Subsea umbilicals are a critical element on most subsea oil & gas production systems. They can include hydraulic conduits and electrical and/or optical cables that serve as a critical lifeline, connecting topside facilities and equipment to subsea architecture, distributing multiple elements including power, chemicals, and communication functions throughout the deepest and harshest of environments. Globally, thousands of subsea umbilical systems are approaching or have reached the end of their design lives after 15-25 years of operation. In many cases, operators would have a need to continue using such umbilical systems for longer, but they are unsure whether that can be done or if they need to invest in new replacement umbilicals. To make the right decision, operators need a well-defined process for evaluating the risks of continuing to produce using the existing umbilical systems. This paper presents a simple methodology for assessing risks related to extending the life of subsea umbilical systems. This method helps operators identify the most critical areas of the system and their associated risks, and helps estimate the reliability of the system in its current and future states. Upon review of available literature and current practices, it was determined that there were no industry standards for extending the life of umbilicals. The method described here is in line with the guidelines and recommendations of API RP 17N [Ref. 1]. The approach includes a review of the available information related to the life of the asset along with risk identification, risk assessment and recommendations for risk mitigation. Reliability models using field data and available literature are used to quantify the system reliability under different conditions, including life-extension scenarios. The proposed method is demonstrated using an actual business case from the North Sea, where risks associated with the life extension of a given umbilical system were identified and reviewed, and where the work has supported the operator's decision-making process.
海底脐带缆是大多数海底油气生产系统的关键部件。它们可以包括液压管道、电气和/或光缆，作为关键的生命线，将上层设施和设备连接到海底建筑，在最深和最恶劣的环境中分配包括电力、化学品和通信功能在内的多种元素。在全球范围内，经过15-25年的运行，成千上万的海底脐带系统正在接近或达到其设计寿命的终点。在许多情况下，作业者需要继续使用这种脐带系统更长时间，但他们不确定是否可以做到这一点，或者是否需要投资购买新的脐带系统。为了做出正确的决定，作业者需要一个明确的流程来评估继续使用现有脐带系统进行生产的风险。本文介绍了一种简单的方法来评估与延长水下脐带系统寿命相关的风险。该方法可以帮助作业者识别系统中最关键的区域及其相关风险，并有助于评估系统在当前和未来状态下的可靠性。通过对现有文献和当前实践的回顾，确定没有延长脐带缆使用寿命的行业标准。这里描述的方法符合API RP 17N的指南和建议[参考文献1]。该方法包括审查与资产寿命有关的现有信息，以及风险识别、风险评估和减轻风险的建议。可靠性模型使用现场数据和现有文献来量化系统在不同条件下的可靠性，包括寿命延长场景。该方法通过北海的一个实际商业案例进行了验证，该案例识别并评估了与延长脐带系统寿命相关的风险，并为作业者的决策过程提供了支持。
{"title":"Reliability Study on the Life Extension of Subsea Umbilical Systems","authors":"João Melo, Murilo Silva","doi":"10.4043/29530-MS","DOIUrl":"https://doi.org/10.4043/29530-MS","url":null,"abstract":"\u0000 Subsea umbilicals are a critical element on most subsea oil & gas production systems. They can include hydraulic conduits and electrical and/or optical cables that serve as a critical lifeline, connecting topside facilities and equipment to subsea architecture, distributing multiple elements including power, chemicals, and communication functions throughout the deepest and harshest of environments.\u0000 Globally, thousands of subsea umbilical systems are approaching or have reached the end of their design lives after 15-25 years of operation. In many cases, operators would have a need to continue using such umbilical systems for longer, but they are unsure whether that can be done or if they need to invest in new replacement umbilicals. To make the right decision, operators need a well-defined process for evaluating the risks of continuing to produce using the existing umbilical systems.\u0000 This paper presents a simple methodology for assessing risks related to extending the life of subsea umbilical systems. This method helps operators identify the most critical areas of the system and their associated risks, and helps estimate the reliability of the system in its current and future states.\u0000 Upon review of available literature and current practices, it was determined that there were no industry standards for extending the life of umbilicals. The method described here is in line with the guidelines and recommendations of API RP 17N [Ref. 1]. The approach includes a review of the available information related to the life of the asset along with risk identification, risk assessment and recommendations for risk mitigation. Reliability models using field data and available literature are used to quantify the system reliability under different conditions, including life-extension scenarios. The proposed method is demonstrated using an actual business case from the North Sea, where risks associated with the life extension of a given umbilical system were identified and reviewed, and where the work has supported the operator's decision-making process.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75289367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During installation of the Big Foot TLP, nine of the 16 pre-installed tendons fell to the seafloor prior to completion of installation. a comprehensive Root Cause Analysis was undertaken to determine the "cause" of the incident. During the early investigative phase, a number of sources of data were identified. Two particular information sets were gathered, interrogated, processed, etc. to extract usable patterns and values to aid in decision making. A third composite data set of deepwater current values was coupled to extract additional insights. All data sets were designed for installation support and not intended for a forensic investigation. Identification of the data, understanding original intent, multi-stage filtering and conclusions will be illustrated. One is a time series containing x, y, z spatial data of a common location on each of the 16 pre-installed tendon assemblies and the second being non-periodic discrete air volume measurements of buoyancy devices. The time series data contained repetitive data points, contained spurious readings, etc. The multi-stage automatic and manual filtering processes utilized to develop non-periodic traces useful for insight and decision making is described. Accompanying this discussion will be the description of original intent and the adaptation from intended use to RCA value. The second data involved non-periodic air volume measurements. Initial interpretation of data during installation was sufficient for operational purposes. A Root Cause Analysis team analyzed the data including use of correlative time series unrelated to the measurement to identify telling patterns useful in decisions. The absolute as recorded data was unable to provide sufficient confidence using standard data processing techniques. Target manual filtering and correlative comparisons provided useful and valuable decision-making insights. The ability to use data designed for specific purposes not aligned with ultimate usage for RCA purposes is shown
在大脚张力腿平台的安装过程中，16根预安装的肌腱中有9根在安装完成之前就掉到了海底。进行了全面的根本原因分析，以确定事件的“原因”。在早期调查阶段，确定了一些数据来源。收集、询问、处理两个特定的信息集，以提取可用的模式和值来帮助决策。第三个复合数据集结合了深水电流值，以获得更多的见解。所有数据集都是为安装支持而设计的，并非用于取证调查。将说明数据的识别、理解原始意图、多阶段过滤和结论。一个是包含16个预安装的肌腱组件上每个共同位置的x, y, z空间数据的时间序列，第二个是浮力装置的非周期性离散空气体积测量。时间序列数据包含重复的数据点，包含虚假读数等。描述了用于开发对洞察力和决策有用的非周期性跟踪的多阶段自动和手动过滤过程。伴随此讨论的将是对原始意图的描述以及从预期用途到RCA价值的调整。第二个数据涉及非周期性空气量测量。安装期间对数据的初步解释足以用于操作目的。根本原因分析团队分析数据，包括使用与测量无关的相关时间序列，以确定对决策有用的告诉模式。使用标准数据处理技术，绝对记录的数据无法提供足够的信心。目标人工过滤和相关比较提供了有用和有价值的决策见解。显示了为特定目的而设计的数据与RCA目的的最终用途不一致的能力
{"title":"Forensic Data & Decisions: Utilizing Data Intended for Other Purposes","authors":"D. Wisch, L. Huyse","doi":"10.4043/29601-MS","DOIUrl":"https://doi.org/10.4043/29601-MS","url":null,"abstract":"\u0000 During installation of the Big Foot TLP, nine of the 16 pre-installed tendons fell to the seafloor prior to completion of installation. a comprehensive Root Cause Analysis was undertaken to determine the \"cause\" of the incident. During the early investigative phase, a number of sources of data were identified. Two particular information sets were gathered, interrogated, processed, etc. to extract usable patterns and values to aid in decision making. A third composite data set of deepwater current values was coupled to extract additional insights. All data sets were designed for installation support and not intended for a forensic investigation. Identification of the data, understanding original intent, multi-stage filtering and conclusions will be illustrated.\u0000 One is a time series containing x, y, z spatial data of a common location on each of the 16 pre-installed tendon assemblies and the second being non-periodic discrete air volume measurements of buoyancy devices. The time series data contained repetitive data points, contained spurious readings, etc. The multi-stage automatic and manual filtering processes utilized to develop non-periodic traces useful for insight and decision making is described. Accompanying this discussion will be the description of original intent and the adaptation from intended use to RCA value. The second data involved non-periodic air volume measurements. Initial interpretation of data during installation was sufficient for operational purposes. A Root Cause Analysis team analyzed the data including use of correlative time series unrelated to the measurement to identify telling patterns useful in decisions.\u0000 The absolute as recorded data was unable to provide sufficient confidence using standard data processing techniques. Target manual filtering and correlative comparisons provided useful and valuable decision-making insights. The ability to use data designed for specific purposes not aligned with ultimate usage for RCA purposes is shown","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75519921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ricardo Toneto de Melo, Tadeu Milão dos Santos, C. J. Valença, Leonidio Buk, Rafael Merenda Pereira, F. M. Passarelli, Fabio Rocha Hoelz
The paper uses a case study approach to present the challenges to develop a large and thick oil carbonate reservoir, full of opportunities but also of uncertainties. Additionally, Libra block development is under a Production Sharing Contract that was awarded to a Consortium where Petrobras is the operator in partnership with Shell, Total, CNOOC Limited and CNPC. This paper will present the main subsea technological achievements made during the execution of the Libra Extended Well Test (EWT) Project so far and also future technologies that will contribute to overcome the full field development challenges.
{"title":"Libra Subsea Achievements and Future Challenges","authors":"Ricardo Toneto de Melo, Tadeu Milão dos Santos, C. J. Valença, Leonidio Buk, Rafael Merenda Pereira, F. M. Passarelli, Fabio Rocha Hoelz","doi":"10.4043/29235-MS","DOIUrl":"https://doi.org/10.4043/29235-MS","url":null,"abstract":"\u0000 The paper uses a case study approach to present the challenges to develop a large and thick oil carbonate reservoir, full of opportunities but also of uncertainties. Additionally, Libra block development is under a Production Sharing Contract that was awarded to a Consortium where Petrobras is the operator in partnership with Shell, Total, CNOOC Limited and CNPC. This paper will present the main subsea technological achievements made during the execution of the Libra Extended Well Test (EWT) Project so far and also future technologies that will contribute to overcome the full field development challenges.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78594724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Offshore floating vessel mooring failure and subsequent loss of station can have catastrophic consequences for the vessel and the associated subsea infrastructure. Therefore, integrity management and timely detection of mooring failure is critical. Traditional methods of failure detection rely on line tension measurements and watch circle approaches. Both these approaches have limitations and are not reliable. Alternate methods of detecting line failure are therefore required. This paper discusses a novel approach of using measured vessel positions and 6-degrees-of-freedom accelerations along with a deep machine learning algorithm to detect mooring line failure in near real time. Results from a numerical case study for a turret moored FPSO with over 4000 test cases demonstrate that this approach can accurately identify failed mooring line cases over 99% of the time.
{"title":"Mooring Line Failure Detection Using Machine Learning","authors":"V. Jaiswal, A. Ruskin","doi":"10.4043/29511-MS","DOIUrl":"https://doi.org/10.4043/29511-MS","url":null,"abstract":"\u0000 Offshore floating vessel mooring failure and subsequent loss of station can have catastrophic consequences for the vessel and the associated subsea infrastructure. Therefore, integrity management and timely detection of mooring failure is critical. Traditional methods of failure detection rely on line tension measurements and watch circle approaches. Both these approaches have limitations and are not reliable. Alternate methods of detecting line failure are therefore required.\u0000 This paper discusses a novel approach of using measured vessel positions and 6-degrees-of-freedom accelerations along with a deep machine learning algorithm to detect mooring line failure in near real time. Results from a numerical case study for a turret moored FPSO with over 4000 test cases demonstrate that this approach can accurately identify failed mooring line cases over 99% of the time.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77961184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Brewer, Darrell Knight, Gautier Noiray, Harit Naik
For decades, upstream personnel have struggled to efficiently gather offshore data and effectively analyze it to make better business decisions. One internal audit conducted by an oil and gas company found its upstream employees spent up to 80 percent of their time just looking for, then converting the data because the data historically has not been housed in one place within one platform. Traditionally, employees must collect large volumes of information from multiple data sources, including spreadsheets, data streams or tacit knowledge. Alone, the Internet of Things (IoT) sensors connected to equipment in the field can send 1,000 readings a minute to engineers, resulting in an insurmountable data for engineers to assess critically. All of this effort results in underutilized time and loss of money. The paper's topic addresses the digital twin technology solution which solves energy company's Big Data problems and recoups the wasted time and associated costs of field workers looking for data. Specifically, FutureOn's FieldTwin technology offers a cloud-based, comprehensive and secure platform with the ability to break up the barriers of the data silos built by legacy systems, creating accessible data across the company. Employees spend less time searching for data, and more time identifying trends and innovative ways to exploit the data, i.e., smarter drilling, greater field automation or improved safety. FieldTwin technology also provides a real-time, data-driven visual representation of the field that creates actionable data, whether it is an operator using the visualization of its subsea assets for field expansions or a renewable company utilizing the digitalization of its offshore wind projects for more effective planning. Studies show humans respond to and process visual data better than any other type of data. The human brain processing images 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual. FieldTwin exploits this reality to enhance data processing and organizational effectiveness spanning project management to risk management in the field.
{"title":"Digital Twin Technology in the Field Reclaims Offshore Resources","authors":"T. Brewer, Darrell Knight, Gautier Noiray, Harit Naik","doi":"10.4043/29231-MS","DOIUrl":"https://doi.org/10.4043/29231-MS","url":null,"abstract":"\u0000 For decades, upstream personnel have struggled to efficiently gather offshore data and effectively analyze it to make better business decisions. One internal audit conducted by an oil and gas company found its upstream employees spent up to 80 percent of their time just looking for, then converting the data because the data historically has not been housed in one place within one platform. Traditionally, employees must collect large volumes of information from multiple data sources, including spreadsheets, data streams or tacit knowledge. Alone, the Internet of Things (IoT) sensors connected to equipment in the field can send 1,000 readings a minute to engineers, resulting in an insurmountable data for engineers to assess critically. All of this effort results in underutilized time and loss of money.\u0000 The paper's topic addresses the digital twin technology solution which solves energy company's Big Data problems and recoups the wasted time and associated costs of field workers looking for data. Specifically, FutureOn's FieldTwin technology offers a cloud-based, comprehensive and secure platform with the ability to break up the barriers of the data silos built by legacy systems, creating accessible data across the company. Employees spend less time searching for data, and more time identifying trends and innovative ways to exploit the data, i.e., smarter drilling, greater field automation or improved safety.\u0000 FieldTwin technology also provides a real-time, data-driven visual representation of the field that creates actionable data, whether it is an operator using the visualization of its subsea assets for field expansions or a renewable company utilizing the digitalization of its offshore wind projects for more effective planning. Studies show humans respond to and process visual data better than any other type of data. The human brain processing images 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual. FieldTwin exploits this reality to enhance data processing and organizational effectiveness spanning project management to risk management in the field.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79808002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flavio Costa Coccoli, Luiz Carlos Milagre Cruz, Thiago Henrique Galindo Vasconcelos, Rogério Pinto Marti
This paper describes the wells completion design, operational performance and use of intelligent completion by Libra Consortium for EWT wells. Additionally, it will describe new technologies that will support the full field development. The paper presents the de-risking plan EWT Program implementation to help full field development, which consists up to four ultra-deepwater mega projects. The EWT wells were assumed as keepers, thus designed as convertible, with metallurgy requirements for both oil production and Water Alternating Gas injection. The intelligent completion, with multi-position valves for each production zone, was selected to allow choking-back gas coning, early gas breakthrough or water production in one zone, while another remains opened. These valves were useful to determine vertical communication between different reservoir zones, making it possible to have buildup in one zone while producing another. Four appraisal wells were completed, for the EWT program up to now, using a full-dual rig. Three of the completions were in sequence, providing a sound learning curve with a significant time reduction, and no installation problems. It was noticeable a correlation between improvement in operational efficiency and a decrease in HSE incidents. The use of intelligent completion made possible an efficient well stimulation, avoiding inter-zonal communication during the acid job. A production rate higher than 60 kboe/day was reached and 100 MM scf/day gas was re-injected. This injection rate implied risk of control lines fatigue due to induced vibration, therefore a Computational Fluid Dynamics (CFD) study was required to determine the maximum injection rate. The Operator implemented mitigation measures for intelligent completion valves actuation since some operational difficulties were observed during the EWT. These situations did not affect the EWT information acquisition plan, but represented significant operational experience for Mero Field future development projects, where multiplexed intelligent completions and open-hole intelligent completion are being also considered. The approach taken by Libra Consortium helps to gain early experience about well design for carbonate reservoirs and intelligent completion behavior, with high CO2 and GOR, where gas re-injection is a necessity and the use of intelligent completion is a mandatory strategy. The performance of the completion design fitted the field evaluation purpose and the wells will be successfully used as keepers.
{"title":"Intelligent Completion in Extended Well Test","authors":"Flavio Costa Coccoli, Luiz Carlos Milagre Cruz, Thiago Henrique Galindo Vasconcelos, Rogério Pinto Marti","doi":"10.4043/29396-MS","DOIUrl":"https://doi.org/10.4043/29396-MS","url":null,"abstract":"\u0000 This paper describes the wells completion design, operational performance and use of intelligent completion by Libra Consortium for EWT wells. Additionally, it will describe new technologies that will support the full field development.\u0000 The paper presents the de-risking plan EWT Program implementation to help full field development, which consists up to four ultra-deepwater mega projects. The EWT wells were assumed as keepers, thus designed as convertible, with metallurgy requirements for both oil production and Water Alternating Gas injection. The intelligent completion, with multi-position valves for each production zone, was selected to allow choking-back gas coning, early gas breakthrough or water production in one zone, while another remains opened. These valves were useful to determine vertical communication between different reservoir zones, making it possible to have buildup in one zone while producing another.\u0000 Four appraisal wells were completed, for the EWT program up to now, using a full-dual rig. Three of the completions were in sequence, providing a sound learning curve with a significant time reduction, and no installation problems. It was noticeable a correlation between improvement in operational efficiency and a decrease in HSE incidents. The use of intelligent completion made possible an efficient well stimulation, avoiding inter-zonal communication during the acid job. A production rate higher than 60 kboe/day was reached and 100 MM scf/day gas was re-injected. This injection rate implied risk of control lines fatigue due to induced vibration, therefore a Computational Fluid Dynamics (CFD) study was required to determine the maximum injection rate. The Operator implemented mitigation measures for intelligent completion valves actuation since some operational difficulties were observed during the EWT. These situations did not affect the EWT information acquisition plan, but represented significant operational experience for Mero Field future development projects, where multiplexed intelligent completions and open-hole intelligent completion are being also considered.\u0000 The approach taken by Libra Consortium helps to gain early experience about well design for carbonate reservoirs and intelligent completion behavior, with high CO2 and GOR, where gas re-injection is a necessity and the use of intelligent completion is a mandatory strategy. The performance of the completion design fitted the field evaluation purpose and the wells will be successfully used as keepers.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81286956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francisco Ferreira da Costa, Guilherme Marques Calôba, Erika Botsman, A. Kaercher, Lucas Silva Lia
The paper uses a case study approach to present the challenges to develop a large and thick oil carbonate reservoir, full of opportunities but also of uncertainties. Additionally, Libra block development is under a Production Share Contract that was award to a Consortium where Petrobras is the operator in partnership with Shell, Total, CNOOC and CNPC. The paper will briefly present the de-risking plan and detail the EWT Program implementation and the way it is helping the full field development, which consists of four mega ultra deepwaters projects.
{"title":"EWT Program - Enabling Optimization and Speed Up for Libra Block Production Systems Development in Ultra-Deepwater","authors":"Francisco Ferreira da Costa, Guilherme Marques Calôba, Erika Botsman, A. Kaercher, Lucas Silva Lia","doi":"10.4043/29501-MS","DOIUrl":"https://doi.org/10.4043/29501-MS","url":null,"abstract":"\u0000 The paper uses a case study approach to present the challenges to develop a large and thick oil carbonate reservoir, full of opportunities but also of uncertainties. Additionally, Libra block development is under a Production Share Contract that was award to a Consortium where Petrobras is the operator in partnership with Shell, Total, CNOOC and CNPC. The paper will briefly present the de-risking plan and detail the EWT Program implementation and the way it is helping the full field development, which consists of four mega ultra deepwaters projects.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85585891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines shear modulus degradation and damping for offshore sands. The work is based on an extensive database of dynamic soil properties for undisturbed and reconstituted granular samples from offshore sites worldwide. The data set consists of direct measurements obtained from advanced laboratory tests (resonant columns). Data considered in this paper includes a wide range of granular marine sediments. The effects of soil properties such as confining pressure and fines content on the dynamic parameters are described and guidance on interpretation and applicability is provided. A new calibrated model is proposed to evaluate normalized shear modulus reduction and damping ratio curves as a function of shear strain of marine sands. The proposed relationship can be used for a wide range of granular soil conditions for direct use in routine offshore dynamic engineering analyses and, in particular, in non-linear site response analysis.
{"title":"Dynamic properties of offshore sands","authors":"L. Zuccarino, D. Morandi, L. Ottonello","doi":"10.4043/29590-MS","DOIUrl":"https://doi.org/10.4043/29590-MS","url":null,"abstract":"\u0000 This paper examines shear modulus degradation and damping for offshore sands. The work is based on an extensive database of dynamic soil properties for undisturbed and reconstituted granular samples from offshore sites worldwide. The data set consists of direct measurements obtained from advanced laboratory tests (resonant columns). Data considered in this paper includes a wide range of granular marine sediments. The effects of soil properties such as confining pressure and fines content on the dynamic parameters are described and guidance on interpretation and applicability is provided.\u0000 A new calibrated model is proposed to evaluate normalized shear modulus reduction and damping ratio curves as a function of shear strain of marine sands. The proposed relationship can be used for a wide range of granular soil conditions for direct use in routine offshore dynamic engineering analyses and, in particular, in non-linear site response analysis.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76612502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper documents the results from the 2017 Society of Naval Architects and Marine Engineers (SNAME) OC-8 Panel Comparative Wind Load Study. Initial unpublished results were presented at a one-day panel at the 2017 SNAME Maritime Convention; however, the final results are brought together for the first time in this paper. A blind, comparative study was organized through the SNAME OC-8 Panel in 2017 to assess the relative accuracy and repeatability of existing wind load estimation methods. Twenty-five companies and organizations throughout the world participated in this study, which encompassed three available wind load estimation methods: empirical building block procedures, wind tunnel testing, and Computational Fluid Dynamics (CFD). To permit an ‘apples-to-apples’ comparison, the same representative semisubmersible design was used by all participants, including a single physical model shipped consecutively to each of the five wind tunnel facilities participating in the study. The most significant finding from the study is the remarkably low variability in wind tunnel and CFD results relative to the empirical building block method incorporated in the U.S. Code of Federal Regulations (CFR), classification rules, and industry codes for stability calculations. Moreover, only wind tunnel and CFD results were able to accurately quantify the contribution of a lifting force and its effect on the overturning moment. The lessons learned from the comparative study will be incorporated into a long-awaited revision to SNAME's wind tunnel testing guideline, and has inspired the development of a new industry guideline which will broadly address wind load estimation methods in design, including the use of CFD throughout the design spiral.
{"title":"A Detailed Look into the 2017 SNAME OC-8 Comparative Wind Load Study","authors":"Kevin Berto, David P. Hodapp, J. Falzarano","doi":"10.4043/29289-MS","DOIUrl":"https://doi.org/10.4043/29289-MS","url":null,"abstract":"\u0000 This paper documents the results from the 2017 Society of Naval Architects and Marine Engineers (SNAME) OC-8 Panel Comparative Wind Load Study. Initial unpublished results were presented at a one-day panel at the 2017 SNAME Maritime Convention; however, the final results are brought together for the first time in this paper.\u0000 A blind, comparative study was organized through the SNAME OC-8 Panel in 2017 to assess the relative accuracy and repeatability of existing wind load estimation methods. Twenty-five companies and organizations throughout the world participated in this study, which encompassed three available wind load estimation methods: empirical building block procedures, wind tunnel testing, and Computational Fluid Dynamics (CFD). To permit an ‘apples-to-apples’ comparison, the same representative semisubmersible design was used by all participants, including a single physical model shipped consecutively to each of the five wind tunnel facilities participating in the study.\u0000 The most significant finding from the study is the remarkably low variability in wind tunnel and CFD results relative to the empirical building block method incorporated in the U.S. Code of Federal Regulations (CFR), classification rules, and industry codes for stability calculations. Moreover, only wind tunnel and CFD results were able to accurately quantify the contribution of a lifting force and its effect on the overturning moment. The lessons learned from the comparative study will be incorporated into a long-awaited revision to SNAME's wind tunnel testing guideline, and has inspired the development of a new industry guideline which will broadly address wind load estimation methods in design, including the use of CFD throughout the design spiral.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72837610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using accurate and high quality data is critical for any application relying heavily on the data, be it machine learning, artificial intelligence, or digital twins. Bad quality and erroneous data can result in inaccurate predictions even if the model is otherwise robust. Ensuring data quality is more critical in real-time applications where there is no human in the loop to perform sense checks on data or results. A real-time digital twin implementation for a floating system uses time-series data from numerous measurements such as wind, waves, GPS, vessel motions, mooring tensions, draft, etc. Statistics computed from the data are used in the digital twin. An extensive data checking and cleaning routine was written that performs data quality checks and corrections on the time series data before statistics are computed. Various types of errors that typically occur in a time series include noise, flat-lined data, clipped data, outliers, and discontinuities. Statistical procedures were developed to check the raw time-series for all these errors. The procedures are generic and robust so they can be used for different types of data. Some data types are slow varying (e.g., GPS) while the others are fast varying random processes. A measurement classified as an error in one type of data is not necessarily an error in the other data type. For example, GPS data can be discontinuous by nature but a discontinuity in the wave data indicates an error. Likewise, checking for white noise in mooring tension data is not that meaningful. We developed parametric data procedures so that the same routine can handle different types of data and their errors. Outlier removal routines use the standard deviation of the time-series which itself could be biased from errors. Therefore, a method to compute unbiased statistics from the raw data is developed and implemented for robust outlier removal. Extensive testing on years of measured data and on hundreds of data channels was performed to ensure that data cleaning procedures function as intended. Statistics (mean, standard deviations, maximum, and minimum) were computed from both the raw and cleaned data. Comparison showed significant differences in raw and cleaned statistics, with the latter obviously being more accurate. Data cleaning, while not sounding as high tech as other analytics algorithms, is a critical foundation of any data science application. Using cleaned time-series data and corresponding statistics ensure that a data analytics model provides actionable results. Clean data and statistics help achieve the intended purpose of the digital twin, which is to inform operators of the health/condition of the asset and flag any anomalous events.
{"title":"Real-Time Cleaning of Time-Series Data for a Floating System Digital Twin","authors":"P. Agarwal, S. McNeill","doi":"10.4043/29642-MS","DOIUrl":"https://doi.org/10.4043/29642-MS","url":null,"abstract":"\u0000 Using accurate and high quality data is critical for any application relying heavily on the data, be it machine learning, artificial intelligence, or digital twins. Bad quality and erroneous data can result in inaccurate predictions even if the model is otherwise robust. Ensuring data quality is more critical in real-time applications where there is no human in the loop to perform sense checks on data or results. A real-time digital twin implementation for a floating system uses time-series data from numerous measurements such as wind, waves, GPS, vessel motions, mooring tensions, draft, etc. Statistics computed from the data are used in the digital twin. An extensive data checking and cleaning routine was written that performs data quality checks and corrections on the time series data before statistics are computed.\u0000 Various types of errors that typically occur in a time series include noise, flat-lined data, clipped data, outliers, and discontinuities. Statistical procedures were developed to check the raw time-series for all these errors. The procedures are generic and robust so they can be used for different types of data. Some data types are slow varying (e.g., GPS) while the others are fast varying random processes. A measurement classified as an error in one type of data is not necessarily an error in the other data type. For example, GPS data can be discontinuous by nature but a discontinuity in the wave data indicates an error. Likewise, checking for white noise in mooring tension data is not that meaningful. We developed parametric data procedures so that the same routine can handle different types of data and their errors. Outlier removal routines use the standard deviation of the time-series which itself could be biased from errors. Therefore, a method to compute unbiased statistics from the raw data is developed and implemented for robust outlier removal.\u0000 Extensive testing on years of measured data and on hundreds of data channels was performed to ensure that data cleaning procedures function as intended. Statistics (mean, standard deviations, maximum, and minimum) were computed from both the raw and cleaned data. Comparison showed significant differences in raw and cleaned statistics, with the latter obviously being more accurate.\u0000 Data cleaning, while not sounding as high tech as other analytics algorithms, is a critical foundation of any data science application. Using cleaned time-series data and corresponding statistics ensure that a data analytics model provides actionable results. Clean data and statistics help achieve the intended purpose of the digital twin, which is to inform operators of the health/condition of the asset and flag any anomalous events.","PeriodicalId":11149,"journal":{"name":"Day 1 Mon, May 06, 2019","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78681210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：