Assessment of risk tolerance for future autonomous offshore installations

The future normally unmanned production concepts for offshore petroleum installations in the North Sea and associated waters are briefly outlined. A case study is summarised for a fictitious normally unmanned facility, and risk results are presented for three different cases with varying extents of safety systems. The case study results are discussed with respect to the risk levels for personnel for the three cases. Risk tolerance criteria are discussed in general and for unmanned production installations in particular. Also risk reducing evaluations are discussed briefly, in general as well as for unmanned production concepts. Individual and societal risk are discussed, together with some regulatory challenges from the risk management point of view for normally unmanned installations. There are no applicable risk tolerance criteria for unmanned facilities, and the criteria for manned facilities are not suitable. There is a strong need for the authorities to focus on the use of risk tolerance criteria for manned as well as unmanned facilities.acceptedVersion© 2020. This is the authors’ accepted and refereed manuscript to the article. Locked until 27 October 2023 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Building safety in the offshore petroleum industry: Development of risk-based major hazard risk indicators at a national level

There has been an important controversy over whether the series of major accidents at Chinese Bohai Bay in 2011, i.e. the Penglai 19-3 and Suizhong 36-1 oil spills, are a sign of systematic safety problems in the Chinese offshore petroleum industry or a casual result of fortuities. It is hard to obtain the answer unless the national risk level of the offshore petroleum industry is monitored and measured. This paper describes an effort to propose and discuss an analytical approach for the development of major hazard risk indicators that can be used for monitoring, measuring and predicting national risk levels in the offshore petroleum industry. This study focuses on major hazards on offshore installations, hence personal safety hazards that affect individuals are not covered. Firstly, a risk-based approach for developing major hazard risk indicators on offshore installations is developed. Both leading and lagging major hazard risk indicators on offshore installations are suggested. After that, the proposed analytical approach is tested by the risk assessment results of the Norwegian Continental Shelf (NCS) in the latest ten years (2007–2017). This is followed by a discussion on suitability and challenges of the proposed risk-based approach. It has been demonstrated that the results of this study can provide a realistic and jointly agreed major hazard risk picture in the offshore petroleum industry.acceptedVersion© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 12.6.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Towards an online risk model for dynamic positioning operations

Automation and increasing complexity mean that operators have to handle data and alarms and emergent decisions under the pressure of unexpected and rapidly changing hazardous situations. Position loss during marine operations may lead to serious accidents, such as collision, loss of well integrity, etc. An online risk model aims at assisting operators in dynamic positioning operations to successfully recover the vessel’s position in a good timing. The objective of this paper is to identify generic scenarios of position loss during operational phase and the information that is needed for successful recovery action. The results show that position loss normally involves of complex human machine interactions, generally in two patterns. Based on the findings, it has been recognized that risk model considering time aspect is of vital importance to develop an online risk model for DP operations.publishedVersionPublished by Taylor & Francis. Made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

An allision risk model for passing vessels and offshore oil and gas installations on the Norwegian Continental Shelf

This article presents a new risk model for estimating the probability of allision risk (the impact between a ship under way and a stationary installation) from passing vessels on the Norwegian Continental Shelf (NCS). Offshore petroleum operators on the NCS are required by the Norwegian Petroleum Safety Authority (PSA) to perform risk assessments to estimate the probability of impacts between ships and offshore installations, both for field related and passing (merchant) vessels. This has typically been done using the aging industry standard COLLIDE risk model, but this article presents a new risk model based on a Bayesian Belief Network (BBN) that can replace the old COLLIDE model for passing vessels. The new risk model incorporates a wider range of risk influencing factors (RIFs) and enables a holistic and detailed analysis of risk factors, barrier elements and dependencies. Even though the risk of allision with passing vessels is very small, the potential consequences can be critical. The new risk model is more transparent and provides a better understanding of the mechanisms behind allision risk calculations. The results from the new model are aligned with industry expectations, indicating an overall satisfactory performance. The article discusses several key elements, such as the use of expert judgement to estimate RIFs when no empirical data is available, model sensitivity, and a comparative assessment of the new risk model to the old COLLIDE model.acceptedVersion© 2020. This is the authors' accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The final authenticated version is available online at: http://dx.doi.org/10.1177/1748006X2095748

Challenges with bringing collision risk models used in the north sea and norwegian sea to the Barents Sea

Norwegian offshore oil and gas exploration and production is moving into new and previously unexplored areas further north, such as the Barents Sea. This poses multiple new challenges in design, development and operation. Some challenges are due to the harsh arctic climate and conditions, others because this area is remote and undeveloped compared to the offshore fields further south. The distance between installations will be much greater and the overall infrastructure is far less developed. The industry standard risk model for ship-installation impacts (COLLIDE) relies heavily on operational elements and risk reducing measures commonly found throughout the North Sea and Norwegian Sea. These are presently not available in the Barents Sea. This implies that risk assessments will have to adapt the current methodology to account for the lack of data, quality of available data, and overall “modus operandi” as offshore operations in the Barents Sea may prove to be different from comparable operations in more established areas of the Norwegian Continental Shelf. The main operational barrier and risk reducing measure against ship-installation impacts in Southern Norway are two control centers that operates an integrated network of radars, VHF stations and AIS base stations. The same level of emergency resources, surveillance and communication coverage is not available in the Barents Sea. The lack of infrastructure and available resources affect core assumptions in collision risk assessment models and may cause otherwise minor incidents to have a significantly more serious outcome in such a remote area. This paper discusses the main challenges of using existing risk assessment tools and models in an area with significantly less infrastructure and available resources, and proposes alternatives to overcome these issues.acceptedVersion© 2015 Port and Ocean Engineering under Arctic Conditions. Available at http://www.poac.com/Papers/2015/author_index.ht

Supervised dynamic probabilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results

One challenge that has received attention in maritime industry is assessing the risk level of dynamic positioning (DP) systems in emergency situations. Statistics from recent years have shown that the risk level of some DP operations is above the industry's risk criteria. Operators have a significant impact on incidents’ consequences by making responsive decisions. In emergencies, one is afforded little time to make a decision. Available risk models are not efficient enough to provide systems’ risk level in a short period of time. In this study, the application of a new supervised methodology to assist decision making in emergencies is proposed. This method significantly reduces the processing and execution time of a system's probabilistic risk assessment models. In this methodology, the most probable failure scenarios are generated using an optimization model. The objective of the optimization model in this study is to find scenarios with the highest occurrence probabilities. The constraints are a system's dynamic simulation and its risk model. The proposed method is applied to three incidents that occurred in the Norwegian offshore sector in previous years. The results show that the model can predict the most probable scenarios with an acceptable accuracy in a very short time.publishedVersio

Towards supervisory risk control of autonomous ships

The objective of this paper is to outline a framework for online risk modelling for autonomous ships. There is a clear trend towards increased autonomy and intelligence in ships because it enables new functionality, as well as safer and more cost-efficient operations. Nevertheless, emerging risks are involved, related to lack of knowledge and operational experience with the autonomous systems, the dependency on complex software-based control systems, as well as a limited ability to verify the safe performance of such systems. The framework presented in the paper is the first step towards supervisory risk control, i.e., developing control systems for autonomous systems with risk management capabilities to improve the decision-making and intelligence of such systems. The framework consists of two main phases, (i) hazard identification and analysis through the systems theoretic process analysis (STPA), and (ii) generating risk models represented by Bayesian Belief Networks (BBN) based on the outcomes of the STPA. The application in the paper is aimed at autonomous ships, but the results of the paper have a general relevance for both manned and unmanned systems with different levels of autonomy, complexity, and major hazard potential.acceptedVersion"© 2020. This is the authors’ accepted and refereed manuscript to the article. Locked until 11.11.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Risk-based decision-making support model for offshore dynamic positioning operations

A Dynamic Positioning (DP) system enables vessels and rigs to accurately maintain a predetermined position and heading or track. It enables precise operations under harsh environmental conditions. DP is used for a variety of purposes; however, the role of the DP operator (DPO) is considered the same regardless of type of operation: to monitor and keep the vessel in position. Some of the decisions that the DPO makes are safety critical, for example, decisions about the set-up of the system can prevent the vessel from colliding with an offshore oil and gas platform. Applied cognitive task analysis (ACTA) is performed to analyze how the different operational settings influence the role and decision-making of the DPO. Two DPOs with experience from five different operation types were interviewed. The results from the ACTA for the different operation types are compared with respect to technical steps, cues, the cognitive steps and components, actions, and decisions. The contextual factors are evaluated using an adapted version of Rasmussen’s dynamic safety model. The results of the comparison are used to evaluate the current role of the DPO, in light of the DP system and different DP operations. Recommendations for the improvement of safety, the design of the DP system, training and set-up of DP operations are formulated.publishedVersio

Human Reliability and the Impact of Control Function Allocation in the Design of Dynamic Positioning Systems

The design and function allocation of control in complex technological systems have mainly been technology-driven, resulting in increased automation. A human or user perspective is rarely taken in the technological development. The pertaining attitude seems to be that increased automation will reduce the occurrence of human error and thereby ensure safer design and operation. Increased levels of automation, however, may come with a cost of reduced situation awareness for the human operator. This is also the case in the design of the dynamic positioning (DP) system for vessels. Accident statistics show that the frequency of collisions in certain DP operations is above the acceptance criteria and that a combination of technical and human failures were the main causes in nearly all accidents. This article underlines the importance of considering the role of the human operator and human reliability in the design and operation of DP systems. It presents a functional model of the DP system, and discusses current function allocation of control and its impact on operators’ situation awareness and performance. This article concludes with recommendations regarding function allocation of control and visualization of operational risk to enhance operator performance and reliability.acceptedVersion© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 20.12.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Part 1: General Overview

Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate risks associated with complex systems. However, current approaches to construct and execute DPRA models are challenged by high execution time owing to numerous possible scenarios. This issue will affect the execution time of the model, which is in contrast with the aim of modeling. DPRA models must be sufficiently fast to assist decision-making processes in the required time. In this study, a new method is proposed to enhance the execution times of DPRA models. This method uses optimization algorithms to determine failure scenarios and sort scenarios based on their occurrence probabilities. The most efficient optimization algorithms, considering the nature of the DPRA models, are mixed-integer sequential quadratic programming, modified branch-and-bound algorithm, and modified particle swarm optimization, which are then compared and discussed. To validate the effectiveness of this method, a simple case study is presented. The results show the effectiveness of the method, which has high accuracy and reduces the execution time significantly (e.g. execution time of risk assessment of 16,464 possible behavior scenarios after an incident in a dynamic positioning system is one fifth of the conventional methods). A detailed supervised DPRA model of dynamic positioning systems and its application on three incidents that occurred in the Norwegian offshore sector in previous years is presented in a subsequent article (Part 2 of 1) (Parhizkar et. al.). Case study results confirm that the supervised DPRA method can be applied to other complex systems so that the dynamic probabilistic risk values can be evaluated quickly and accurately.publishedVersio