Blowout Prevention - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Blowout Prevention

The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform

Tor Inge Waag – 1st expert on this subject based on the ideXlab platform

  • Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
    , 2020
    Co-Authors: Nadia Saad Noori, Filippo Bianchi, Tor Inge Waag

    Abstract:

    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.

  • 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), 2019
    Co-Authors: Nadia Saad Noori, Tor Inge Waag

    Abstract:

    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.

  • 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), 2019
    Co-Authors: Nadia Saad Noori, Tor Inge Waag

    Abstract:

    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 – 2nd expert on this subject based on the ideXlab platform

  • Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
    , 2020
    Co-Authors: Nadia Saad Noori, Filippo Bianchi, Tor Inge Waag

    Abstract:

    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.

  • 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), 2019
    Co-Authors: Nadia Saad Noori, Tor Inge Waag

    Abstract:

    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.

  • 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), 2019
    Co-Authors: Nadia Saad Noori, Tor Inge Waag

    Abstract:

    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 – 3rd expert on this subject based on the ideXlab platform

  • Identifying defective valves in a Blowout preventer valve network
    , 2013
    Co-Authors: Diptesh Ghosh

    Abstract:

    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.

  • On the Blowout Preventer Testing Problem: An Approach to Checking for Leakage in BOP Networks
    , 2012
    Co-Authors: Diptesh Ghosh

    Abstract:

    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.