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Forecasting Offshore Platform Integrity: Applying Machine Learning Algorithms to Quantify Lifespan and Mitigate Risk

2021-12-28T18:18:47+00:00Categories: 2021 Presentations, Assessing Current and Future Infrastructure Hazards, Presentations|Tags: , , |

Romeo, L., Dyer, A., Bauer, J., Barkhurst, A., Duran, R., Nelson, J., Sabbatino, M., Wenzlick, M., Wingo, P., Zaengle, D. and Rose, K. 2021. Forecasting Offshore Platform Integrity: Applying Machine Learning Algorithms to Quantify Lifespan and Mitigate Risk. Machine Learning in Oil & Gas. April 15, 2021. Virtual.

Gulf of Mexico Geology and Petroleum System: Overview and Literature Review in Support of Risk and Resource Assessments

2019-12-06T18:10:07+00:00Categories: 2015 Publications, ORM (Offshore Risk Modeling)|Tags: , , , , , |

NETL-TRS-4-2015; EPAct Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2015; p 28.
Mark-Moser, M.; Disenhof, C.; Rose, K.
July 2015
https://edx.netl.doe.gov/dataset/gulf-of-mexico-geology-and-petroleum-system-overview-and-literature-review/resource_download/cb0d5e74-22ea-4b0e-98c3-eaa613a8e7a3

Annual Report:  Deepwater and Ultra-Deepwater

2019-12-06T18:05:51+00:00Categories: 2013 Publications, ORM (Offshore Risk Modeling)|Tags: , , , , , , , |

NETL-TRS-UDW-2013; NETL Technical Report Series, U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR, 2013; p 37.
Rose, K.; Guthrie, G.
2013
https://edx.netl.doe.gov/dataset/annual-report-deepwater-and-ultra-deepwater-research/resource_download/9215def4-2733-47ad-bdf1-e790bf7a676f

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