subsea production 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

Machine learning-based, Fault, Forecasting model, Subsea process, Equipment, Harsh production, Environments

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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) : 82-98
Manuscript Number : GISRRJ225410
Publisher : Technoscience Academy

ISSN : 2582-0095

Cite This Article :

Andrew Tochukwu Ofoedu, Joshua Emeka Ozor, Oludayo Sofoluwe, Dazok Donald Jambol, "A Machine Learning-Based Fault Forecasting Model for Subsea Process Equipment in Harsh Production Environments", Gyanshauryam, International Scientific Refereed Research Journal (GISRRJ), ISSN : 2582-0095, Volume 5, Issue 4, pp.82-98, July-August.2022
URL : https://gisrrj.com/GISRRJ225410

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