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Drilling Systems  \nEditors:  \nThiago G. Ritto, Universidade Federal do Rio de Janeiro (Brazil)  \nDaniel A. Castello, Universidade Federal do Rio de Janeiro (Brazil)  \nBalakumar Balachandran, University of Maryland, College Park, MD (USA) Emmanuel Detournay, University of Minnesota, Minneapolis, MN (USA)  \nNathan van de Wouw, Eindhoven University of Technology, Eindhoven (Netherlands)  \nUniversidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil)  \nJuly 1-3, 2024  \nii  \nGROUP PICTURE  \nEvent webpage: [https://eventos.abcm.org.br/dcds2024/](https://eventos.abcm.org.br/dcds2024/)  \nContents  \nContents iv  \nForeword v  \nColloquium Program vii  \nInsights into Mechanics of Drilling  \nEmmanuel Detournay 1  \nRecent advances in Drilling Optimization using Model-Based design and Rock Formation Logs: Brazilian Pre-Salt Case Studies Romulo R. Aguiar 9  \nA PDE-ODE Model For The Regenerative Effect In Lateral Cut  \nting Processes: From Milling To Bit Whirl  \nKaidong Chen, Emmanuel Detournay, Nathan van de Wouw 19  \nInnovative Downhole Anchoring and Drive System Reduces Drilling  \nDysfunction and Enables Autonomous Reeled Drilling  \nLiam Lines, John Wisinger, William Murray, Matus Gajdos, Mark Webb, Graham Watson 27  \nOptimizing Drilling Strategies for Deep Wells in Complex In  \nterbedded Formations: Addressing Drill String Stress, BHA  \nTool Failure, and Performance Enhancement  \nAdriano Passos, Flank Lima, Guilherme Britto, Jos´e R. B. Moura Filho, M´arcio Francisco, Reinaldo Tomita 35  \nUncertain propagation in the thermo-mechanical coupled dynamic  \nof a drill string model  \nYuri B. S. Moralles, Daniel A. Castello, Thiago G. Ritto 45  \nNonlinear dynamics of smart drilling systems  \nMarcelo A. Savi 53  \niv CONTENTS  \nClosing Pandora’s Box: Characterizing and Mitigating High-Fre  \nquency Torsional Oscillation  \nAshley Johnson, Dave Scott 59  \nDynamic data-driven application systems and drilling  \nB. Shayak, S. Chanana, B. Balachandran 67  \nA Machine Learning approach to classify drilling operations data  \nas a support tool for detection of dynamic drilling dysfunctions  \nJo˜ao V. B. dos Santos, Leone B. Florindo, Let´ıcia T. Vechi, Gregory S. Payette 71  \nComparing Open-Source Drill String Models  \nDongyoung Yoon, Elmir Hamidov, Lance Endres, Eduardo Gildin, Paul Pastusek, Roman Shor 79  \nWeb-based system for drill string analysis with data-driven and  \nnumeric models  \nL. P. Gouveia, F. A. V. Binas, D. V. G. Ferreira, A. S. R.  \nBarboza, E. T. Lima Junior, J. P. L. Santos, J. R. B. Moura Filho 87  \nModel-Based Drilling Performance Analysis of a Downhole Regu  \nlator  \nArviandy G. Aribowo, Ulf Jakob F. Aarsnes, Kaidong Chen, Emmanuel Detournay, Nathan van de Wouw 95  \nOn the Robustness of Deep Drilling Control Techniques  \nRicardo P. F´eres, H´elio J. Cruz Neto, Pedro A.P. Magalh˜aes,  \nMarcelo A. Trindade 107  \nStick slip vibrations in drilling: modeling, estimation, avoidance  \nJean Auriol, Nasser Kazemi 115  \nExamples of Downhole Tools Lateral Dynamics Modeling in Frequency and Time Domains  \nMohamed Mahjoub, Khac-Long Nguyen, Ngoc-Ha Dao, and St´ephane Menand 123  \nNonlinear Modeling of Three-Dimensional Borehole Propagation  \nHe Zhang, Ketan Bhaidasna 133  \nDistributed drilling system analysis: a time-delay system approach  \nShabnam Tashakori, Mohammad A. Faghihi 141  \nList of participants 153  \nForeword  \nThe Sixth International Colloquium on Nonlinear Dynamics and Control of Deep Drilling Systems was held at the Federal University of Rio de Janeiro (UFRJ), Centro de Tecnologia, Cidade Universit´aria, Brazil. This meeting followed previous meetings, which were held at the University of Maryland, College Park, MD (2022), Preikestolen, Norway (2018), the University of Minnesota, Minneapolis, MN, USA (2014), the Eindhoven University of Technology, Eindhoven, the Netherlands (2012), and the University of Li`ege, Li`ege, Belgium (2009) . This is t","cbCaiey1Tnj87Nqd","https://ap.wps.com/l/cbCaiey1Tnj87Nqd","pdf",23001968,168,"English","# Foreword\n# Colloquium Program\n## Insights into Mechanics of Drilling\n## Closing Pandora’s Box: Characterizing and Mitigating High-Frequency Torsional Oscillation\n## Dynamic data-driven application systems and drilling\n## List of participants","[{\"question\":\"本论文集主要涵盖哪些主题？\",\"answer\":\"主要聚焦深井钻进系统的非线性动力学与控制方法，包括力学机理、建模与优化、振动与扭转振荡抑制、以及数据驱动/机器学习辅助的钻进运维与故障检测等。\"},{\"question\":\"会议与论文集的组织结构有哪些部分？\",\"answer\":\"包含Foreword致辞、Colloquium Program会议程序以及论文/报告条目，最后附有List of participants参与者名单。\"},{\"question\":\"数据驱动与机器学习在深井钻进中如何被讨论？\",\"answer\":\"文献中提出将机器学习用于钻进操作数据分类，并作为动态钻进失效/异常检测的支持工具，同时也有动态数据驱动应用系统相关内容。\"}]","Proceedings of the 6th International Colloquium on Nonlinear Dynamics and Control of Deep Drilling Systems | PDF",423]