Gary H. Farrow, A. Potts, Simon Dimopoulos, A. Kilner
The first phase of the Chain FEARS (Finite Element Analysis of Residual Strength) Joint Industry Project (JIP) aimed to develop guidance for the determination of a rational discard criteria for mooring chains subject to severe pitting corrosion which, based on current code requirements, would otherwise require immediate removal and replacement. Critical to the ability to establish rational discard criteria, is the ability to accurately predict the residual strength of degraded chain, and to have as a benchmark for loss in strength, an accurate estimate of the chain in its as-new condition. With a correlated FEA method for residual strength prediction and a benchmark for as-new condition capacity, it would then be possible to establish a theoretical relationship between different types of degradation and mooring chain capacity loss, from which rational discard criteria would be derived. To this end the Chain FEARS JIP first developed a Finite Element Analyses (FEA) residual capacity assessment method to accurately predict the residual strength of degraded chains. A number of assessments were carried out to establish the sensitivity of the Predicted Break Load (PBL) to both engineering parameters such as friction coefficient, and numerical modelling techniques. The developed method was validated by the correlation of the PBL against a number of physical break tests. This paper presents a review of the break strength test data of pitting corrosion degraded chain links. The FEA modelling methodology based Predicted Break Load (PBL) are compared with the test data Actual Break Load (ABL) along with the sensitivity of engineering parameters and numerical model modelling techniques on predictions. The developed FEA method accurately predicts the location of the ‘failure’ within the chain string and the ductile necking failure mode, determined to be the prevalent mode of failure for the chain links samples considered in this study. The degree of correlation between PBL and ABL confirms that accurate prediction of the effects of corrosion degradation consequent on uniform and large pitting corrosion can be accurately predicted by use of the Finite Element Method. The developed FEA method was also employed to establish a benchmark for the strength capacity of as-new condition links as presented in [1], the basis for assessing the relationship between corrosion degradation and residual chain link capacity [2] and a basis for a multi-axial fatigue assessment method to establish the fatigue capacity of as-new and degraded chain links [3,4,5].
{"title":"Correlation of Finite Element Analysis FEA Predicted Residual Strength of Degraded Offshore Mooring Chains with Test Data","authors":"Gary H. Farrow, A. Potts, Simon Dimopoulos, A. Kilner","doi":"10.4043/29528-MS","DOIUrl":"https://doi.org/10.4043/29528-MS","url":null,"abstract":"\u0000 The first phase of the Chain FEARS (Finite Element Analysis of Residual Strength) Joint Industry Project (JIP) aimed to develop guidance for the determination of a rational discard criteria for mooring chains subject to severe pitting corrosion which, based on current code requirements, would otherwise require immediate removal and replacement.\u0000 Critical to the ability to establish rational discard criteria, is the ability to accurately predict the residual strength of degraded chain, and to have as a benchmark for loss in strength, an accurate estimate of the chain in its as-new condition. With a correlated FEA method for residual strength prediction and a benchmark for as-new condition capacity, it would then be possible to establish a theoretical relationship between different types of degradation and mooring chain capacity loss, from which rational discard criteria would be derived.\u0000 To this end the Chain FEARS JIP first developed a Finite Element Analyses (FEA) residual capacity assessment method to accurately predict the residual strength of degraded chains. A number of assessments were carried out to establish the sensitivity of the Predicted Break Load (PBL) to both engineering parameters such as friction coefficient, and numerical modelling techniques. The developed method was validated by the correlation of the PBL against a number of physical break tests.\u0000 This paper presents a review of the break strength test data of pitting corrosion degraded chain links. The FEA modelling methodology based Predicted Break Load (PBL) are compared with the test data Actual Break Load (ABL) along with the sensitivity of engineering parameters and numerical model modelling techniques on predictions. The developed FEA method accurately predicts the location of the ‘failure’ within the chain string and the ductile necking failure mode, determined to be the prevalent mode of failure for the chain links samples considered in this study. The degree of correlation between PBL and ABL confirms that accurate prediction of the effects of corrosion degradation consequent on uniform and large pitting corrosion can be accurately predicted by use of the Finite Element Method.\u0000 The developed FEA method was also employed to establish a benchmark for the strength capacity of as-new condition links as presented in [1], the basis for assessing the relationship between corrosion degradation and residual chain link capacity [2] and a basis for a multi-axial fatigue assessment method to establish the fatigue capacity of as-new and degraded chain links [3,4,5].","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":"87735848","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}
Sue Wang, Xiying Zhang, Tom Kwan, Kai-tung Ma, Zhen Li, D. Baker, A. Izadparast, Gary H. Farrow, A. Potts, A. Nair, M. Prabhu, P. Vargas, I. Pérez, Meng Luo, E. Fontaine
Evaluation of corroded chain link for continued use or life extension is a challenging task for the industry. ABS, together with fifteen (15) participating organizations, initiated the Fatigue of Corroded Chains (FoCCs) Joint Industry Project (JIP) in 2016. The objective of the FoCCs JIP is to investigate methodologies for assessing remaining fatigue life of the corroded mooring chain used for floating production systems. The JIP scope includes fatigue testing in labs and finite element analysis (FEA) of corroded chain samples retrieved from six floating production facilities in West Africa and the North Sea. The participating organizations include oil majors, chain manufactures, consulting firms, and classification societies, which represent a pool of broad range of mooring knowledge and experience. Knowledge gained from the JIP will be summarized and used toward the development of guidance notes for assessing fatigue life of corroded mooring chain for the industry. Six sets of mooring chain samples with different corrosion conditions have been collected, cleaned and digitally scanned for fatigue testing and FEA. Procedures for testing and analysis have been developed with the objective of establishing commonly accepted methods. Different FEA procedures have been studied for making a better prediction of stress ranges of the corroded chain links. The findings from the fatigue testing and FEA will be utilized as basis for further development of the methods for fatigue assessment of corroded mooring chain. This paper summarizes the tests and FE analysis work for the selected chain samples. The JIP research work has found that corrosion, either general corrosion or local/pitting corrosion, can significantly reduce the chain fatigue capacity. The location and the geometry of corrosion pits have more impact on fatigue lives than the pit size. The JIP study has shown that FE analysis is an effective tool to capture the hot spot of corroded chain links and can provide insight in their fatigue performance. Different methods on the assessment of the stress range of a hot spot are compared and discussed.
{"title":"Assessing Fatigue Life of Corroded Mooring Chains Through Advanced Analysis","authors":"Sue Wang, Xiying Zhang, Tom Kwan, Kai-tung Ma, Zhen Li, D. Baker, A. Izadparast, Gary H. Farrow, A. Potts, A. Nair, M. Prabhu, P. Vargas, I. Pérez, Meng Luo, E. Fontaine","doi":"10.4043/29449-MS","DOIUrl":"https://doi.org/10.4043/29449-MS","url":null,"abstract":"\u0000 Evaluation of corroded chain link for continued use or life extension is a challenging task for the industry. ABS, together with fifteen (15) participating organizations, initiated the Fatigue of Corroded Chains (FoCCs) Joint Industry Project (JIP) in 2016. The objective of the FoCCs JIP is to investigate methodologies for assessing remaining fatigue life of the corroded mooring chain used for floating production systems. The JIP scope includes fatigue testing in labs and finite element analysis (FEA) of corroded chain samples retrieved from six floating production facilities in West Africa and the North Sea. The participating organizations include oil majors, chain manufactures, consulting firms, and classification societies, which represent a pool of broad range of mooring knowledge and experience. Knowledge gained from the JIP will be summarized and used toward the development of guidance notes for assessing fatigue life of corroded mooring chain for the industry.\u0000 Six sets of mooring chain samples with different corrosion conditions have been collected, cleaned and digitally scanned for fatigue testing and FEA. Procedures for testing and analysis have been developed with the objective of establishing commonly accepted methods. Different FEA procedures have been studied for making a better prediction of stress ranges of the corroded chain links. The findings from the fatigue testing and FEA will be utilized as basis for further development of the methods for fatigue assessment of corroded mooring chain. This paper summarizes the tests and FE analysis work for the selected chain samples. The JIP research work has found that corrosion, either general corrosion or local/pitting corrosion, can significantly reduce the chain fatigue capacity. The location and the geometry of corrosion pits have more impact on fatigue lives than the pit size. The JIP study has shown that FE analysis is an effective tool to capture the hot spot of corroded chain links and can provide insight in their fatigue performance. Different methods on the assessment of the stress range of a hot spot are compared and discussed.","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":"90237214","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}
A soil response framework for use in fatigue assessment of offshore wells and piles is presented. The framework covers clay and sand soil types. It was developed through comprehensive series of physical testing and numerical simulations. It hinges on determination of the unload-reload secant stiffness response of soils degraded under cyclic fatigue loading and reaching a steady-state condition. The framework comprises two calibrated approaches: spring-only and spring-dashpot. The latter is more appropriate when dynamic response of a structure needs to be more accurately determined through for time-domain analysis. Efficacy and validation of the framework are demonstrated through three (3) field monitoring programs involving offshore wells installed in ground conditions ranging from soft clays typically encountered in deepwater to layered sands and clays in shallow waters. Further validation is provided by presenting results from an extensive laboratory testing program involving nine (9) soil samples taken from various geographical locations against the key relationships of the framework. The laboratory tests were conducted in a novel apparatus developed specifically for obtaining soil resistance–displacement relationship for input to fatigue analysis.
{"title":"Validation and Extension of Soil Response Framework for Fatigue Analysis of Offshore Wells and Piles","authors":"A. Zakeri, H. Sturm, P. Jeanjean","doi":"10.4043/29236-MS","DOIUrl":"https://doi.org/10.4043/29236-MS","url":null,"abstract":"\u0000 A soil response framework for use in fatigue assessment of offshore wells and piles is presented. The framework covers clay and sand soil types. It was developed through comprehensive series of physical testing and numerical simulations. It hinges on determination of the unload-reload secant stiffness response of soils degraded under cyclic fatigue loading and reaching a steady-state condition. The framework comprises two calibrated approaches: spring-only and spring-dashpot. The latter is more appropriate when dynamic response of a structure needs to be more accurately determined through for time-domain analysis. Efficacy and validation of the framework are demonstrated through three (3) field monitoring programs involving offshore wells installed in ground conditions ranging from soft clays typically encountered in deepwater to layered sands and clays in shallow waters. Further validation is provided by presenting results from an extensive laboratory testing program involving nine (9) soil samples taken from various geographical locations against the key relationships of the framework. The laboratory tests were conducted in a novel apparatus developed specifically for obtaining soil resistance–displacement relationship for input to fatigue 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":"88791448","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}
Fatigue damage due to structural stress is a common problem for equipment used in offshore drilling such subsea connectors and subsea riser joints. Fatigue damage is characterized by the weakening of a structure or component of the equipment due to cyclic loading. Consistently operating the equipment above the operational parameters can lead to premature failure of the equipment causing unplanned downtime and posing a safety risk to nearby workers. There are several methods currently being used to determine cumulative fatigue damage as a way of assessing the operational life of machines used for drilling. Linear cumulative fatigue damage analysis is one of the most used methods for life prediction of a structure and components of equipment subjected to cyclic loading. The model involves examining the operational stress ranges caused by cyclic loading and comparing them to an established fatigue curve to estimate the total utilization and predict equipment failure. However, the linear damage rule (Miner's rule) has several limitations namely: The damage model often depends on complex, and time consuming, stress analysis depicting exact geometry and operating conditions.Damage can only be assessed subsequently, making it difficult to forecast use and plan scheduled maintenance. This document presents the development of a mechanical stress soft sensing algorithm for determining real-time cumulative fatigue damage using finite element analysis with response surface methodology. The results in this document show that the new real-time cumulative damage determination approach could effectively help address the limitations of the current models by providing a means of determining real-time cumulative damage with little computational power.
{"title":"Equipment Health Monitoring and Damage Prediction Using Mechanical Stress Soft Sensing Through Data Analytics","authors":"R. Williams","doi":"10.4043/29460-MS","DOIUrl":"https://doi.org/10.4043/29460-MS","url":null,"abstract":"\u0000 Fatigue damage due to structural stress is a common problem for equipment used in offshore drilling such subsea connectors and subsea riser joints. Fatigue damage is characterized by the weakening of a structure or component of the equipment due to cyclic loading. Consistently operating the equipment above the operational parameters can lead to premature failure of the equipment causing unplanned downtime and posing a safety risk to nearby workers. There are several methods currently being used to determine cumulative fatigue damage as a way of assessing the operational life of machines used for drilling. Linear cumulative fatigue damage analysis is one of the most used methods for life prediction of a structure and components of equipment subjected to cyclic loading. The model involves examining the operational stress ranges caused by cyclic loading and comparing them to an established fatigue curve to estimate the total utilization and predict equipment failure. However, the linear damage rule (Miner's rule) has several limitations namely: The damage model often depends on complex, and time consuming, stress analysis depicting exact geometry and operating conditions.Damage can only be assessed subsequently, making it difficult to forecast use and plan scheduled maintenance.\u0000 This document presents the development of a mechanical stress soft sensing algorithm for determining real-time cumulative fatigue damage using finite element analysis with response surface methodology. The results in this document show that the new real-time cumulative damage determination approach could effectively help address the limitations of the current models by providing a means of determining real-time cumulative damage with little computational power.","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":"84802922","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}
Jonathan Fernández, A. Arredondo, W. Storesund, J. González
Whilst it is known that mean stress has an effect on the fatigue endurance of steel components, this effect is not considered when designing mooring system components. The S-N and T-N fatigue design curves for mooring chain in the standards are based on tests carried out at a single mean load, which is 20% of the chain minimum breaking load (MBL), and these curves are used to compute the damage of all load cycles regardless of their mean value. Lately it has been found that the effect of the mean load can be larger than probably expected, and that mooring chains exhibit a significant increase of fatigue capacity when cyclically loaded at reduced mean load. In the majority of the floating units, the pre-tension of the moorings without environmental loads is below or well below 15% of the chain MBL, and most, if not all, of the in-service damage is produced at mean loads below 20% MBL. This in practice results in additional conservatism to the fatigue life computed using the existing design curves. Some deepwater units, subjected to high pre-tension level, can experience some or relevant damage occurring at mean loads above 20% MBL, which would be underestimated with the present design approach. The paper provides an insight on the effect of the mean load on the fatigue endurance of mooring chains and quantifies this dependency based on a large number of fatigue tests carried out on different chain diameters between 70 and 171 mm, different grades, and different mean loads ranging between 7% and 20% of the MBL of the tested chains. The Smith-Watson-Topper (SWT) mean stress correction model is used to transform the stress state of the tested chains into different stress states associated to different mean loads. Then regression analyses are performed and correction functions derived for the design curves of both S-N and T-N approaches to account for the mean load while keeping the same confidence of the existing curves.
{"title":"Influence of the Mean Load on the Fatigue Performance of Mooring Chains","authors":"Jonathan Fernández, A. Arredondo, W. Storesund, J. González","doi":"10.4043/29621-MS","DOIUrl":"https://doi.org/10.4043/29621-MS","url":null,"abstract":"\u0000 Whilst it is known that mean stress has an effect on the fatigue endurance of steel components, this effect is not considered when designing mooring system components. The S-N and T-N fatigue design curves for mooring chain in the standards are based on tests carried out at a single mean load, which is 20% of the chain minimum breaking load (MBL), and these curves are used to compute the damage of all load cycles regardless of their mean value.\u0000 Lately it has been found that the effect of the mean load can be larger than probably expected, and that mooring chains exhibit a significant increase of fatigue capacity when cyclically loaded at reduced mean load.\u0000 In the majority of the floating units, the pre-tension of the moorings without environmental loads is below or well below 15% of the chain MBL, and most, if not all, of the in-service damage is produced at mean loads below 20% MBL. This in practice results in additional conservatism to the fatigue life computed using the existing design curves. Some deepwater units, subjected to high pre-tension level, can experience some or relevant damage occurring at mean loads above 20% MBL, which would be underestimated with the present design approach.\u0000 The paper provides an insight on the effect of the mean load on the fatigue endurance of mooring chains and quantifies this dependency based on a large number of fatigue tests carried out on different chain diameters between 70 and 171 mm, different grades, and different mean loads ranging between 7% and 20% of the MBL of the tested chains.\u0000 The Smith-Watson-Topper (SWT) mean stress correction model is used to transform the stress state of the tested chains into different stress states associated to different mean loads. Then regression analyses are performed and correction functions derived for the design curves of both S-N and T-N approaches to account for the mean load while keeping the same confidence of the existing curves.","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":"90936718","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}
Pile foundations supporting offshore structures are typically subjected to lateral cyclic loading. In design, the pile response under peak design load is generally estimated using a beam-column model where soil-structure interaction is simulated with a series of uncoupled non-linearly force-displacement springs (p-y curves) along the depth of the pile. In clay, the current state-of-practice uses p-y springs derived from some limited field pile tests carried out in the 1950s at Sabine River site, where only one soil type was tested and the cyclic load history applied in the tests was intended to provide a lower bound estimate of soil reaction under cyclic loading. In this study, comprehensive analyses are carried out to investigate the pile responses under two representative storm load histories (Gulf of Mexico (GoM) hurricannes and North Sea winter storms), in three different soil conditions (Gulf of Mexico clays, North Sea soft clays and North Sea stiff clays) for two types of structures (jackets and spars). The analyses use a numerical procedure that was developed in recent years and is fundamentally based on soil response measured at element level. From the parametric analyses, cyclic p-y curves are recommended for the design of jackets under GoM and North Sea metocean and soil conditions, and for spar anchors in GoM conditions.
{"title":"Cyclic p-y Curves in Clays for Offshore Structures","authors":"Youhu Zhang, K. Andersen, P. Jeanjean","doi":"10.4043/29346-MS","DOIUrl":"https://doi.org/10.4043/29346-MS","url":null,"abstract":"\u0000 Pile foundations supporting offshore structures are typically subjected to lateral cyclic loading. In design, the pile response under peak design load is generally estimated using a beam-column model where soil-structure interaction is simulated with a series of uncoupled non-linearly force-displacement springs (p-y curves) along the depth of the pile. In clay, the current state-of-practice uses p-y springs derived from some limited field pile tests carried out in the 1950s at Sabine River site, where only one soil type was tested and the cyclic load history applied in the tests was intended to provide a lower bound estimate of soil reaction under cyclic loading. In this study, comprehensive analyses are carried out to investigate the pile responses under two representative storm load histories (Gulf of Mexico (GoM) hurricannes and North Sea winter storms), in three different soil conditions (Gulf of Mexico clays, North Sea soft clays and North Sea stiff clays) for two types of structures (jackets and spars). The analyses use a numerical procedure that was developed in recent years and is fundamentally based on soil response measured at element level. From the parametric analyses, cyclic p-y curves are recommended for the design of jackets under GoM and North Sea metocean and soil conditions, and for spar anchors in GoM conditions.","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":"77875349","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}
N. Verma, A. Wasson, Zhen Li, H. Sidhar, X. Yue, Haiping He, H. Jin, S. Ling, H. Jun, A. Ozekcin
Oil and gas industry experiences indicate mooring chain corrosion is a major challenge. Observed corrosion rates in the field can be several times higher than the design allowance. In addition, pitting corrosion is not considered in design but can be significant in service. Pre-emptive chain replacements may be required which are typically very costly. In addition to corrosion, some of the other performance factors for mooring chains include strength, wear resistance, toughness and fatigue resistance. Carbon steel is the conventional material currently employed for mooring chains. There are significant incentives to develop new material technologies with improved seawater corrosion and wear performance for mooring chain application. This paper describes one such new material technology – High Manganese Steel (HMS), and its assessment for mooring chain application. HMS is a family of alloyed steels that, when optimized, can offer improved properties over conventional carbon steel. Several HMS chemistries were manufactured, on which small scale performance evaluation testing and weldability assessments were carried out. Based on the assessments, these custom HMS alloys show promising results in terms of the performance factors required for mooring chain application.
{"title":"High Manganese Steel HMS Technology for Mooring Chains Application","authors":"N. Verma, A. Wasson, Zhen Li, H. Sidhar, X. Yue, Haiping He, H. Jin, S. Ling, H. Jun, A. Ozekcin","doi":"10.4043/29246-MS","DOIUrl":"https://doi.org/10.4043/29246-MS","url":null,"abstract":"\u0000 Oil and gas industry experiences indicate mooring chain corrosion is a major challenge. Observed corrosion rates in the field can be several times higher than the design allowance. In addition, pitting corrosion is not considered in design but can be significant in service. Pre-emptive chain replacements may be required which are typically very costly. In addition to corrosion, some of the other performance factors for mooring chains include strength, wear resistance, toughness and fatigue resistance. Carbon steel is the conventional material currently employed for mooring chains. There are significant incentives to develop new material technologies with improved seawater corrosion and wear performance for mooring chain application.\u0000 This paper describes one such new material technology – High Manganese Steel (HMS), and its assessment for mooring chain application. HMS is a family of alloyed steels that, when optimized, can offer improved properties over conventional carbon steel. Several HMS chemistries were manufactured, on which small scale performance evaluation testing and weldability assessments were carried out. Based on the assessments, these custom HMS alloys show promising results in terms of the performance factors required for mooring chain application.","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":"83073502","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}
Rob van Dorp, Nicolas Moscoso, M. Bielefeld, G. Verbeek
Upon recently, most offshore foundations were based on a number of driven piles that were installed with an impact hammer. With the increase in offshore windfarms two major developments have occurred: the shift from jackets to monopiles, leading to increase of the diameter of the foundation piles, and a shift from impact hammers to vibro hammers. As the use of vibratory hammers is becoming more and more common practice, the need for accurate vibro-driving simulation software has increased, which requires that the soil modelling is enhanced to address soil fatigue during pile driving and to predict reliably the soil behavior and resistance during pile driving. In addition pile driving monitoring, which is routine for piles driven with an impact hammer, needs to become common practice. This paper addresses advances in soil modelling that allows more accurate pile driving simulation as well as the application of Vibro Driving Analysis (VDA) or Monitoring (VDM) to validate the simulation results. This is illustrated by a case study of the test pile for the Delft Offshore Turbine project, a 28 m long monopile with a diameter of 4 m that was driven 15 m into dense sand layers using a vibro hammer. After some 6 months the pile was extracted, and pile driving simulations and VDA were done both for the installation and extraction phase.
{"title":"Prediction and Monitoring of Installation of Offshore Foundation Monopiles for Windfarms","authors":"Rob van Dorp, Nicolas Moscoso, M. Bielefeld, G. Verbeek","doi":"10.4043/29400-MS","DOIUrl":"https://doi.org/10.4043/29400-MS","url":null,"abstract":"\u0000 Upon recently, most offshore foundations were based on a number of driven piles that were installed with an impact hammer. With the increase in offshore windfarms two major developments have occurred: the shift from jackets to monopiles, leading to increase of the diameter of the foundation piles, and a shift from impact hammers to vibro hammers.\u0000 As the use of vibratory hammers is becoming more and more common practice, the need for accurate vibro-driving simulation software has increased, which requires that the soil modelling is enhanced to address soil fatigue during pile driving and to predict reliably the soil behavior and resistance during pile driving. In addition pile driving monitoring, which is routine for piles driven with an impact hammer, needs to become common practice.\u0000 This paper addresses advances in soil modelling that allows more accurate pile driving simulation as well as the application of Vibro Driving Analysis (VDA) or Monitoring (VDM) to validate the simulation results. This is illustrated by a case study of the test pile for the Delft Offshore Turbine project, a 28 m long monopile with a diameter of 4 m that was driven 15 m into dense sand layers using a vibro hammer. After some 6 months the pile was extracted, and pile driving simulations and VDA were done both for the installation and extraction phase.","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":"82987313","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}
In the industry, it is a common practice to estimate continuous permeability by establishing a porosity-permeability relationship (poroperm) from conventional core analysis. For each new oilfield, core data is required to build a permeability model for this particular field. Due to reservoir heterogeneity, core derived poroperm can sometimes lead to biased predictions. This is particularly true for oilfields where core samples are scarce or provide a poor coverage of the reservoirs. Improving the accuracy of permeability models in these oilfields is key to better productivity estimation in the oilfield development planning. In 1984, Hearn et al. first proposed the concept of flow unit while studying Shannon reservoir in HartogDraw oilfield, Wyoming, USA. Since Hearn put forward the concept of reservoir flow unit, various Electrofacies classification methods have been proposed by different scholars (Hearn et al. 1984). Generally they can be divided into two categories. One is geological research method, which mainly uses geological cuttings and routine core analysis to calculate flow zone index (FZI) for reservoir classification (Xinlei et al. 2017; Elphick et al. 1999; Kohonen et al. 1982). This method improves the accuracy of permeability evaluation to a certain extent, but it mainly relies on routine core analysis data. Due to poor ductility, this method has certain limitations in the classification of uncored reservoirs. The other is the relatively popular artificial intelligence technology in the oil industry in recent years. With the rapid development of computer hardware, artificial intelligence as a new technology is becoming more and more popular. In particular, the machine learning algorithm represented by neural network has a long history in petroleum industry technology, which solves many problems in petroleum specialty and is favored by many petroleum engineers. Machine learning classifies electrofacies mainly by clustering analysis of logging curves through mathematical algorithms such as neural network classification, K-nearest neighbor classification (KNN) and Multi-Resolution Graph based Clustering (MRGC), and then the corresponding relationship between electrofacies and lithofacies is established by combining core analysis and cutting data. Since this method is based on continuous well logs, it has strong extensibility and is easy to learn from uncored wells (Xinlei et al. 2017). In this paper, we describe a novel workflow that predicts continuous permeability from conventional well logs, based on Electrofacies classification and core data collected in multiple oilfields. In this method, firstly, the MRGC is used to classify electrofacies of the logging curves in coring sections. Secondly, KNN algorithm is used to learn the results of electrofacies classification into uncored sections. Finally, the permeability model based on the electrofacies constraint is established. Compared with the neural network classification, the MRG
在行业中，通过常规岩心分析建立孔隙度-渗透率关系(poroperm)来估计连续渗透率是一种常见的做法。对于每个新油田，都需要岩心数据来建立该特定油田的渗透率模型。由于储层的非均质性，岩心衍生的孔隙度有时会导致有偏差的预测。对于那些岩心样本稀缺或储层覆盖率低的油田尤其如此。在油田开发规划中，提高渗透率模型的精度是提高产能估计的关键。1984年，Hearn等人在研究美国怀俄明州HartogDraw油田的Shannon油藏时首次提出了流动单元的概念。自Hearn提出储层流动单元概念以来，不同学者提出了各种电相分类方法(Hearn et al. 1984)。通常它们可以分为两类。一种是地质研究方法，主要利用地质岩屑和常规岩心分析计算流带指数(FZI)进行储层分类(Xinlei et al. 2017;Elphick et al. 1999;Kohonen et al. 1982)。该方法在一定程度上提高了渗透率评价的准确性，但主要依赖于常规岩心分析数据。由于延展性差，该方法在对无芯储层进行分类时存在一定的局限性。另一个是近年来石油行业比较流行的人工智能技术。随着计算机硬件的飞速发展，人工智能作为一门新兴技术越来越受到人们的欢迎。特别是以神经网络为代表的机器学习算法在石油工业技术中有着悠久的历史，它解决了石油专业中的许多问题，受到许多石油工程师的青睐。机器学习主要通过神经网络分类、k近邻分类(KNN)和多分辨率图聚类(MRGC)等数学算法对测井曲线进行聚类分析，然后结合岩心分析和切削数据建立电相与岩相的对应关系。由于该方法基于连续测井数据，因此具有较强的可扩展性，并且易于从未取芯井中学习(Xinlei et al. 2017)。在本文中，我们描述了一种新的工作流程，该流程基于在多个油田收集的电相分类和岩心数据，从常规测井中预测连续渗透率。该方法首先利用MRGC对取心剖面测井曲线的电相进行分类;其次，利用KNN算法将电相分类结果学习到未取芯剖面;最后，建立了基于电相约束的渗透率模型。与神经网络分类相比，MRGC具有运算速度快、运算结果稳定等优点。算法中的Neighbor Index (NI)参数可以快速对样本数据进行分类，Kernel Representative Index (KRI)参数可以从多次分类的结果中选择最优的类(Yunjiang et al. 2018;Ting et al. 2018)。研究区由13个油田组成，具有相同的沉积环境和矿物学特征。因此，这些油田的测井响应具有相似的特征。这些油田共采集了2122个岩心样品，并对同一口井进行了三重组合测井。根据常规岩心分析和测井特征分析，将测井响应划分为6个电相。然后利用岩心数据为每个电相建立渗透率模型，并用于在没有岩心数据的情况下对新井进行预测。利用提出的思想，对研究区一口生产井的渗透率和产能进行了重新估计。与常规方法相比，相约束渗透率与岩心测量的拟合性更好。由于渗透率的提高，该工作流程计算的产能指数与钻柱测试(DST)的估算结果相吻合。
{"title":"Improving Permeability and Productivity Estimation with Electrofacies Classification and Core Data Collected in Multiple Oilfields","authors":"Xinlei Shi, Hongbing Chen, Ruijuan Li, Xiaoyu Yang, Huan-Min Liu, Ting Li","doi":"10.4043/29214-MS","DOIUrl":"https://doi.org/10.4043/29214-MS","url":null,"abstract":"\u0000 In the industry, it is a common practice to estimate continuous permeability by establishing a porosity-permeability relationship (poroperm) from conventional core analysis. For each new oilfield, core data is required to build a permeability model for this particular field. Due to reservoir heterogeneity, core derived poroperm can sometimes lead to biased predictions. This is particularly true for oilfields where core samples are scarce or provide a poor coverage of the reservoirs. Improving the accuracy of permeability models in these oilfields is key to better productivity estimation in the oilfield development planning.\u0000 In 1984, Hearn et al. first proposed the concept of flow unit while studying Shannon reservoir in HartogDraw oilfield, Wyoming, USA. Since Hearn put forward the concept of reservoir flow unit, various Electrofacies classification methods have been proposed by different scholars (Hearn et al. 1984). Generally they can be divided into two categories. One is geological research method, which mainly uses geological cuttings and routine core analysis to calculate flow zone index (FZI) for reservoir classification (Xinlei et al. 2017; Elphick et al. 1999; Kohonen et al. 1982). This method improves the accuracy of permeability evaluation to a certain extent, but it mainly relies on routine core analysis data. Due to poor ductility, this method has certain limitations in the classification of uncored reservoirs. The other is the relatively popular artificial intelligence technology in the oil industry in recent years. With the rapid development of computer hardware, artificial intelligence as a new technology is becoming more and more popular. In particular, the machine learning algorithm represented by neural network has a long history in petroleum industry technology, which solves many problems in petroleum specialty and is favored by many petroleum engineers. Machine learning classifies electrofacies mainly by clustering analysis of logging curves through mathematical algorithms such as neural network classification, K-nearest neighbor classification (KNN) and Multi-Resolution Graph based Clustering (MRGC), and then the corresponding relationship between electrofacies and lithofacies is established by combining core analysis and cutting data. Since this method is based on continuous well logs, it has strong extensibility and is easy to learn from uncored wells (Xinlei et al. 2017).\u0000 In this paper, we describe a novel workflow that predicts continuous permeability from conventional well logs, based on Electrofacies classification and core data collected in multiple oilfields. In this method, firstly, the MRGC is used to classify electrofacies of the logging curves in coring sections. Secondly, KNN algorithm is used to learn the results of electrofacies classification into uncored sections. Finally, the permeability model based on the electrofacies constraint is established. Compared with the neural network classification, the MRG","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":"88845458","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}
The northwestern Gulf of Mexico outer continental shelf includes approximately 38,660,700 acres of submerged land under federal permitting authority, which are in turn subject to NEPA and NHPA Section 106-compliant archaeological survey. Of those nearly 40 million acres, sea-level curve data correlating with periods of known human occupation in North America suggest that those portions of the outer continental shelf out to the 60 m depth contour could have been exposed as dry land and available for human occupation during the Late Pleistocene and early Holocene before sea-level reached current levels. After four decades of regulatory compliant survey and assessment, no definitive prehistoric archaeological sites have been identified on the outer continental shelf. Although this is a daunting statistic, it does not accurately reflect the state of submerged prehistoric research. It is necessary to assess the data as they pertain to establishing both the temporal and spatial context of submerged prehistoric archaeology.
{"title":"Submerged Prehistoric Archaeology: Confronting Issues of Scale and Context on the Gulf of Mexico Outer Continental Shelf","authors":"A. Evans, M. Keith","doi":"10.4043/29657-MS","DOIUrl":"https://doi.org/10.4043/29657-MS","url":null,"abstract":"\u0000 The northwestern Gulf of Mexico outer continental shelf includes approximately 38,660,700 acres of submerged land under federal permitting authority, which are in turn subject to NEPA and NHPA Section 106-compliant archaeological survey. Of those nearly 40 million acres, sea-level curve data correlating with periods of known human occupation in North America suggest that those portions of the outer continental shelf out to the 60 m depth contour could have been exposed as dry land and available for human occupation during the Late Pleistocene and early Holocene before sea-level reached current levels. After four decades of regulatory compliant survey and assessment, no definitive prehistoric archaeological sites have been identified on the outer continental shelf. Although this is a daunting statistic, it does not accurately reflect the state of submerged prehistoric research. It is necessary to assess the data as they pertain to establishing both the temporal and spatial context of submerged prehistoric archaeology.","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":"85918198","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}
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