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Proceedings Papers

Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning

Yuecheng Cai, Jasmin Jelovica

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A037, June 21–30, 2021

Paper No: OMAE2021-62304

DOI: https://doi.org/10.1115/OMAE2021-62304

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Topics: Artificial neural networks, Machine learning, Optimization, Structural optimization

Proceedings Papers

Machine Learning Applications in Drilling Fluid Engineering: a Review

Sercan Gul

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A007, June 21–30, 2021

Paper No: OMAE2021-63094

DOI: https://doi.org/10.1115/OMAE2021-63094

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Topics: Drilling, Fluids, Fluids engineering, Machine learning

Proceedings Papers

Research on Kick Detection Model Based on Machine Learning

Xianzhi Song, Shiming Duan, Zhijun Pei, Zhaopeng Zhu

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A005, June 21–30, 2021

Paper No: OMAE2021-62785

DOI: https://doi.org/10.1115/OMAE2021-62785

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Topics: Artificial neural networks, Drilling, Fuzzy neural nets, Machine learning, Probability, Risk, Support vector machines

Proceedings Papers

Attempt of Early Stuck Detection Using Unsupervised Deep Learning With Probability Mixture Model

Tomoya Inoue, Yujin Nakagawa, Ryota Wada, Keisuke Miyoshi, Shungo Abe, Kouhei Kuroda, Hitoshi Tamamura, Hakan Bilen

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A004, June 21–30, 2021

Paper No: OMAE2021-62739

DOI: https://doi.org/10.1115/OMAE2021-62739

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Topics: Machine learning, Pipes, Probability, Drilling, Artificial neural networks, Countermeasures, Support vector machines, Wells

Proceedings Papers

Estimation of Cuttings Concentration and Frictional Pressure Losses During Drilling Using Data-Driven Models

Murat Ozbayoglu, Evren Ozbayoglu, Baris Guney Ozdilli, Oney Erge

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A008, June 21–30, 2021

Paper No: OMAE2021-63653

DOI: https://doi.org/10.1115/OMAE2021-63653

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Topics: Artificial neural networks, Drilling, Pressure, Pipes, Fluids, Modeling, Rotation, Wells, Drill strings, Errors

Proceedings Papers

A Well-Based Field Development Redefinition of Unconventional Reservoirs Through Unconventionality Index: Utilizing Machine Learning

Mohammed Aldhuhoori, Hadi Belhaj, Bisweswar Ghosh, Hamda Alkuwaiti, Rabab Qaddoura

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A017, June 21–30, 2021

Paper No: OMAE2021-62964

DOI: https://doi.org/10.1115/OMAE2021-62964

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Topics: Machine learning, Reservoirs

Proceedings Papers

Machine Learning Approach to Estimate the Diffusion Coefficient of CO 2 in Hydrocarbons

Nikhil Bagalkot, Arvind Keprate

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A036, June 21–30, 2021

Paper No: OMAE2021-62407

DOI: https://doi.org/10.1115/OMAE2021-62407

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Topics: Carbon dioxide, Diffusion (Physics), Machine learning

Proceedings Papers

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

Mehrdad Gharib Shirangi, Roger Aragall, Reza Ettehadi, Roland May, Edward Furlong, Charles A. Thompson, Jr, Thomas G. Dahl

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A006, June 21–30, 2021

Paper No: OMAE2021-62987

DOI: https://doi.org/10.1115/OMAE2021-62987

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Topics: Drilling, Fluids, Machine learning, Rheology

Proceedings Papers

Software-in-the-Loop Combined Machine Learning for Dynamic Responses Analysis of Floating Offshore Wind Turbines

Peng Chen, Changhong Hu, Zhiqiang Hu

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A024, June 21–30, 2021

Paper No: OMAE2021-65524

DOI: https://doi.org/10.1115/OMAE2021-65524

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Topics: Computer software, Dynamic response, Floating wind turbines, Machine learning, Reinforcement learning

Proceedings Papers

The Fundamental Research on AI Prediction and Determination of Control Force for Maximizing the Power Generation of PA-WEC in Irregular Waves

Motohiko Murai, Sho Sakamoto

Proc. ASME. OMAE2021, Volume 5: Ocean Space Utilization, V005T05A010, June 21–30, 2021

Paper No: OMAE2021-61493

DOI: https://doi.org/10.1115/OMAE2021-61493

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Topics: Energy generation, Waves, Renewable energy, Machine learning, Artificial intelligence, Certainty, Fossil fuels, Oceans, Wave energy, Wave energy converters

Proceedings Papers

Machine Learning Prediction and Reliability Analysis Applied to Subsea Spool and Jumper Design

Mengdi Song, Massyl Gheroufella, Paul Chartier

Proc. ASME. OMAE2021, Volume 4: Pipelines, Risers, and Subsea Systems, V004T04A019, June 21–30, 2021

Paper No: OMAE2021-62033

DOI: https://doi.org/10.1115/OMAE2021-62033

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Topics: Design, Event history analysis, Machine learning, Ocean engineering, Underwater pipelines

Proceedings Papers

Benchmark Study of Supervised Machine Learning Methods for a Ship Speed-Power Prediction at Sea

Xiao Lang, Da Wu, Wengang Mao

Proc. ASME. OMAE2021, Volume 6: Ocean Engineering, V006T06A018, June 21–30, 2021

Paper No: OMAE2021-62395

DOI: https://doi.org/10.1115/OMAE2021-62395

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Topics: Machine learning, Seas, Ships

Proceedings Papers

Enhancing Machine Learning Models With Prior Physical Knowledge to Aid in VIV Response Prediction

Leixin Ma, Themistocles L. Resvanis, J. Kim Vandiver

Proc. ASME. OMAE2021, Volume 8: CFD and FSI, V008T08A021, June 21–30, 2021

Paper No: OMAE2021-62145

DOI: https://doi.org/10.1115/OMAE2021-62145

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Topics: Cylinders, Machine learning, Vortex-induced vibration

Proceedings Papers

Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System

Zhi Yung Tay, Januwar Hadi, Dimitrios Konovessis, De Jin Loh, David Kong Hong Tan, Xiaobo Chen

Proc. ASME. OMAE2021, Volume 6: Ocean Engineering, V006T06A019, June 21–30, 2021

Paper No: OMAE2021-62658

DOI: https://doi.org/10.1115/OMAE2021-62658

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Topics: Fuel efficiency, Machine learning, Monitoring systems, Time series

Proceedings Papers

Application of Machine Learning Algorithm in Optimization of PSV for 110000Dwt Oil Tanker

Liu Xiyang, Chen Jingpu, Sun Wenyu, Xu Wei

Proc. ASME. OMAE2020, Volume 1: Offshore Technology, V001T01A003, August 3–7, 2020

Paper No: OMAE2020-18354

DOI: https://doi.org/10.1115/OMAE2020-18354

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Topics: Algorithms, Machine learning, Optimization, Platform supply vessels, Tankers

Proceedings Papers

Offshore Topside Weight Prediction Using Data Science

Reza Ahmadian, Nicolas Tcherniguin, Jean-François Tiberti

Proc. ASME. OMAE2020, Volume 1: Offshore Technology, V001T01A010, August 3–7, 2020

Paper No: OMAE2020-19210

DOI: https://doi.org/10.1115/OMAE2020-19210

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Topics: Ocean engineering, Weight (Mass), Design, Algorithms, Data mining, Databases, Economics , Engineering systems and industry applications, Machine learning, Pressure

Proceedings Papers

Algorithms for Supervised Machine Learning-Based Structural Performance Evaluation Framework

Xiaowei Wang, YeongAe Heo

Proc. ASME. OMAE2020, Volume 2A: Structures, Safety, and Reliability, V02AT02A018, August 3–7, 2020

Paper No: OMAE2020-18947

DOI: https://doi.org/10.1115/OMAE2020-18947

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Topics: Algorithms, Machine learning, Machinery, Model validation, Performance evaluation

Proceedings Papers

Offshore Drilling: Extending the Weather Window for Operations by Optimal Use of Simulations and Probabilistic Machine Learning

Simen Eldevik, Stian Sætre, Erling Katla, Andreas B. Aardal

Proc. ASME. OMAE2020, Volume 2B: Structures, Safety, and Reliability, V02BT02A002, August 3–7, 2020

Paper No: OMAE2020-19119

DOI: https://doi.org/10.1115/OMAE2020-19119

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Topics: Machine learning, Simulation, Uncertainty, Underwater drilling

Proceedings Papers

A Decision Support Framework for Cost-Effective and Energy-Efficient Shipping

Khanh Q. Bui, Lokukaluge P. Perera

Proc. ASME. OMAE2020, Volume 6A: Ocean Engineering, V06AT06A026, August 3–7, 2020

Paper No: OMAE2020-18368

DOI: https://doi.org/10.1115/OMAE2020-18368

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Topics: Artificial intelligence, Cycles, Data acquisition systems, Decision making, Emissions, Energy efficiency, Flow (Dynamics), Greenhouse gases, Innovation, Internet of things

Proceedings Papers

Nonlinear and Machine-Learning-Based Station-Keeping Control of an Unmanned Surface Vehicle

Armando J. Sinisterra, Alexandrea Barker, Siddhartha Verma, Manhar R. Dhanak

Proc. ASME. OMAE2020, Volume 6B: Ocean Engineering, V06BT06A027, August 3–7, 2020

Paper No: OMAE2020-19276

DOI: https://doi.org/10.1115/OMAE2020-19276

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Topics: Machine learning, Machinery, Vehicles, Artificial intelligence, Control equipment, Energy consumption, Motion control, Robotics, Robustness, Trajectories (Physics)

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NARROW

Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning

Machine Learning Applications in Drilling Fluid Engineering: a Review

Research on Kick Detection Model Based on Machine Learning

Attempt of Early Stuck Detection Using Unsupervised Deep Learning With Probability Mixture Model

Estimation of Cuttings Concentration and Frictional Pressure Losses During Drilling Using Data-Driven Models

A Well-Based Field Development Redefinition of Unconventional Reservoirs Through Unconventionality Index: Utilizing Machine Learning

Machine Learning Approach to Estimate the Diffusion Coefficient of CO 2 in Hydrocarbons

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

Software-in-the-Loop Combined Machine Learning for Dynamic Responses Analysis of Floating Offshore Wind Turbines

The Fundamental Research on AI Prediction and Determination of Control Force for Maximizing the Power Generation of PA-WEC in Irregular Waves

Machine Learning Prediction and Reliability Analysis Applied to Subsea Spool and Jumper Design

Benchmark Study of Supervised Machine Learning Methods for a Ship Speed-Power Prediction at Sea

Enhancing Machine Learning Models With Prior Physical Knowledge to Aid in VIV Response Prediction

Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System

Application of Machine Learning Algorithm in Optimization of PSV for 110000Dwt Oil Tanker

Offshore Topside Weight Prediction Using Data Science

Algorithms for Supervised Machine Learning-Based Structural Performance Evaluation Framework

Offshore Drilling: Extending the Weather Window for Operations by Optimal Use of Simulations and Probabilistic Machine Learning

A Decision Support Framework for Cost-Effective and Energy-Efficient Shipping

Nonlinear and Machine-Learning-Based Station-Keeping Control of an Unmanned Surface Vehicle

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