The Experts below are selected from a list of 2253 Experts worldwide ranked by ideXlab platform
Dag G. Ellingsen - One of the best experts on this subject based on the ideXlab platform.
-
Pneumoproteins in Offshore Drill Floor Workers
MDPI AG, 2019Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Lars Barregard, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Dag G. EllingsenAbstract:The aim was to assess pneumoproteins and a certain biomarker of systemic inflammation in Drill Floor workers exposed to airborne contaminants generated during Drilling offshore, taking into consideration serum biomarkers of smoking, such as nicotine (S-Nico) and cotinine. Blood samples of club cell protein 16 (CC-16), surfactant protein D (SP-D) and C-reactive protein (CRP) were collected before and after a 14-day work period from 65 Drill Floor workers and 65 referents. Air samples of oil mist, Drilling mud components and elemental carbon were collected in person. The Drill Floor workers were exposed to a median air concentration of 0.18 mg/m3 of oil mist and 0.14 mg/m3 of airborne mud particles. There were no differences in the concentrations of CC-16 and SP-D across the 14-day work period and no difference between Drill Floor workers and referents at baseline after adjusting for differences in sampling time and smoking. CRP decreased across the work period. There was a strong association between the CC-16 concentrations and the time of sampling. Current smokers with S-Nico > detection limit (DL) had a statistically significantly lower CC-16 concentration, while smokers with S-Nico <DL had CC-16 concentrations similar to that of the non-smokers. Fourteen days of work offshore had no effect on serum pneumoprotein and CRP concentrations. However, the time of blood sampling was observed to have a strong effect on the measured concentrations of CC-16. The effect of current smoking on the CC-16 concentrations appears to be dependent on the S-Nico concentrations
-
pulmonary function and high resolution computed tomography examinations among offshore Drill Floor workers
International Archives of Occupational and Environmental Health, 2018Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Trond Mogens Aalokken, Anne Gunther, May Brit Lund, Dag G. EllingsenAbstract:The aim of this study was to assess short-term changes in pulmonary function in Drill Floor workers currently exposed to airborne contaminants generated as a result of Drilling offshore. We also aimed to study the prevalence of pulmonary fibrosis using high-resolution computed tomography (HRCT) scans of another group of previously exposed Drill Floor workers. Pulmonary function was measured before and after a 14-day work period in a follow-up study of 65 Drill Floor workers and 65 referents. Additionally, 57 other Drill Floor workers exposed to Drilling fluids during the 1980s were examined with HRCT of the lungs in a cross-sectional study. The Drill Floor workers had a statistically significant decline in forced expiratory volume in 1 s (FEV1) across the 14-day work period after adjustment for diurnal variations in pulmonary function (mean 90 mL, range 30–140 mL), while the small decline among the referents (mean 20 mL, range − 30 to 70 mL) was not of statistical significance. Larger declines in FEV1 among Drill workers were associated with the fewer number of days of active Drilling. There were no signs of pulmonary fibrosis related to oil mist exposure among the other previously exposed Drill Floor workers. After 14 days offshore, a statistically significant decline in FEV1 was observed in the Drill Floor workers, which may not be related to oil mist exposure. No pulmonary fibrosis related to oil mist exposure was observed.
-
Occupational exposure to airborne contaminants during offshore oil Drilling
Environmental Science: Processes & Impacts, 2015Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Yngvar Thomassen, Torill Woldbæk, Dag G. EllingsenAbstract:The aim was to study exposure to airborne contaminants in oil Drillers during ordinary work. Personal samples were collected among 65 Drill Floor workers on four stationary and six moveable rigs in the Norwegian offshore sector. Air concentrations of Drilling mud were determined based on measurements of the non-volatile mud components Ca and Fe. The median air concentration of mud was 140 μg m−3. Median air concentrations of oil mist (180 μg m−3), oil vapour (14 mg m−3) and organic carbon (46 μg m−3) were also measured. All contaminants were detected in all work areas (Drill Floor, shaker area, mud pits, pump room, other areas). The highest air concentrations were measured in the shaker area, but the differences in air concentrations between working areas were moderate. Oil mist and oil vapour concentrations were statistically higher on moveable rigs than on stationary rigs, but after adjusting for differences in mud temperature the differences between rig types were no longer of statistical significance. Statistically significant positive associations were found between mud temperature and the concentrations of oil mist (Spearman's R = 0.46) and oil vapour (0.39), and between viscosity of base oil and oil mist concentrations. Use of pressure washers was associated with higher air concentrations of mud. A series of 18 parallel stationary samples showed a high and statistically significant association between concentrations of organic carbon and oil mist (r = 0.98). This study shows that workers are exposed to airborne non-volatilized mud components. Air concentrations of volatile mud components like oil mist and oil vapour were low, but were present in all the studied working areas.
Løkkevik Jens - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of an Intelligent Autonomous Drilling Rig: Testing and Implementation of Machine Learning and Control Algorithms for Formation Classification, Downhole Vibrations Management and Directional Drilling
Universitetet i Stavanger, 2019Co-Authors: Løken, Erik Andreas, Løkkevik JensAbstract:In recent years, considerable resources have been invested to explore applications for- and to exploit the vast amount of data that gets collected during exploration, Drilling and production of oil and gas. Such data will potentially become a game changer for the industry in terms of reduced costs through improved operational efficiency and fewer accidents, improved HSE through strengthened situational awareness, ensured optimal placement of wells, less wear on equipment and so on. While machine learning algorithms have been around for decades, it is only in the last five to ten years that increased computational power along with heavily digitalized control- and monitoring systems have been made available. Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, the idea of intelligent and fully automated machinery on the Drill Floor that is capable of consistently selecting the best decisions or predictions based on the information available and providing the Driller and operator with such recommendations, becomes closer to a reality every day. This thesis is the result of research carried out on the topic of Drilling automation. Its basis has been improvements and upgrades conducted on a laboratory-scale Drilling rig developed at the University of Stavanger, as part of the multi-disciplinary project; UiS Drillbotics. Main contribution of the thesis is a study on how machine learning can be used to develop models that are capable of accurately predicting what rock formation is being Drilled using an autonomous control system, along with detecting some common Drilling incidents in real-time on the laboratory rig. Methodology is also applied to field data from the Volve field. Furthermore, research and implementation of search algorithms to ensure optimal Drilling speed (ROP), safety to personnel and environment (HSE), and efficiency along with a digitalized Drilling program for directional Drilling, gets presented. Finally, rig upgrades for directional Drilling and research into downhole sensors that get used in a closed-loop steering model is elaborated on
-
Optimization of an Intelligent Autonomous Drilling Rig: Testing and Implementation of Machine Learning and Control Algorithms for Formation Classification, Downhole Vibrations Management and Directional Drilling
Universitetet i Stavanger, 2019Co-Authors: Løken, Erik Andreas, Løkkevik JensAbstract:Master's thesis in Petroleum EngineeringIn recent years, considerable resources have been invested to explore applications for- and to exploit the vast amount of data that gets collected during exploration, Drilling and production of oil and gas. Such data will potentially become a game changer for the industry in terms of reduced costs through improved operational efficiency and fewer accidents, improved HSE through strengthened situational awareness, ensured optimal placement of wells, less wear on equipment and so on. While machine learning algorithms have been around for decades, it is only in the last five to ten years that increased computational power along with heavily digitalized control- and monitoring systems have been made available. Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, the idea of intelligent and fully automated machinery on the Drill Floor that is capable of consistently selecting the best decisions or predictions based on the information available and providing the Driller and operator with such recommendations, becomes closer to a reality every day. This thesis is the result of research carried out on the topic of Drilling automation. Its basis has been improvements and upgrades conducted on a laboratory-scale Drilling rig developed at the University of Stavanger, as part of the multi-disciplinary project; UiS Drillbotics. Main contribution of the thesis is a study on how machine learning can be used to develop models that are capable of accurately predicting what rock formation is being Drilled using an autonomous control system, along with detecting some common Drilling incidents in real-time on the laboratory rig. Methodology is also applied to field data from the Volve field. Furthermore, research and implementation of search algorithms to ensure optimal Drilling speed (ROP), safety to personnel and environment (HSE), and efficiency along with a digitalized Drilling program for directional Drilling, gets presented. Finally, rig upgrades for directional Drilling and research into downhole sensors that get used in a closed-loop steering model is elaborated on.submittedVersio
Niels E. Kirkhus - One of the best experts on this subject based on the ideXlab platform.
-
Pneumoproteins in Offshore Drill Floor Workers
MDPI AG, 2019Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Lars Barregard, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Dag G. EllingsenAbstract:The aim was to assess pneumoproteins and a certain biomarker of systemic inflammation in Drill Floor workers exposed to airborne contaminants generated during Drilling offshore, taking into consideration serum biomarkers of smoking, such as nicotine (S-Nico) and cotinine. Blood samples of club cell protein 16 (CC-16), surfactant protein D (SP-D) and C-reactive protein (CRP) were collected before and after a 14-day work period from 65 Drill Floor workers and 65 referents. Air samples of oil mist, Drilling mud components and elemental carbon were collected in person. The Drill Floor workers were exposed to a median air concentration of 0.18 mg/m3 of oil mist and 0.14 mg/m3 of airborne mud particles. There were no differences in the concentrations of CC-16 and SP-D across the 14-day work period and no difference between Drill Floor workers and referents at baseline after adjusting for differences in sampling time and smoking. CRP decreased across the work period. There was a strong association between the CC-16 concentrations and the time of sampling. Current smokers with S-Nico > detection limit (DL) had a statistically significantly lower CC-16 concentration, while smokers with S-Nico <DL had CC-16 concentrations similar to that of the non-smokers. Fourteen days of work offshore had no effect on serum pneumoprotein and CRP concentrations. However, the time of blood sampling was observed to have a strong effect on the measured concentrations of CC-16. The effect of current smoking on the CC-16 concentrations appears to be dependent on the S-Nico concentrations
-
pulmonary function and high resolution computed tomography examinations among offshore Drill Floor workers
International Archives of Occupational and Environmental Health, 2018Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Trond Mogens Aalokken, Anne Gunther, May Brit Lund, Dag G. EllingsenAbstract:The aim of this study was to assess short-term changes in pulmonary function in Drill Floor workers currently exposed to airborne contaminants generated as a result of Drilling offshore. We also aimed to study the prevalence of pulmonary fibrosis using high-resolution computed tomography (HRCT) scans of another group of previously exposed Drill Floor workers. Pulmonary function was measured before and after a 14-day work period in a follow-up study of 65 Drill Floor workers and 65 referents. Additionally, 57 other Drill Floor workers exposed to Drilling fluids during the 1980s were examined with HRCT of the lungs in a cross-sectional study. The Drill Floor workers had a statistically significant decline in forced expiratory volume in 1 s (FEV1) across the 14-day work period after adjustment for diurnal variations in pulmonary function (mean 90 mL, range 30–140 mL), while the small decline among the referents (mean 20 mL, range − 30 to 70 mL) was not of statistical significance. Larger declines in FEV1 among Drill workers were associated with the fewer number of days of active Drilling. There were no signs of pulmonary fibrosis related to oil mist exposure among the other previously exposed Drill Floor workers. After 14 days offshore, a statistically significant decline in FEV1 was observed in the Drill Floor workers, which may not be related to oil mist exposure. No pulmonary fibrosis related to oil mist exposure was observed.
-
Occupational exposure to airborne contaminants during offshore oil Drilling
Environmental Science: Processes & Impacts, 2015Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Yngvar Thomassen, Torill Woldbæk, Dag G. EllingsenAbstract:The aim was to study exposure to airborne contaminants in oil Drillers during ordinary work. Personal samples were collected among 65 Drill Floor workers on four stationary and six moveable rigs in the Norwegian offshore sector. Air concentrations of Drilling mud were determined based on measurements of the non-volatile mud components Ca and Fe. The median air concentration of mud was 140 μg m−3. Median air concentrations of oil mist (180 μg m−3), oil vapour (14 mg m−3) and organic carbon (46 μg m−3) were also measured. All contaminants were detected in all work areas (Drill Floor, shaker area, mud pits, pump room, other areas). The highest air concentrations were measured in the shaker area, but the differences in air concentrations between working areas were moderate. Oil mist and oil vapour concentrations were statistically higher on moveable rigs than on stationary rigs, but after adjusting for differences in mud temperature the differences between rig types were no longer of statistical significance. Statistically significant positive associations were found between mud temperature and the concentrations of oil mist (Spearman's R = 0.46) and oil vapour (0.39), and between viscosity of base oil and oil mist concentrations. Use of pressure washers was associated with higher air concentrations of mud. A series of 18 parallel stationary samples showed a high and statistically significant association between concentrations of organic carbon and oil mist (r = 0.98). This study shows that workers are exposed to airborne non-volatilized mud components. Air concentrations of volatile mud components like oil mist and oil vapour were low, but were present in all the studied working areas.
Løken, Erik Andreas - One of the best experts on this subject based on the ideXlab platform.
-
Optimization of an Intelligent Autonomous Drilling Rig: Testing and Implementation of Machine Learning and Control Algorithms for Formation Classification, Downhole Vibrations Management and Directional Drilling
Universitetet i Stavanger, 2019Co-Authors: Løken, Erik Andreas, Løkkevik JensAbstract:In recent years, considerable resources have been invested to explore applications for- and to exploit the vast amount of data that gets collected during exploration, Drilling and production of oil and gas. Such data will potentially become a game changer for the industry in terms of reduced costs through improved operational efficiency and fewer accidents, improved HSE through strengthened situational awareness, ensured optimal placement of wells, less wear on equipment and so on. While machine learning algorithms have been around for decades, it is only in the last five to ten years that increased computational power along with heavily digitalized control- and monitoring systems have been made available. Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, the idea of intelligent and fully automated machinery on the Drill Floor that is capable of consistently selecting the best decisions or predictions based on the information available and providing the Driller and operator with such recommendations, becomes closer to a reality every day. This thesis is the result of research carried out on the topic of Drilling automation. Its basis has been improvements and upgrades conducted on a laboratory-scale Drilling rig developed at the University of Stavanger, as part of the multi-disciplinary project; UiS Drillbotics. Main contribution of the thesis is a study on how machine learning can be used to develop models that are capable of accurately predicting what rock formation is being Drilled using an autonomous control system, along with detecting some common Drilling incidents in real-time on the laboratory rig. Methodology is also applied to field data from the Volve field. Furthermore, research and implementation of search algorithms to ensure optimal Drilling speed (ROP), safety to personnel and environment (HSE), and efficiency along with a digitalized Drilling program for directional Drilling, gets presented. Finally, rig upgrades for directional Drilling and research into downhole sensors that get used in a closed-loop steering model is elaborated on
-
Optimization of an Intelligent Autonomous Drilling Rig: Testing and Implementation of Machine Learning and Control Algorithms for Formation Classification, Downhole Vibrations Management and Directional Drilling
Universitetet i Stavanger, 2019Co-Authors: Løken, Erik Andreas, Løkkevik JensAbstract:Master's thesis in Petroleum EngineeringIn recent years, considerable resources have been invested to explore applications for- and to exploit the vast amount of data that gets collected during exploration, Drilling and production of oil and gas. Such data will potentially become a game changer for the industry in terms of reduced costs through improved operational efficiency and fewer accidents, improved HSE through strengthened situational awareness, ensured optimal placement of wells, less wear on equipment and so on. While machine learning algorithms have been around for decades, it is only in the last five to ten years that increased computational power along with heavily digitalized control- and monitoring systems have been made available. Considering the state of art technology that exists today and the significant resources that are being invested into the technology of tomorrow, the idea of intelligent and fully automated machinery on the Drill Floor that is capable of consistently selecting the best decisions or predictions based on the information available and providing the Driller and operator with such recommendations, becomes closer to a reality every day. This thesis is the result of research carried out on the topic of Drilling automation. Its basis has been improvements and upgrades conducted on a laboratory-scale Drilling rig developed at the University of Stavanger, as part of the multi-disciplinary project; UiS Drillbotics. Main contribution of the thesis is a study on how machine learning can be used to develop models that are capable of accurately predicting what rock formation is being Drilled using an autonomous control system, along with detecting some common Drilling incidents in real-time on the laboratory rig. Methodology is also applied to field data from the Volve field. Furthermore, research and implementation of search algorithms to ensure optimal Drilling speed (ROP), safety to personnel and environment (HSE), and efficiency along with a digitalized Drilling program for directional Drilling, gets presented. Finally, rig upgrades for directional Drilling and research into downhole sensors that get used in a closed-loop steering model is elaborated on.submittedVersio
Yngvar Thomassen - One of the best experts on this subject based on the ideXlab platform.
-
Pneumoproteins in Offshore Drill Floor Workers
MDPI AG, 2019Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Lars Barregard, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Dag G. EllingsenAbstract:The aim was to assess pneumoproteins and a certain biomarker of systemic inflammation in Drill Floor workers exposed to airborne contaminants generated during Drilling offshore, taking into consideration serum biomarkers of smoking, such as nicotine (S-Nico) and cotinine. Blood samples of club cell protein 16 (CC-16), surfactant protein D (SP-D) and C-reactive protein (CRP) were collected before and after a 14-day work period from 65 Drill Floor workers and 65 referents. Air samples of oil mist, Drilling mud components and elemental carbon were collected in person. The Drill Floor workers were exposed to a median air concentration of 0.18 mg/m3 of oil mist and 0.14 mg/m3 of airborne mud particles. There were no differences in the concentrations of CC-16 and SP-D across the 14-day work period and no difference between Drill Floor workers and referents at baseline after adjusting for differences in sampling time and smoking. CRP decreased across the work period. There was a strong association between the CC-16 concentrations and the time of sampling. Current smokers with S-Nico > detection limit (DL) had a statistically significantly lower CC-16 concentration, while smokers with S-Nico <DL had CC-16 concentrations similar to that of the non-smokers. Fourteen days of work offshore had no effect on serum pneumoprotein and CRP concentrations. However, the time of blood sampling was observed to have a strong effect on the measured concentrations of CC-16. The effect of current smoking on the CC-16 concentrations appears to be dependent on the S-Nico concentrations
-
pulmonary function and high resolution computed tomography examinations among offshore Drill Floor workers
International Archives of Occupational and Environmental Health, 2018Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Øivind Skare, Raymond Olsen, Yngvar Thomassen, Trond Mogens Aalokken, Anne Gunther, May Brit Lund, Dag G. EllingsenAbstract:The aim of this study was to assess short-term changes in pulmonary function in Drill Floor workers currently exposed to airborne contaminants generated as a result of Drilling offshore. We also aimed to study the prevalence of pulmonary fibrosis using high-resolution computed tomography (HRCT) scans of another group of previously exposed Drill Floor workers. Pulmonary function was measured before and after a 14-day work period in a follow-up study of 65 Drill Floor workers and 65 referents. Additionally, 57 other Drill Floor workers exposed to Drilling fluids during the 1980s were examined with HRCT of the lungs in a cross-sectional study. The Drill Floor workers had a statistically significant decline in forced expiratory volume in 1 s (FEV1) across the 14-day work period after adjustment for diurnal variations in pulmonary function (mean 90 mL, range 30–140 mL), while the small decline among the referents (mean 20 mL, range − 30 to 70 mL) was not of statistical significance. Larger declines in FEV1 among Drill workers were associated with the fewer number of days of active Drilling. There were no signs of pulmonary fibrosis related to oil mist exposure among the other previously exposed Drill Floor workers. After 14 days offshore, a statistically significant decline in FEV1 was observed in the Drill Floor workers, which may not be related to oil mist exposure. No pulmonary fibrosis related to oil mist exposure was observed.
-
Occupational exposure to airborne contaminants during offshore oil Drilling
Environmental Science: Processes & Impacts, 2015Co-Authors: Niels E. Kirkhus, Bente Ulvestad, Yngvar Thomassen, Torill Woldbæk, Dag G. EllingsenAbstract:The aim was to study exposure to airborne contaminants in oil Drillers during ordinary work. Personal samples were collected among 65 Drill Floor workers on four stationary and six moveable rigs in the Norwegian offshore sector. Air concentrations of Drilling mud were determined based on measurements of the non-volatile mud components Ca and Fe. The median air concentration of mud was 140 μg m−3. Median air concentrations of oil mist (180 μg m−3), oil vapour (14 mg m−3) and organic carbon (46 μg m−3) were also measured. All contaminants were detected in all work areas (Drill Floor, shaker area, mud pits, pump room, other areas). The highest air concentrations were measured in the shaker area, but the differences in air concentrations between working areas were moderate. Oil mist and oil vapour concentrations were statistically higher on moveable rigs than on stationary rigs, but after adjusting for differences in mud temperature the differences between rig types were no longer of statistical significance. Statistically significant positive associations were found between mud temperature and the concentrations of oil mist (Spearman's R = 0.46) and oil vapour (0.39), and between viscosity of base oil and oil mist concentrations. Use of pressure washers was associated with higher air concentrations of mud. A series of 18 parallel stationary samples showed a high and statistically significant association between concentrations of organic carbon and oil mist (r = 0.98). This study shows that workers are exposed to airborne non-volatilized mud components. Air concentrations of volatile mud components like oil mist and oil vapour were low, but were present in all the studied working areas.
