The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
Tor Inge Waag - One of the best experts on this subject based on the ideXlab platform.
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Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
2020Co-Authors: Nadia Saad Noori, Filippo Bianchi, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring operating conditions of drilling systems are critical to the overall production cycle. In this paper, we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work, we aim to design an intelligent system for monitoring the health of IBOP system using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly. The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.
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Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System
2019 IEEE International Systems Conference (SysCon), 2019Co-Authors: Nadia Saad Noori, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires a careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring signs of changes in operating conditions of a machine are critical to the overall oil production cycle. In this paper we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work we aim to design an intelligent system for monitoring the health of IBOP system based using multisensory data. The process comprises two steps: 1) produce IBOP system logical behavior analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop a pattern recognition Neural Networks system for activity monitoring and fault detection for the top drive assembly. HCPN allows simulation and graphical visualization of dynamic discrete process and provides means to identify bottlenecks, deadlocks and optimization parameters. This work presents preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP vale in top drive assembly. The effects of failure rate and repair time of each component on system performance are researched.
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SysCon - Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System
2019 IEEE International Systems Conference (SysCon), 2019Co-Authors: Nadia Saad Noori, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires a careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring signs of changes in operating conditions of a machine are critical to the overall oil production cycle. In this paper we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work we aim to design an intelligent system for monitoring the health of IBOP system based using multisensory data. The process comprises two steps: 1) produce IBOP system logical behavior analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop a pattern recognition Neural Networks system for activity monitoring and fault detection for the top drive assembly. HCPN allows simulation and graphical visualization of dynamic discrete process and provides means to identify bottlenecks, deadlocks and optimization parameters. This work presents preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP vale in top drive assembly. The effects of failure rate and repair time of each component on system performance are researched.
Nadia Saad Noori - One of the best experts on this subject based on the ideXlab platform.
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Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
2020Co-Authors: Nadia Saad Noori, Filippo Bianchi, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring operating conditions of drilling systems are critical to the overall production cycle. In this paper, we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work, we aim to design an intelligent system for monitoring the health of IBOP system using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly. The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.
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Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System
2019 IEEE International Systems Conference (SysCon), 2019Co-Authors: Nadia Saad Noori, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires a careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring signs of changes in operating conditions of a machine are critical to the overall oil production cycle. In this paper we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work we aim to design an intelligent system for monitoring the health of IBOP system based using multisensory data. The process comprises two steps: 1) produce IBOP system logical behavior analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop a pattern recognition Neural Networks system for activity monitoring and fault detection for the top drive assembly. HCPN allows simulation and graphical visualization of dynamic discrete process and provides means to identify bottlenecks, deadlocks and optimization parameters. This work presents preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP vale in top drive assembly. The effects of failure rate and repair time of each component on system performance are researched.
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SysCon - Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System
2019 IEEE International Systems Conference (SysCon), 2019Co-Authors: Nadia Saad Noori, Tor Inge WaagAbstract:Offshore oil drilling is a complex process that requires a careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring signs of changes in operating conditions of a machine are critical to the overall oil production cycle. In this paper we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work we aim to design an intelligent system for monitoring the health of IBOP system based using multisensory data. The process comprises two steps: 1) produce IBOP system logical behavior analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop a pattern recognition Neural Networks system for activity monitoring and fault detection for the top drive assembly. HCPN allows simulation and graphical visualization of dynamic discrete process and provides means to identify bottlenecks, deadlocks and optimization parameters. This work presents preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP vale in top drive assembly. The effects of failure rate and repair time of each component on system performance are researched.
Diptesh Ghosh - One of the best experts on this subject based on the ideXlab platform.
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Identifying defective valves in a Blowout preventer valve network
2013Co-Authors: Diptesh GhoshAbstract:Blowouts are nancially damaging for drilling companies and are ecological hazards. Hence Blowout Prevention equipment is critical infrastructure for drilling companies. Blowout preventer valves are important components of Blowout Prevention equipments and need to be checked regularly. However, since these valves are often physically inaccessible, they are checked in batches called test sets. In this paper we present an exact method to check the functional status of all Blowout preventer valves using a minimum number of test sets. We also present a heuristic method to identify malfunctioning valves if they exist. We illustrate both methods using a real world example.
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On the Blowout Preventer Testing Problem: An Approach to Checking for Leakage in BOP Networks
2012Co-Authors: Diptesh GhoshAbstract:Blowout Preventers (BOPs) and choke manifolds are key pieces of drilling rig equipment to prevent the uncontrolled release of potentially hazardous formation fluids to surface. The Blowout Prevention testing problem is that of testing BOP valves to check if they are functional or not. Several type of testing is done on these valves. This paper deals with the check if the valves are capable of holding pressure. We present a decision model that allows a structured and time saving approach to minimize the number of test sets in order to identify leakage. Recently the BOP terminology has gained prominence and public attention as a result of the Macondo blow-out and resulting oil-spill in the Gulf of Mexico off the coast of the USA.
Zhang Laibin - One of the best experts on this subject based on the ideXlab platform.
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Bow-tie model for offshore drilling Blowout accident
Journal of Safety Science and Technology, 2013Co-Authors: Zhang LaibinAbstract:Offshore drilling is a complex and dynamic system.Meanwhile,like any other process industry,it can also be divided into several independent operating steps and procedures.Therefore,the process model of safety barrier is very suitable to analyze safety of offshore drilling operations.Blowout was an important threat to offshore drilling.A Blowout accident model is very meaningful to instruct offshore drilling safety.Based on safety barrier theory,a bow-tie model for offshore drilling Blowout was established by utilizing fault tree and event tree methods.Causes of an offshore drilling Blowout event were analyzed by fault tree,while development processes of fire explosion accident after a Blowout event were analyzed by event tree.Causes of an offshore drilling Blowout event and consequences of fire explosion accident after the Blowout were combined into a single model.This model is very convenient for operators to understand the whole generating and developing process of an offshore drilling Blowout accident.Therefore,it can be used as a guide for offshore drilling operators to find relevant Blowout Prevention and control or mitigation measures.Finally,by applying the Deepwater Horizon Blowout accident into this model,its effectiveness on analyzing offshore drilling Blowout accident was verified.
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Research on reliability of subsea Blowout preventer based on Markov method
Journal of Safety Science and Technology, 2012Co-Authors: Zhang LaibinAbstract:Subsea Blowout preventer(BOP) is the crucial equipment to ensure offshore drilling safety.Quantitative assessment for its reliability is very meaningful to instruct field well control practice.To cover the limitations of existing reliability assessment methods for subsea BOP system,working states of subsea BOP system were divided into four,which included available with no failure,closed with no failure,failed with no demand,and failed when demanded.Markov model of subsea BOP was built based on Markov method.Markov transition program was presented to find out the transition relationships of the four working states.After analyzing failure statistics of subsea drilling BOP systems installed on 83 deepwater wells in Gulf of Mexico,the definition of well control critical failure for subsea BOP system was defined.Based on the statistics of well control critical failures,reliability of deepwater drilling BOP to prevent Blowout was assessed quantitatively.Compared to the result which ignores failures after BOP closed,Blowout Prevention failure occurrence of subsea BOP system increased by 65 percent.Therefore,traditional quantitative reliability assessment methods may get relatively optimistic conclusions in subsea BOP reliability assessment.Well control critical failure happened during shut-in should not be ignored in field well control practice.
Ramachandran Venkatesan - One of the best experts on this subject based on the ideXlab platform.
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A Numerical and Experimental Study of Kick Dynamics at Downhole
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, 2018Co-Authors: Rakibul Islam, Faisal Khan, Ramachandran VenkatesanAbstract:The early detection of a kick and mitigation with appropriate well control actions can minimize the risk of a Blowout. This paper proposes a downhole monitoring system, and presents a dynamic numerical simulation of a compressible two-phase flow to study the kick dynamics at downhole during drilling operation. This approach enables early kick detection and could lead to the development of potential Blowout Prevention strategies. A pressure cell that mimics a scaled-down version of a downhole is used to study the dynamics of a compressible two-phase flow. The setup is simulated under boundary conditions that resemble realistic scenarios; special attention is given to the transient period after injecting the influx. The main parameters studied include pressure gradient, raising speed of a gas kick, and volumetric behavior of the gas kick with respect to time. Simulation results exhibit a sudden increase of pressure while the kick enters and volumetric expansion of gas as it flows upward. This improved understanding helps to develop effective well control and Blowout Prevention strategies. This study confirms the feasibility and usability of an intelligent drill pipe as a tool to monitor well conditions and develop Blowout risk management strategies.
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Wellbore Blowout Prevention: improving safety through identification and monitoring of early kick indicators
2013Co-Authors: Ayesha Arjumand Nayeem, Ramachandran Venkatesan, Faisal KhanAbstract:Early detection of fluid influx from formation is an important concern for the safety of oil wells. Ignoring the leading indicators of kick such as, reduced drilling fluid density, reduced height of the drilling fluid column and abnormal formation pressure zones may cause the influx to occur in the well bore. Misinterpreting the lagging indicators such as, increase in-flow rate and pit volume, change in string weight and changes in return mud properties may cause difficulty in controlling the influx leading to Blowout occurrence. Current industry practice of monitoring the lagging kick indicators at the surface is inadequate to prevent Blowout until it is combined with the monitoring of leading kick indicators at the bottom of the well bore. In this paper we propose a methodology for improving the safety through sensing of leading kick indicators in the bottom of the wellbore based on the experiments performed in the laboratory, where we can replicate and track the actual full behavior of a kick, starting from leading indicators of deteriorating well condition to the occurrence of the kick. The main focus of this paper is to explain the importance of identification and monitoring of leading and lagging indicators of kick and describe how sensing of leading kick indicators in the bottom of the wellbore improve the safety. References: R. D. Grace, Blowout and well control handbook, Gulf Professional Pub, 2003. D. Veeningen, “Identify Safe Drilling Margin, Detect and Distinguish Kicks from Ballooning and Better Well Control for Deepwater, Through Independent Down hole Measurements” SPE/APPEA International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Perth, Australia, September 2012.
