Risk assessment of Arctic drilling waste management operations based on Bayesian Networks

Int J Syst Assur Eng Manag

Barabadi

2016

Human reliability has a high contribution in maintenance performance, safety, and cost efficiency of any production process. To improve human reliability, the causes of human errors should be identified and the probability of human errors should be quantified. Analysis of human error is very case specific in which the context of the field should be taken into account. The aim of this study is to identify the causes of human errors and, improve human reliability in maintenance activities in cable manufacturing industry. The central thrust of this paper is to employ the three most common HRA techniques -HEART (Human Error Assessment and Reduction Technique), SPAR-H (Standardized Plant Analysis Risk-Human Reliability), and BN (Bayesian Network) for estimating human error probabilities, and then to check the consistency of results obtained. The case study results demonstrated that the main causes of human error during maintenance activities are time pressure, lack of experience, and poor procedure. Moreover, the probabilities of human error obtained by employing the three techniques are almost similar and consistent.

Section: Application Of Bayesian Network (Bn) For Estimating the Probmentioning

confidence: 99%

Human reliability assessment (HRA) in maintenance of production process: a case study

Aalipour

Int J Syst Assur Eng Manag

Barabadi

2016

“…Step 2.5-Select prior probability distribution for the defined system: In this step, a prior reliability or failure rate distribution function needs to be asserted for the defined solids-control system. This function is the description of the failure rate of the solidscontrol system, and failure rate is the measure of frequency of a system or component failure [13]. The prior function represents the probability of n or fewer failures during a time interval of (0, t), when all RIFs are equal to zero or absent, in the course of waste handling activities [13].…”

Section: -2 / Vol 138 October 2016mentioning

confidence: 99%

“…This function is the description of the failure rate of the solidscontrol system, and failure rate is the measure of frequency of a system or component failure [13]. The prior function represents the probability of n or fewer failures during a time interval of (0, t), when all RIFs are equal to zero or absent, in the course of waste handling activities [13]. For instance, assuming that the components fail according to a Poisson process, the probability of n or fewer failures can be calculated as follows [29]: Step 2.6-Construct the likelihood function, based on the system failure rate data: After observing the RIFs' data and defining the prior probability function, the likelihood function has to be constructed.…”

Section: -2 / Vol 138 October 2016mentioning

confidence: 99%

“…Hence, to avoid the inadequacies of traditional risk assessment approaches and to provide solutions to the problems mentioned above, the application of Bayesian networks (BN) is gaining popularity and has been discussed in several pieces of the literature; see, e.g., Refs. [11][12][13][14]. BNs are particularly useful in the risk analysis of offshore waste handling activities, as they allow us to understand the causal relationship as well as to combine any historical data that is available with qualitative data and subjective judgments about the risk of events.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

Journal of Offshore Mechanics and Arctic Engineering

Barabady

Droguett

2016

The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero "hazardous" discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season

“…Furthermore, to provide a basis for comparing alternative ways of achieving a certain benefit, aid safety design and offer a fair basis for evaluating alternative drilling waste handling practices, a number of quantitative risk assessment models have been developed; see, e.g., Refs. [17][18][19][20][21][22]. For instance, Khakzad et al.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Cost-Effectiveness Analysis of Waste Handling Practices in the Arctic Drilling Operation

Journal of Offshore Mechanics and Arctic Engineering

Barabadi

Droguett

2016

As oil and gas companies in the Arctic attempt to maximize the value of each project and optimize their portfolio of investment opportunities, it has become vital to evaluate drilling waste handling practices for their cost-effectiveness in order to support strategic decisions. Identifying cost-effective waste handling practices, which have a minimal environmental footprint, however, is one of the biggest challenges for Arctic offshore industries. The cost and potential risks of drilling waste handling practices in the Arctic offshore operation will differ vastly, depending on the operating environment such as the ice conditions and negative sea temperature. However, in the majority of the available cost-effectiveness and risk analysis literature, the influence of the operating environment on the cost and risk profile has received less attention. Hence, the aim of this paper is to propose a methodology for risk-based cost-effectiveness analysis (RB-CEA) of drilling waste handling practices by considering the complex and fast-changing nature of the Arctic. The central thrust of this paper is to highlight the fact that comparing different alternatives based on the cost elements alone is misleading. The proposed methodology uses risk assessment as a key component for the cost-effectiveness analysis (CEA). The application of the proposed methodology is demonstrated by a case study of the drilling waste handling practices of an oil field in the Barents Sea. The case study results demonstrate that the operating environment causes costs to be between 1.18 and 1.52 times greater, depending on the type of practices and operating season, in the Arctic offshore compared with the North Sea. Further, the risk of undesirable events is between 1.48 and 2.60 times greater during waste handling activities under Arctic operational conditions.Research Council of Norway ENI Norge AS through the EWMA (Environmental Waste Management) projec