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Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A026, June 5–10, 2022

Paper No: OMAE2022-78433

DOI: https://doi.org/10.1115/OMAE2022-78433

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Topics: Algorithms, Machine learning, Ships, Waves

Proceedings Papers

Application of Machine Learning for the Generation of Tailored Wave Sequences

Marco Klein, Merten Stender, Mathies Wedler, Svenja Ehlers, Moritz Hartmann, Nicolas Desmars, Marc-André Pick, Robert Seifried, Norbert Hoffmann

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT12A015, June 5–10, 2022

Paper No: OMAE2022-78601

DOI: https://doi.org/10.1115/OMAE2022-78601

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Topics: Machine learning, Waves, Artificial neural networks, Modeling, Nonlinear waves, Seas, Spectra (Spectroscopy), Time series

Proceedings Papers

Analyzing Flexural Strength Data of Ice: How Useful Is Explainable Machine Learning?

Patrik Buil, Leon Kellner, Sören Ehlers, Franz von Bock und Polach

Proc. ASME. OMAE2022, Volume 6: Polar and Arctic Sciences and Technology, V006T07A012, June 5–10, 2022

Paper No: OMAE2022-87434

DOI: https://doi.org/10.1115/OMAE2022-87434

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Topics: Bending strength, Ice, Machine learning

Proceedings Papers

Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks

Adrian Ambrus, Sergey Alyaev, Nazanin Jahani, Felix James Pacis, Tomasz Wiktorski

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A010, June 5–10, 2022

Paper No: OMAE2022-79046

DOI: https://doi.org/10.1115/OMAE2022-79046

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Topics: Artificial neural networks, Drilling, Machine learning, Modeling, Optimization, Uncertainty, Architecture, Artificial intelligence, Climate, Decision making

Proceedings Papers

Optimizing Models for Predicting Torque on Bit Using Data From the Volve Field in Norway

Patrick Höhn, Ahmed Rahim Kreem Bashara, Carlos Paz, Joachim Oppelt

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A011, June 5–10, 2022

Paper No: OMAE2022-79543

DOI: https://doi.org/10.1115/OMAE2022-79543

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Topics: Drilling, Machine learning, Torque

Proceedings Papers

Well Path Design Using Q-Learning Algorithms and Bezier Curves With Obstacles Avoidance

Jie Cao, Samiul Ehsan Chowdhury, Tomasz Wiktorski, Dan Sui

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A007, June 5–10, 2022

Paper No: OMAE2022-78427

DOI: https://doi.org/10.1115/OMAE2022-78427

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Topics: Algorithms, Design, Drilling, Optimization, Reinforcement learning, Trajectories (Physics), Collisions (Physics), Engineers, Dynamic light scattering, Dynamic programming

Proceedings Papers

Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data

Jie Cao, Hui Ren, Dan Sui

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A013, June 5–10, 2022

Paper No: OMAE2022-79747

DOI: https://doi.org/10.1115/OMAE2022-79747

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Topics: Drilling, Optimization, Underwater drilling, Workflow, Design, Machine learning, Artificial neural networks, Fluid dynamics, Genetic algorithms, String

Proceedings Papers

Evaluation and Interpretation on Data Driven ROP Models From Engineering Perspectives

Miguel Fernández Berrocal, Jie Cao, Dan Sui

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A009, June 5–10, 2022

Paper No: OMAE2022-78752

DOI: https://doi.org/10.1115/OMAE2022-78752

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Topics: Drilling, Dynamics (Mechanics), Errors, Evaluation methods, Machine learning, Model validation, Modeling, Optimization, Petroleum industry, Pressure

Proceedings Papers

Picking the Optimum Directional Drilling Technology (RSS vs PDM): A Machine Learning-Based Model

Muhammad Nour, Mohamed S. Farahat, Omar Mahmoud

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A014, June 5–10, 2022

Paper No: OMAE2022-80569

DOI: https://doi.org/10.1115/OMAE2022-80569

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Topics: Drilling, Machine learning, Machinery, Motors, Pulse width modulation

Proceedings Papers

Development of a Permeability Reduction Model Using Deep Learning for CO 2 Hydrate Storage

Alan Junji Yamaguchi, Toru Sato, Takaomi Tobase, Xinran Wei, Lin Huang, Jia Zhang, Jiang Bian, Tie-Yan Liu

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A001, June 5–10, 2022

Paper No: OMAE2022-78523

DOI: https://doi.org/10.1115/OMAE2022-78523

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Topics: Carbon capture and storage, Carbon dioxide, Machine learning, Permeability, Storage

Proceedings Papers

Unsupervised Machine Learning: A Well Planning Tool for the Future

Peter Batruny, Tim Robinson

Proc. ASME. OMAE2022, Volume 10: Petroleum Technology, V010T11A006, June 5–10, 2022

Paper No: OMAE2022-78423

DOI: https://doi.org/10.1115/OMAE2022-78423

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Topics: Drilling, Machine learning

Proceedings Papers

Machine Learning in Fatigue Analysis for an Unbonded Flexible Riser

Jiabei Yuan, Linfa Zhu, Yucheng Hou, Zhimin Tan

Proc. ASME. OMAE2022, Volume 3: Materials Technology; Pipelines, Risers, and Subsea Systems, V003T04A017, June 5–10, 2022

Paper No: OMAE2022-83730

DOI: https://doi.org/10.1115/OMAE2022-83730

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Topics: Fatigue analysis, Flexible risers, Machine learning

Proceedings Papers

Advanced Data Analytics Based Hybrid Engine-Propeller Combinator Diagram for Green Ship Operations

Lokukaluge P. Perera, Kostas Belibassakis, Evangelos Filippas, Maneesha Premasiri

Proc. ASME. OMAE2022, Volume 5A: Ocean Engineering, V05AT06A030, June 5–10, 2022

Paper No: OMAE2022-79490

DOI: https://doi.org/10.1115/OMAE2022-79490

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Topics: Engines, Machine learning, Propellers, Ships

Proceedings Papers

Machine Learning Based Prediction of Wave-Induced Vessel Response

Ali Cetin, Vegard R. Solum, Cristina M. Evans

Proc. ASME. OMAE2022, Volume 5A: Ocean Engineering, V05AT06A034, June 5–10, 2022

Paper No: OMAE2022-78261

DOI: https://doi.org/10.1115/OMAE2022-78261

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Topics: Machine learning, Vessels, Waves, Ocean engineering, Seas, Spectra (Spectroscopy), Uncertainty

Proceedings Papers

On the Calibration of Metocean Time Series Using Machine Learning

François Enet, Olga Podrazka, Laury Renac

Proc. ASME. OMAE2022, Volume 5A: Ocean Engineering, V05AT06A006, June 5–10, 2022

Paper No: OMAE2022-80274

DOI: https://doi.org/10.1115/OMAE2022-80274

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Topics: Calibration, Machine learning, Time series, Computer simulation, Waves, Wind, Buoys, Engineering design, Reliability, Shorelines

Proceedings Papers

A Journey From Daily Engineering to Machine Learning, Application to Rigid Pipeline Reel-Laying

François-Guillaume Capelle, Jonas Durand-Gasselin, Eric Giry, Amandine Laye

Proc. ASME. OMAE2022, Volume 3: Materials Technology; Pipelines, Risers, and Subsea Systems, V003T04A026, June 5–10, 2022

Paper No: OMAE2022-79870

DOI: https://doi.org/10.1115/OMAE2022-79870

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Topics: Cable reels, Machine learning, Pipelines, Design, Engineers, Finite element analysis, Interpolation, Underwater pipelines

Proceedings Papers

Estimation of Ocean’s Currents Acting on a Turret-Moored FPSO Using Machine Learning

Pedro Felipe Lavra Dias, Eduardo Aoun Tannuri, Anna Helena Reali Costa, Glauco Augusto De Paula Caurin, Gustavo Alencar Bisinotto

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A002, June 5–10, 2022

Paper No: OMAE2022-78607

DOI: https://doi.org/10.1115/OMAE2022-78607

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Topics: Artificial neural networks, Currents, FPSO, Machine learning, Mooring

Proceedings Papers

Comparison of Cutter Suction Dredger Slurry Concentration Soft Measurement Method Based on the Mechanism and Data Dual-Driven Model

Bin Wang, Shidong Fan, Ting Xiong, Hanhua Zhu

Proc. ASME. OMAE2022, Volume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering, V05BT06A024, June 5–10, 2022

Paper No: OMAE2022-80359

DOI: https://doi.org/10.1115/OMAE2022-80359

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Topics: Machine learning, Reliability, Slurries, Suction

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NARROW

Mooring Optimization Using ML Techniques

Prelude FLNG Free Weathervaning Heading Prediction and Uncertainties, Based on Machine Learning Model

Application of Machine Learning Algorithms for Predicting Added Resistance in Arbitrary Wave Headings of a Ship

Application of Machine Learning for the Generation of Tailored Wave Sequences

Analyzing Flexural Strength Data of Ice: How Useful Is Explainable Machine Learning?

Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks

Optimizing Models for Predicting Torque on Bit Using Data From the Volve Field in Norway

Well Path Design Using Q-Learning Algorithms and Bezier Curves With Obstacles Avoidance

Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data

Evaluation and Interpretation on Data Driven ROP Models From Engineering Perspectives

Picking the Optimum Directional Drilling Technology (RSS vs PDM): A Machine Learning-Based Model

Development of a Permeability Reduction Model Using Deep Learning for CO 2 Hydrate Storage

Unsupervised Machine Learning: A Well Planning Tool for the Future

Machine Learning in Fatigue Analysis for an Unbonded Flexible Riser

Advanced Data Analytics Based Hybrid Engine-Propeller Combinator Diagram for Green Ship Operations

Machine Learning Based Prediction of Wave-Induced Vessel Response

On the Calibration of Metocean Time Series Using Machine Learning

A Journey From Daily Engineering to Machine Learning, Application to Rigid Pipeline Reel-Laying

Estimation of Ocean’s Currents Acting on a Turret-Moored FPSO Using Machine Learning

Comparison of Cutter Suction Dredger Slurry Concentration Soft Measurement Method Based on the Mechanism and Data Dual-Driven Model

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