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The dataset contains integrated data from an array of disparate data sources, all spatially and temporally linked to platforms in the federal waters of the Gulf of Mexico (platform data from BSEE, 2020). Integrated data includes past reported incidents dating back to 1956 (BSEE, BOEM, MMS), metocean data (see Nelson et al. in review for source information), and geohazard data (see Nelson et al. in review for source information). Proprietary well production information was redacted from this dataset, but was used in resulting analytics.

Poster presented at AGU 2021 Fall Meeting, Virtual, December 13-17, 2021.

2021 Carbon Management and Oil and Gas Research Project Review Meeting, Virtual, August 23-27, 2021

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 structural lifespan of operating platforms by analyzing the effects that stressors may have on offshore infrastructure integrity. This study employed two machine learning models to forecastmore » the remaining lifespan 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-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, as well as localized maintenance strategies aiming to prevent operational and environmental risk while maintaining energy production.« less