Elsevier

Marine Structures

Volume 83, May 2022, 103152
Marine Structures

Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks

https://doi.org/10.1016/j.marstruc.2021.103152Get rights and content
Under a Creative Commons license
open access

Highlights

  • Multiple machine learning models validated to forecast offshore platform lifespans.

  • Offshore platform features assessed to determine importance in modeling lifespan.

  • Models integrate structure, incident, metocean, production, and geohazard data.

  • Gradient boosting and neural network machine learning model results compared.

  • Multiple model approach captures variations in lifespan predictions and predictors.

Abstract

Offshore oil and gas platforms operating past their design life can pose significant risk to operators and the surrounding environment, as the integrity of these structures decreases over time due to a variety of stressors. This has important implications for industry and government, which are seeking to safely extend the life of platforms for continued use or reuse for alternative offshore energy applications. As a result, there is a need to quantify the remaining useful life (RUL) of operating platforms by analyzing the effects that stressors may have on structural integrity. This study provides a platform risk assessment by employing two machine learning models to forecast the removal age of existing platforms in the U.S. federal waters of the Gulf of Mexico (GoM): a gradient boosted regression tree (GBRT) and an artificial neural network (ANN). These data-driven models were applied to a large, extensive dataset representing the natural and engineered offshore system. Both models were found to provide promising predictions, with 95–97% accuracy and predictions within 1.42–2.04 years on average of the observed removal age during validation. These results can be applied to inform life extension opportunities for fixed and mobile offshore platforms, as well as localized maintenance strategies aiming to prevent operational and environmental risk while maintaining energy production.

Keywords

Offshore platform
Remaining useful life prediction
Risk assessment
Machine learning
Gradient boosting
Artificial neural network
ANN
Artificial Neural Network
CV
Cross-validation
GBRT
Gradient Boosted Regression Tree
GoM
Gulf of Mexico
KNN
K-nearest neighbor
MAE
Mean absolute error
Metocean
Meteorological and oceanographic
ML
Machine learning
MSE
Mean squared error
PFI
Permutation Feature Importance
RFE
Recursive Feature Elimination
RMSE
Root mean squared error
RUL
Remaining useful life
SIM
Structural Integrity Management

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