Safety and risk analysis of managed pressure drilling operation using Bayesian network

Abimbola, Majeed; Khan, Faisal; Khakzad, Nima; Butt, Stephen

doi:10.1016/j.ssci.2015.01.010

Cited by 160 publications

(70 citation statements)

References 6 publications

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“…Such studies help to determine the probability of blowouts, facilitating the preventive and mitigating actions for undesired events. Also described in the literature is the impact on the operation risk by the introduction of new drilling techniques, such as MPD (managed pressure drilling) and UBD (underbalanced drilling) (Abimbola et al 2015). Although there were interesting assessments on the probabilities of failure of several basic events in offshore well construction operations (Abimbola et al 2014;OREDA 2002), no studies were found in the literature that included the selection of the closure sequence (EDS) of the BOP stack rams, in emergency disconnect events, in case of BOP closing failure.…”

Section: Eds State-of-the-art Surveymentioning

confidence: 99%

Integrated risk management: a Petrobras application in offshore well construction safety to minimize critical emergency disconnections

et al. 2018

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In order to avoid mistakes and to save a great deal of time in analysis, an innovative methodology was developed that can analyze the well operations and rig characteristics involved to define the best emergency disconnect sequence (EDS) available. A solution was developed based on the characteristics of the rigs and blowout preventers (BOPs), and six variables were considered that directly affect the choice of EDS. All possible combinations of 64 scenarios were analyzed, and the priority of choice of the EDS was defined empirically. This paper presents an approach to EDS risk management and examples of exposure time (time without riser safety margin and shear capability) for the same well, which can be lowered from 13% to 0.1%. The impact of this reduction is related to the ability of the BOP to cut some of the heavy casings, in addition to improved availability of EDS modes. This implementation opened up many possibilities for the performance of risk exposure analysis, enabling comparison of several BOP configurations of contracted rigs and selection of the best options. This innovative approach allowed a better management of the rig schedules, prioritizing safety aspects and making it possible to allocate the fleet in a systematic way.

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Section: Eds State-of-the-art Surveymentioning

confidence: 99%

Integrated risk management: a Petrobras application in offshore well construction safety to minimize critical emergency disconnections

et al. 2018

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“…The formation condition and well structure design can also be taken into consideration in terms of the contribution to the lost circulation. Therefore, totally 21 potential root causes in fault tree was found based on the work of Fuh et al (1992), Skogdalen and Vinnem (2012) and Abimbola et al (2015).…”

Section: Risk Identification For Lost Circulationmentioning

confidence: 99%

“…Abimbola et al (2015) have, for example, proposed a BN-based risk model that considers potential scenarios for different pressure regimes. BNs may be derived from other frequently used models, such as bow-ties (BTs), fault trees (FTs) and event trees (ETs), for example with basis in the BTs, FTs and ETs developed by Khakzad et al (2013).…”

Section: Introductionmentioning

confidence: 99%

A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

Zhang

Zheng

et al. 2016

Journal of Natural Gas Science and Engineering

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Abstract:Drilling operations of offshore oil and gas fields are characterized by high reliance on advanced technology, high risk, and high costs due to operating in harsh ocean environments, under complicated geological factors, and extreme operating condition. Lost circulation or well "kick" are examples of hazardous events that may occur while drilling wells and such events may develop into a blowout accident if not handled. Identification and analysis of root causes and consequences are effective measures to prevent serious accidents from happening. The risk of having a blowout may change with time, depending on the stage of the drilling operation, and such kind of dynamics should be captured in risk assessment. This paper presents an approach for determining the conditional probabilities of hazardous events and their consequences. The approach includes models that take into account the influence of degradation and (if used in operation) new real-time information which represents the change in a state of a model parameter (such as state change of mud density) that can be captured while the drilling operation is ongoing.The approach presents a newly-developed model based on the basic theory of Dynamic Bayesian network (DBN) and this proposed model can incorporate some additional nodes to handle the uncertainty issues involving the model uncertainty and relevant parameters' uncertainty and also consider the effect of degradation, which are missed in other papers when using the DBN method for risk assessment of similar systems and operations. The main objective of this newly-developed model is to demonstrate how dynamic risk assessments can be used for incident prediction evolution as well as root cause reasoning during offshore drilling operation, given that a specific event has occurred. A bowtie model is established firstly to link the potential incident scenarios with pressure regimes and formation load capacity, and then the model is translated into a DBN. DBN inference is adapted to perform predictive and diagnostic analysis in different time slices for risk assessment and root cause reasoning. A sensitivity analysis is carried out to find the relative importance of each root cause in generating the potential drilling incidents. A case study with focusing on lost circulation during three drilling scenarios is used to illustrate and verify the feasibility of the proposed approach.Key words: Dynamic Bayesian network (DBN); drilling incidents; dynamic risk assessment; prediction of incident evolution; root cause reasoning Highlights: Potential incident scenarios with pressure regimes and weak formation are presented.  A newly-developed model based on Dynamic Bayesian Network is proposed for offshore drilling incidents.  Effects of model and parameter uncertainty are taken into account.  Prediction of risk evolution and root cause reasoning are performed based on the influence of degradation and occurred events.

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“…To demonstrate the application of the proposed CBN model in complex real-life problems, both BN and CBN models are applied to an offshore managed pressure drilling (MPD) operation case study, adopted from a study by Abimbola et al [38]. MPD is an adaptive drilling process used to precisely control the annular pressure profile throughout the wellbore.…”

Section: Case Study Descriptionmentioning

confidence: 99%

“…MPD is an adaptive drilling process used to precisely control the annular pressure profile throughout the wellbore. The overbalanced drilling technique used in the MPD avoids the flow of formation fluid into the wellbore [38]. In their study, Abimbola et al [38] proposed a methodology based on the BN approach for safety and risk analysis of the MPD operation.…”

Section: Case Study Descriptionmentioning

confidence: 99%

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Hashemi

Khan

Ahmed

2016

Computers & Chemical Engineering

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Safety and risk analysis of managed pressure drilling operation using Bayesian network

Cited by 160 publications

References 6 publications

Integrated risk management: a Petrobras application in offshore well construction safety to minimize critical emergency disconnections

Integrated risk management: a Petrobras application in offshore well construction safety to minimize critical emergency disconnections

A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

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