Developing leading indicators-based decision support algorithms and probabilistic models using Bayesian network to predict kicks while drilling

Tamim, Nafiz; Laboureur, Delphine; Hasan, A.R.; Mannan, M. Sam

doi:10.1016/j.psep.2018.10.021

Cited by 28 publications

(10 citation statements)

References 8 publications

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“…Use of data regarding MOC, PSSR, planned work on critical component, challenges to safety system/barriers, bypasses of safety systems, etc. can also be used to develop a leading indicator dashboard ( Tamim et al, 2017 , 2019 ) of the overall health of the barriers in play. Such dashboard will enable management to take proactive measures to monitor the performance of preventative barriers before they degrade and lead to an incident.…”

Section: Next Stepsmentioning

confidence: 99%

Guidance to improve the effectiveness of process safety management systems in operating facilities

Behie

Halim

Efaw

et al. 2020

Journal of Loss Prevention in the Process Industries

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The Process Safety Management (PSM) systems at the operating facilities in the Oil & Gas and in Chemical manufacturing industries have matured over the years and have become, at most facilities, very robust and sophisticated. These programs are administrated by Process Safety (PS) teams at both the corporate business units and plant levels and have been effective in reducing the number and severity of PS events across the industries over the past 25 years or so. Incidents however are occurring at a regular interval and in recent times several noteworthy PS events have occurred in the United States which have brought into question the effectiveness of the PSM programs at play. These facilities have been applying their PSM programs with the expectation that the number and severity of PS events would decrease over time. The expected result has not been realized, especially in context to those facilities that have undergone the recent incidents. Current paper reviews a few publicly available PS performance reports of Oil & Gas and Chemical manufacturing industries. The authors identified a few factors at play that have led to these PS events based on their experience, literature review, and incident investigation reports. Most of the factors are intertwined with multiple PSM elements and it requires a holistic approach to address them. Each of the factors is described and the path forward is proposed to improve the effectiveness of PSM programs.

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Section: Next Stepsmentioning

confidence: 99%

Guidance to improve the effectiveness of process safety management systems in operating facilities

Behie

Halim

Efaw

et al. 2020

Journal of Loss Prevention in the Process Industries

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“…[10]- [13], [10], [14]- [19], [13], [3], [20], [21], [22] however fewer works have considered downhole parameter monitoring for early kick detection such as those reported in [7], [8], [9].…”

Section: Thesis Structurementioning

confidence: 99%

“…Previous efforts have focused on developing model-based mathematical techniques as seen in [30]¸ [10]- [13]. Probabilistic methods have been developed in [10], [14]- [19]. Acoustic tools have been employed for kick monitoring in [13] to predict the response to gas influx in real time.…”

Section: Surface Parameter Monitoring For Kick Detectionmentioning

confidence: 99%

“…Assessment of drilling operations to predict kicks and prevent blowout using a leading indicatorbased approach was proposed in [14]. Decision support algorithms were employed to understand kick progression scenario while probabilistic barrier failure model was employed to assess the performance of the primary well control barrier.…”

Section: Surface Parameter Monitoring For Kick Detectionmentioning

confidence: 99%

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Supervised data-driven approach to early kick detection during drilling operation

Muojeke

Venkatesan

Khan

2020

Journal of Petroleum Science and Engineering

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My profound gratitude goes to God Almighty for the gift of life, good health and connecting me to be refined by renowned supervisors. I would like to express my sincere gratitude to my supervisors, Dr. Ramachandran Venkatesan and Dr. Faisal Khan for their continuous support, insightful comments, and for sharpening my research skills from various perspectives. My sincere appreciation goes to Dr. Stephen Butt, who provided me the opportunity to join his team with access to the Drilling Technology Laboratory and research facilities. I would like to thank team member Nhat Minh for his excellent team play, Augustine Osarogiagbon, DTL team members Mohammed Said, Abdelsalam Abugharara, Jeronimo De Moura, Idris Sule, Olalere Oloruntobi and Justin Royce for assistance during the design, development of experimental setup and conducting the experiments. I would also like to thank Technical Services team Dennis Cramm, David Snook, Ken, Aaron and Shawn Organ of the Concrete laboratory. I would like to thank my family: my parents, siblings, godparents, and friends for supporting me throughout my research and my life in general. Special appreciation to Archbishop Martin Currie, Fr. Joe Mroz, SJ, Presentation Sisters and the Sisters of Mercy for immense spiritual support. I also appreciate the Public Service Alliance of Canada for rich extracurricular learning and volunteering experience which included an oversea trip as a delegate on a Social Justice mission to Guatemala and participation in conferences and convention. I thankfully acknowledge the financial support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chair (Tier I) Program in Offshore Safety and Risk Engineering.

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“…The project primarily aimed to develop leading indicators framework for well operations, which was governed by the Mary Kay O'Connor Process Safety Center (MKOPSC) and Ocean Energy Safety Institute (OESI). Tamim et al (2017Tamim et al ( , 2019 summarized the findings of the project which can be divided into two phases. In the first phase of the work a general framework was developed for identifying sets of leading indicators to predict kicks and blowouts.…”

Section: Leading Indicators Researchmentioning

confidence: 99%

Preventing the Next Big Well Control Event – A Leading Indicators Approach

Tamim

Karlsen

Loopik

et al. 2020

Day 3 Wed, October 28, 2020

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The Oil and Gas industry has historically relied on lagging indicators (e.g., number of spill events, volume of spill, number of well kicks, personal safety statistics) for measuring and monitoring safety performance. Even though these metrics provide some useful information on an organization's safety culture, they do not offer any predictive insights. Leading indicators are metrics for measuring and monitoring events that can lead to or contribute towards an incident. Well control incidents are low frequency-high consequence events. Carefully developed and tracked leading indicators can provide valuable information for preventing significant control events. Currently, a collaborative project is underway to engage the upstream oil and gas industry to develop leading indicators-based dashboard tools for predicting and thereby preventing well control incidents. The goal of this project is to develop a set of leading indicators-driven predictive tools with customizable dashboard displays, for multiple levels of decision makers, to constantly monitor and understand well operations risk from a process safety standpoint. This tool would provide the users (from frontline to management) with role-specific information from status of engineering barriers (e.g., hydrostatic head, BOP status and health) to organizational process safety elements (e.g., procedure, compliance, design, risk management). The ‘processed’ information presented in a dashboard setting can be considered as early warning signs of well control events, which should drive targeted corrective actions to minimize risks in well operations. The project has already identified significant work in this area both from academic and industrial sectors, which will help to build the foundation for moving forward towards building a robust and useful Leading Indicators Dashboard.

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Developing leading indicators-based decision support algorithms and probabilistic models using Bayesian network to predict kicks while drilling

Cited by 28 publications

References 8 publications

Guidance to improve the effectiveness of process safety management systems in operating facilities

Guidance to improve the effectiveness of process safety management systems in operating facilities

Supervised data-driven approach to early kick detection during drilling operation

Preventing the Next Big Well Control Event – A Leading Indicators Approach

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