A review of the offshore oil and gas safety indices

Safety Science 109 (2018) 344–352

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Safety Science journal homepage: www.elsevier.com/locate/safety

Review

A review of the offshore oil and gas safety indices a,⁎

a

Kuok Ho Daniel Tang , Siti Zawiah Md Dawal , Ezutah Udoncy Olugu a b

T b

Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Mechanical Engineering, Faculty of Engineering, Technology & Built Environment, UCSI University, Kuala Lumpur, Malaysia

A R T I C LE I N FO

A B S T R A C T

Keywords: Safety index Indicators Performance Offshore Oil and gas

Derivation of a performance index demonstrating integrated safety achievement of offshore oil and gas platforms has not been subject to extensive study. The indices proposed and adopted thus far are related to inherent safety and chemicals used in processes, with focus placed on the conceptual and design stages. Safety of offshore installations is a combination of asset integrity and personal safety, driven by organizational culture. Asset integrity covers process safety, structural integrity as well as aspects of safety climate dealing with personnel management such as training and competence. Indicators for various aspects of platform safety have been separately proposed in multiple studies. It would be significant to develop a composite index linking the major aspects of safety including the cultural and climatic factors to provide a more representative picture of platforms’ safety performance. This also facilitates performance benchmarking and continual improvement of safety management on the platforms. The adoption of leading indicators is crucial to drive and monitor inputs into the safety system. For the index to ultimately be meaningful, effective and easily understood, the underlying indicators should be specific, measurable, achievable, relevant, timely, evaluated and reviewed.

1. The definition of index An index is fundamentally a means to present a measure of interest numerically using relevant indicators. The measure of interest can be performance, productivity, risk-level or sentiment. (Färe et al., 2004). Indices have been used for a myriad of subjects, for instance, to compare sustainability of cities (Mori and Yamashita, 2015), stock performance (WSJ Market Data Group, 2017), health and safety performance (Tang et al., 2017), air diffusion performance (Liu et al., 2017), environmental performance (Hsu and Zomer, 2016) and myocardial performance (Olson et al., 2016), to name a few. With emergence of diverse indices measuring different subjects, many have argued whether the numbers presented by the indices carry much weight and significance (Jacobs et al., 2004; Saisana et al., 2005; Saltelli, 2007). A good index presents information efficiently, succinctly and meaningfully, thus enabling the audience to quickly get the necessary message for decision-making (Khan and Amyotte, 2004). An index is closely tied to the criteria or indicators constituting the index. In certain instances, an index only has one indicator such as the lodging index whose sole indicator is the average revenue per room-night (Wassenaar and Stafford, 1991). In other instances, an index is based upon multiple indicators, for example the DOSE index, which assesses the risk of chronic obstructive pulmonary disease, uses four criteria, i.e. dyspnea, airflow obstruction, smoking status and exacerbation

⁎

frequency. These indices are also known as composite indices (Jones et al., 2009). As indices are as good as the indicators adopted, the indicators are usually selected to fulfil the SMART criteria. SMART stands for specific, measurable, achievable, relevant and timely. Indicators should be specific in the domains measured or represented, measurable by permitting presentation of quantitative data, achievable in the sense that the data needed can be obtained via day-to-day operations, relevant where the indicators are realistic and related to the underlying aspects of interest, and timely to permit tracking of trends and quick response to deviations (Jacobs et al., 2004). The SMART criteria have been expanded to SMARTER with E symbolizing evaluated and R symbolizing reviewed. The additional criteria imply that index development and use should be a dynamic process with the underlying indicators constantly evaluated and reviewed for their ‘SMARTness’ (Yemm, 2013). Indicators have been popular in the field of safety especially in sectors with high risks. The reason is the need to capture data which reflects safety performance and allows preventive and corrective actions to be initiated (Øien et al, 2011). In safety, the most common indicators are fatality rates, injury rates as well as frequency of fire and explosion. These indicators paint an immediate picture of how a sector is performing in terms of safety (Vinnem, 2010; Reiman and Pietikäinen, 2012). Ironically, while the industry endeavors to uphold safety by preventing fatalities and injuries as far as reasonably

Corresponding author. E-mail addresses: [email protected] (K.H.D. Tang), [email protected] (S.Z. Md Dawal), [email protected] (E.U. Olugu).

https://doi.org/10.1016/j.ssci.2018.06.018 Received 26 February 2018; Received in revised form 20 May 2018; Accepted 20 June 2018 0925-7535/ © 2018 Elsevier Ltd. All rights reserved.

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et al., 2006). As indicators may require multi-dimensional data in diverse forms with different units, converting the data into a comparable form is crucial. This is achieved via normalization. The simplest method of normalization is ranking where performance over time is presented as relative positions or ranks (Nardo et al., 2005). Information and Communications Technology Index was normalized via ranking (Fagerberg, 2001). Safety scores in the oil and gas sector are often normalized by means of qualitative categorical scale. A typical example of qualitative categorical scale is the traffic light system with red indicating noncompliance or major failure, amber representing partial compliance or isolated failure, and green representing compliance (HSE, 2008). Though indicators of an index are, in many instances, assumed to have equal weights, in reality they are not. Assigning unique weights to indicators enables more important indicators to have greater influence on an index (Munda and Nardo, 2005). Weights can originate from survey of the indicators conducted among subject experts or the public, as well as statistically via principal component analysis based on existing data (Nardo et al., 2005). Having assigned weights to indicators, performance of the indicators needs to be brought together for index generation via aggregation. Selection of aggregation methods depends on the tolerance for compensability among indicators. Linear and geometric aggregations permit compensability while multi-criteria approach deters compensability. Non-compensability prevails in aggregation of indicators with highly dissimilar dimensions for instance sustainability indices combining the triple bottom lines where it is arguable whether increased economic performance can compensate for higher pollution (Munda and Nardo, 2005). After an index is developed, it should be tested for sensitivity and uncertainty as a means of continuous improvement. Uncertainty permeates all stages of index development and prompts a constant review of the indicators, methods of normalization, weighting and aggregation as well as quality of data collected (Saisana et al., 2005). Sensitivity analysis not only tests the robustness of an index via alteration of variables one at a time to examine their effects on the index, it also probes how uncertainties affect the index. Finally, a robust index has to be communicated in a meaningful way to the stakeholders, to serve its purpose. Visualization of the findings is worth a careful thought. Often, well-designed graph can convey the message more succinctly than numbers merely (Saltelli, 2007).

Fig. 1. Composite index development process. adapted from Nardo et al., 2005

practicable, these indicators are only made possible with the occurrences of incidents causing injuries and fatalities. Often, these indicators are called the lagging or reactive indicators due to the nature of information they present which comprises the safety outcomes (Vinnem, 2010). These indicators create an immediate sense of alertness and prompt actions to be taken to improve safety but they do not monitor the effectiveness and adequacy of actions taken. This leads to the rise of leading indicators for safety which drive or monitor the effort and inputs into a safety system (Lauder, 2012). Ideally, for safety, a composite index, if developed, should include both lagging and leading indicators with the SMART or SMARTER features to provide effective representation of a system’s state of safety. Having said that, it is worthwhile to look at how a composite index is developed.

3. Safety of the offshore oil and gas sector Conventionally the oil and gas sector has been regarded as a highrisk sector, particularly the offshore sector where workers face not only process hazards associated with the exploration, storage and processing of hydrocarbons on platforms but other forms of hazards related to the harsh working environment and transportation (Broni-Bediako and Amorin, 2010). It is generally agreed that there are two overarching domains governing the offshore safety, i.e. personal safety and process safety (Swuste et al., 2016). Personal safety deals with matters related to chemical and noise exposure, ergonomics, exposure to mechanical and electrical hazards to name a few, resulting in injuries and fatalities of workers (Mearns et al., 2003; Mearns and Hope, 2005). Process safety, however, concerns major hazards of the oil and gas installations particularly major spills, fire and explosion leading not only to injuries and fatalities, but property and environmental damage (Knegtering and Pasman, 2009; Swuste et al., 2016). The consequences of a process safety event are usually, more severe than those of a personal safety event, potentially involving multiple injuries and fatalities (Knegtering and Pasman, 2009). In many instances, the term ‘process safety’ is used interchangeably with ‘asset integrity’. Asset integrity aims to monitor whether an asset can perform to its desired function to safeguard safety, health and environment (HSE, 2008; Lauder, 2012), and comprises three main areas, i.e. structural, operational and technical (Frens and Berg, 2014). Asset

2. Method of index development Development of a composite index generally follows the sequence shown in Fig. 1. Theoretical framework involves determining the area or phenomenon to which an index applies, the domains or sub-domains governing the phenomenon, and the methods for development of the index (Nardo et al., 2005). Once the phenomenon prompting an index is determined, data selection follows during which indicators for measurement are selected based on SMART, i.e. specific, measurable, achievable, relevant and timely (Jacobs et al., 2004). Often, the indicators selected may require different presentation of existing data or collection of new data which could be expensive and impractical. Imputation of missing data accounts for such situations whereby the missing or expensive data are substituted with comparable ones (Royston, 2004). Multivariate analysis aims to establish the weights of and statistical correlations between the indicators. It enables clusters of inter-related indicators to be identified. This stage also examines internal consistency of indicators to minimize outliers. Common multivariate analysis comprises principal components analysis, Cronbach alpha and cluster analysis (Hair 345

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industrial major hazards (HSE, 2008). Multiple approaches have been employed to manage offshore safety consisting mainly of organizational and human factors (Mearns and Flin, 1999), safety culture (Rentsch, 1990), resilience engineering (Hollnagel et al., 2006) and risk-based approach (Hassan and Khan, 2012). Organizational and human factors focus primarily on leadership, management, organizational culture, ergonomics and individual psychosocial aspects (Mearns and Flin, 1999), while safety culture represents a large generalized area of whether safety is internalized as a core value which directs actions and behaviors (Rentsch, 1990). Resilience engineering centers on how well a system or organization maintains or recovers its stable state following an accident or operational distress, so that it can continue operations (Hollnagel et al., 2006). The risk-based approach upholds risk assessment where safety of an equipment or process is defined in terms of risk score. The risk score generated from failure probability and consequence serves as the basis of determining inspection intervals and risk mitigation (Hassan and Khan, 2012).

integrity management usually covers the entire lifecycle of an asset from design to decommissioning and includes management of processes, resources, people and system (Hassan and Khan, 2012). Though both aim to keep major accident hazards in check, they are fundamentally different. The Center for Chemical Process Safety (2011) defines process safety as ‘a disciplined framework for managing the integrity of operating systems and processes handling hazardous substances by applying good design principles, engineering and operating procedures’. The Energy Institute (2017) calls it a means of preventing major accidents involving fires, structural damage, explosion and uncontrolled release of hazardous substances due to loss of containment via engineering and management practices. Therefore, process safety primarily concerns the prevention of major accidents via operating systems and process integrity (Swuste et al., 2016; CCPS, 2011; Energy Institute, 2017). Asset integrity manages ‘asset’ which consists not only of systems and processes but resources and people. It is interesting to note that ensuring people are performing to their desired functions is also an aspect of asset integrity (Ciaraldi, 2005; HSE, 2008). The definition seems to suggest personal safety as part of asset integrity. This is also reflected in Ciaraldi’s (2005) concept of integrity management comprising process, plant and people. In contrast to the three elements, i.e. structural, technical and operational mentioned earlier (Frens and Berg, 2014), Hassan and Khan (2012) proposed asset integrity as an interplay of mechanical, operational and personnel integrity, where mechanical integrity resembles technical integrity. Personnel integrity in this context overlaps with the facets of personal safety dealing with ownership and accountability, competence and leadership to ensure operational integrity (Hassan and Khan, 2012). It is not entirely analogous to personal safety which also revolves around minimizing harms at personal level due to physical, chemical and biological hazards as well psychosocial hazards such as anxiety, hostility and aggression (Mearns and Hope, 2005; Ng et al., 2005). Nonetheless, both merge at optimizing work performance of personnel for the safety of a system. Considering the nuance between personnel integrity and personal safety, it is reasonable to assume that asset integrity management in practice does not cover the entire aspects of personal safety. Asset integrity management can be illustrated as Fig. 2. Though asset integrity has elements of personal safety, in the oil and gas sector, personal or occupational safety has conventionally been managed as a separate domain, focusing on matters related to occupational exposure, fatigue management, work arrangement, ergonomics, prevention of occupational illnesses as well as control of hazards causing injuries, near-misses and fatalities at personal level (Tang et al., 2017; Bergh et al., 2014; Flin et al., 1998). Asset integrity and process safety, on the other hand, aim to reduce, control and manage

4. Safety indicators of the offshore oil and gas sector Having known the major domains and approaches of offshore safety, it is reasonable to expect that safety indicators proposed for safety monitoring of offshore oil and gas platforms would revolve around these domains and approaches. A search through the literature indeed reveals a cornucopia of safety indicators ranging from operational, organizational, safety culture, technical, process and personal. The safety indicators can largely be categorized as shown in Table 1. Delving into the literature reviewed, a link between safety culture, safety climate as well as psychosocial and organizational factors can be traced. Eid et al. (2012) proposed that psychological capitals consisting of hope, resilience, optimism and self-efficacy could potentially affect organizational outcomes and individual work performance. Psychological factors are often associated with social factors as a person’s mental wellbeing and behaviors are invariably influenced by upbringing, social and environmental effects, hence the psychosocial factors (Leka and Jain, 2010). Psychosocial factors center around how work is designed, organized and managed in relation to the social and environmental context, as well as its implications on the psychology, thinking process, physiology and relationship of members of an organization (Clarke et al., 2011). Psychosocial factors are largely classified into 10 categories, i.e. job content, workload and work pace, work schedule, control, environment and equipment, organizational culture and function, interpersonal relationship at work, role in organization, career development as well as work-life balance (Moncada et al., 2014). Though safety culture and safety climate have been widely thought as similar, they are fundamentally different (Mearns and Flins, 1999). Safety climate deals with how employees perceive, thus behave towards safety and risks. Safety culture, however, probes the underlying norms, values and expectations of safety and risks. Safety culture is a more complex dimension than safety climate and is associated with social value and culture (Cox and Cheyne, 2000). Safety climate which is often manifested via safety management practices is ideally an expression of safety culture. In other words, safety culture represents the internal motivation and value of an individual or the norm of a society which drives the outward expression of safety climate. Safety climate, though is existed by safety culture, can also be engineered via leadership (Glendon and Stanton, 2000). This means unlike safety culture, safety climate may change in response to external factors. Safety culture has been perceived as a subset of organizational culture (Guldenmund, 2000). Organizational culture, on the other hand, reflects how the individuals constituting an organization commonly perceive policies, procedures and practices established in the organization (Rentsch, 1990). Like safety culture, organizational culture translates into organizational climate which drives the attitudes of members of an

Fig. 2. Elements of asset integrity management (Frens and Berg, 2014; Hassan and Khan, 2012). 346

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Table 1 Studies related to safety indicators for offshore oil and gas installations. Safety Domain

Citation

Overview

Safety Climate

Mearns et al., 2003

This study reveals how management practices and safety climate affect the numbers of official and selfreported accidents on 13 offshore oil and gas installations. It highlights the management practices and safety climate aspects that correlate with lower accident rates The study highlights the domains most used as a measure of safety climate in the UK industry, i.e. management, safe system and risk This study presents the findings of a safety climate survey involving 722 respondents working on the UK Continental Shelf. The survey encompassed domains such as procedural compliance, supervision, risk and safety management. The survey also revealed key safety management factors for a human factors program This study focuses on safety culture assessment of the offshore sector via a combination of methods comprising focus group, questionnaires, audits and observations of behavior This study develops a psychosocial risk indicator for the oil and gas sector in light of the implications psychosocial risks have on health and safety of offshore employees. It also tests the indicator’s applicability by integrating it with the existing safety management system This study recommends exposure indicators for occupational exposure of oil and gas workers to drilling fluid This study examines the change in organizational factors such as work conditions, commitment to and involvement in safety and workload between 1990 and 1994, on offshore oil platforms in the North Sea This study establishes an organizational factor framework consisting of a qualitative organizational model, organizational risk indicators and methods of quantitatively assessing the impact of organizational risk This paper introduces the development of resilience based early warning indicator (REWI) which is founded upon theory of resilience engineering This paper recommends major hazards risk indicators for offshore oil and gas platforms with inclusion of leading indicators and application of the indicators in individual companies This paper presents a method to evaluate the risk levels of Norwegian offshore oil and gas platforms using indicators such as number of incidents and near-misses, and performance of barriers in combination with risk assessment findings and questionnaire surveys to assess safety culture, perceived risk and communication This paper reviews the historical development, definition, the leading and lagging aspects, as well as the theories behind process safety indicators The forum presents process safety of different countries with existing lagging incident indicators comprising fatalities, injuries, number of gas release, collisions and loss of well control, without proposing new indicators or new methods of presenting process safety data The guideline proposes lagging and leading metrics for process safety but does not include aspects of personal safety and structural integrity. Process safety culture metrics are not included but reference is made to another tool The ANSI/API RP 754 proposes tier-based lagging and leading process safety indicators which are similar to those of the CCPS. Nonetheless, recommended practice does not include an illustration on severityweighted metric as in the CCPS guideline This study proposes an asset integrity index to assess and monitor the three domains of asset integrity of process facilities, i.e. process, mechanical and personnel. The index is based on leading and lagging indicators and is presented in the form of risk metric This paper presents a list of performance indicators commonly used to monitor major hazards in the UK offshore petroleum sector and provides a description of the KPI (key performance indicator) scheme in terms of its components, effectiveness and shortcomings This study proposes asset integrity performance indicators for offshore loading facilities in effort to identify shortfalls of industrial practices This study aims to establish safety indicators for prevention of offshore drilling blowouts This study proposes the use of early warning indicators developed from accident investigation such as Eirik Raude hydraulic oil leak in 2005 to minimize hydrocarbon spill during oil and gas production This study puts forth a method to develop risk indicators for operational risk control at offshore oil and gas platforms, using quantitative risk analysis The author reports the development of a set of risk-monitoring indicators for offshore petroleum platforms initiated in 1992 during the transition between the shutdown of a platform and construction of new platforms in a North Sea oil field The authors illustrate on the ‘Risk Indicator Project’ jointly undertaken by SINTEF and the Norwegian Petroleum Directorate which yielded a risk indicator list to monitor significant risk influencing factors affecting the fatal accident rate The authors report on the Frigg Safety Case in 1995 which yielded six technical indicators to keep safety influencing factors in check. 11 indicators were subsequently added based on a study of the quantitative risk analysis (QRA) and experts’ evaluation The authors review the use of structural vibration response to identify and characterize structural damage for offshore platforms This study proposes evaluation of structural frequencies as a structural damage indicator of offshore platforms This study identifies a list of indicators grouped under 14 safety factors for comprehensive safety performance measurement of offshore oil and gas installations and probes the correlations between the safety factors based on a survey’s findings The report presents the trends in personal, process and transportation safety of the petroleum sector on the Norwegian Continental Shelf using a set of activity and incident indicators primarily and a risk indicator reflecting the weighted risk of loss of life normalized against working hours. The process safety aspect of the report is hardware-oriented, highlighting the failure of the safety critical elements or barriers of offshore installations typically used in facility status reporting. The report also demonstrates exceptional initiatives of reporting leading performances in terms of preventive and corrective maintenance backlogs, modifications and planned shutdown

Flin et al., 2000 Flin et al., 1998

Safety Culture

Cox & Cheyne, 2000

Psychosocial Risk

Bergh et al., 2014

Occupational Hygiene

Broni-Bediako & Amorin, 2010

Organizational Factor/Risks

Rundmo et al., 1998 Øien, 2001a

Resilience Engineering

Øien et al., 2010

Major hazards risk

Vinnem, 2010 Vinnem et al., 2006

Process safety

Swuste et al., 2016 International Regulators’ Forum (IRF), 2016 Center for Chemical Process Safety (CCPS), 2011 ANSI/API, 2016

Asset integrity

Hassan & Khan, 2012

Lauder, 2012

Hassan & Abu Husain, 2013 Offshore drilling blowouts Spill prevention

Skogdalen et al., 2011 Øien, 2008

Risk indicator

Øien, 2001b Undheim, 1999

Øien and Sklet, 1999

Technical indicator

Øien et al., 2011

Structural integrity

Doebling et al., 1996 Betti et al., 2015

Integrative safety

Tang et al., 2017

Petroleum Safety Authority Norway, 2016

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individuals with higher psychosocial risk scores are at risk of stress, desolation, depression, etc. Indicators of organizational factors (Rundmo et al., 1998; Øien, 2001a) overlap with those of safety climate and only a few organizational factors which have been shown in previous studies to affect safety were studied (Mearns and Flin, 1999; Mearns et al., 2003). In view of this, an integrative approach is promulgated in the study of culture and climate covering a wider range of organizational aspects. Where SMART is concerned, cultural and climatic indicators may face difficulty of being measurable and achievable where additional effort may be required to gather the necessary data. Other indicators such as those related to resilience engineering, major hazard risk, process safety, drilling blowouts, spill prevention and technical can be linked to the overarching domain of asset integrity management. As explained in Section 3, the ultimate aim of process safety and asset integrity management is to keep major hazards or major accidents risk in check. Major accidents comprise drilling blowouts, major spillage resulted from loss of containment of energy or hazardous materials, fire, explosion and structural failure (Energy Institute, 2017). Resilience engineering is an important facet of asset integrity which ensures the ability of a system or asset to recover and continue operations after major accidents or distress (Øien et al., 2010). If a major accident or stressful occurrence is viewed as a shift from the equilibrium, resilience engineering then imparts a system the ability to regain equilibrium, hence functionality. The focus of resilience engineering is not on the side of prevention but on recovering ability which also constitutes the integrity of a system or asset. Nonetheless, an obvious shortcoming of the literature related to development of asset integrity indicators is that they are fragmented, covering either the operational, technical or the structural aspects. Studies that combine the major aspects of asset integrity often involve only limited indicators (HSE, 2008; Hassan and Khan, 2012; Lauder, 2012). It is also common for process safety indicators to focus only on the operational and technical integrity without due attention on structural integrity (CCPS, 2011; ANSI/ API, 2016; Swuste et al., 2016). Few instances of process safety reporting is limited to the lagging performance (IRF, 2016). Personnel integrity presents an area of asset integrity that is understudied. Part of the reason is that personnel integrity is not well-defined and is not a term commonly used. It only appears in the study of Hassan and Khan (2012) covering aspects such as training, competence, communication, permit-to-work and safety culture. The inclusion of safety culture as a subset of personnel integrity seems oversimplified as safety culture itself represents a large domain of safety. The indicators used for personnel integrity in the study do point to an initiative to link asset integrity to the underlying cultural and climatic factors. As asset integrity management is an integral part of offshore safety management, the indicators proposed tend to be able to fulfil the SMART criteria compared to those of the culture and climate. With that said, not all asset integrity indicators proposed are in measurable form and indicators can be mistaken as factors, for example in proposing safety indicators for blowouts prevention, indicators of operational aspects such as work practice, competence and management, as well as technical indicators such as pipe and casing handling, cementing and well-monitoring have been proposed (Skogdalen et al., 2011). These indicators are more appropriately considered as factors because they are not presented in measurable forms. Though asset integrity seems to encompass all aspects of safety, the current practice has the tendency to focus on the hardware system of the platforms. Also, personal safety is not entirely captured in asset integrity management, not even in personnel integrity which focuses on competence building and organizational factors. Prior to looking at the importance of personal safety in an inclusive safety management system, the studies related to offshore safety indicators have shed light into the connections between the overarching asset integrity management aiming to prevent fatalities, injuries and damages as well as the underlying cultural and climatic aspects

organization. Safety culture can be perceived as a component of organizational culture connected to safety. By the same token, safety climate can be considered a subset of organizational climate (Mearns and Flin, 1999). While organizational culture may embed safety culture, safety culture cannot be assumed as a matter-of-fact of organizational culture because not all organizations view safety as a vital element in their businesses (Choudhry et al., 2007). Fundamentally, culture is the driving force behind climate with the former governing the values, norms and expectations of members of a society which would affect their perceptions, attitudes and intentions – the three main aspects of climate (Flin et al., 2000; Mearns et al., 2003). Culture is, in turn shaped by psychological and social factors collectively known as the psychosocial factors. Culture and climate are only of concern in a system where humans are involved. In a purely technical system comprising only machines, these aspects can be downplayed. Therefore, an organization or a workplace is often likened as a sociotechnical system characterized by interactions between humans as well as between humans and machines (Fox, 1995). The relationship of psychosocial factors, culture and climate in a socio-technical system is shown in Fig. 3.

Fig. 3. The relationship between culture and climate.

With the link between psychosocial factors, culture and climate elucidated, it points to a lack of attempt in the literature to combine these aspects and produce an integrative approach which can capture the essential features of psychosocial factors, culture and climate. The indicators of safety climate (Mearns et al., 2003; Flin et al., 1998; Flin et al., 2000), on their own are limited in the sense that they explore limited aspects of safety-shaping organizational factors and do not facilitate further analyses on how these outward manifestations of safety are shaped by the underlying culture. In other studies of safety climate (Neal and Griffin, 2004; Dejoy et al., 2017), the distinctions between culture and climate are blurred. The study of Cox and Cheyne (2000) on safety culture focuses on the means of assessing safety culture without defining the aspects of safety culture to be assessed. As psychosocial factors cover a large facet from personal dimension such as workload and work-life balance to organizational dimension such as role clarity and job content (Moncada et al., 2014), it should ideally be approached in a more systematic way. Current psychosocial indicators studies have looked into limited aspects of psychosocial factors particularly role clarity, relationships, job control and support (Bergh et al., 2014). Besides, the wide definition of psychosocial factors seems to give rise to difficulty in locating where it fits in a sociotechnical system. While there are aspects of psychosocial factors that relate to culture, there are also aspects that relate to climate particularly work schedule, control and career development (Moncada et al., 2014). It is questionable whether psychosocial factors need to be singled out from the cultural and climatic factors. Perhaps a risk-based approach to psychosocial indicators would make more sense by generating a psychosocial risk score based on the cultural and climatic factors and associating the risk score to personal safety (Bergh et al., 2014). This implies that 348

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ultimately cause the top event of hydrocarbon leaks. Personal safety, also referred to as occupational safety, is the area of safety that targets at safeguarding the safety and health of workers (Mearns and Hope, 2005). An area of personal safety is occupational hygiene which concerns exposure of workers to chemicals, noise and vibration (Broni-Bediako and Amorin, 2010). Though perceived as a separate domain of safety, it is by no means, an isolated domain. Personal safety and asset integrity merge at the prevention of injuries and fatalities. Personal safety is also driven by cultural and climatic factors (Manuele, 2009). Psychosocial risks are in fact more related to personal well-being. An explanation for the overlapping of psychosocial factors with safety culture and safety climate is that an organization is made up of individuals whose well-being are influenced by their own mental states and cognition as well as their interactions with members of the organization (Eid et al., 2012). On a broader sense, an individual’s wellbeing is also influenced by interactions with members of the society. It is therefore promulgated that safety performance measurement should include asset integrity management, personal safety as well as the cultural and climatic aspects. In relation to this, Tang et al. (2017) has proposed an integrative safety performance measurement framework for offshore oil and gas platforms which combines a list of asset integrity, safety culture and personal safety indicators. However, the framework does not include structural integrity and has only limited indicators of management and work engagement to capture the measurable aspect of safety climate. The framework seems to downplay other obvious organizational factors. The Petroleum Safety Authority Norway's (2016) report on the personal and process safety trends of the petroleum activity uses incident indicators predominantly, which are predominantly lagging in nature, though stressing on the importance of leading indicators. The report uses relative risk metrics which demonstrate the likelihood and severity of accidents resulted from incidents or safety events at specific facilities. The event risk assessment is also lagging in nature and is only a facet of the risk-based indicators mentioned in Table 1 (Øien and Sklet, 1999; Undheim, 1999; Øien, 2001b) which measures the risk influencing factors. The risk influencing factors are risk-affecting elements of a system or an operation which are not only limited to event. The factors can also be conditions and can encompass the organizational aspect. The report, therefore, seems to downplay the cultural and climatic aspects of safety. Nonetheless, the report includes performances of the safety critical elements forming the barriers of the offshore installations. Though hardware-oriented and inclined to draw focus on failure of the barriers, the barriers are themselves measures to prevent the escalation of major accidents and the resulting damages, injuries and fatalities. The approach can be considered leading in nature and is akin to facility status reporting in asset integrity management of offshore facilities currently practiced by some oil and gas companies (Frens and Berg, 2014). The most prevalent leading indicators adopted in the report is the monitoring of preventive and corrective maintenance and the backlogs of such maintenance, as well as the amount of time spent for modifications and planned shutdown. Maintenance management and backlogs are also suggested by HSE (2008) as a reporting element in its asset indicator key programme. There is potential for leading performance measurement to be expanded beyond maintenance, to aspects such as documentation, risk assessment, competence and management of change (CCPS, 2011; Hassan and Khan, 2012; ANSI/ API, 2016). Generally, to some extent, the evaluated and reviewed features of SMARTER are demonstrated in the continuous studies to refine and delineate indicators monitoring various aspects of offshore safety. Review itself is a process that must be undertaken in scientific research to develop and propose safety indicators.

Fig. 4. The influence of organizational culture on asset integrity.

Fig. 5. The reasons for unreliable asset performance (Montgomery and Serratella, 2002).

encompassing the organizational factors and affecting the psychosocial risks. The connections are elucidated in Fig. 4 which bears resemblance to the unreliable asset performance model proposed by Montgomery and Serratella (2002) in Fig. 5. The unreliable asset performance model suggests that weaknesses in asset integrity management, particularly a lack of sustainability approach and sound decision-making are caused by cultural issues. These weaknesses are based upon climatic factors of leadership and decision-making (Dejoy et al., 2017). The weaknesses result in equipment failures and human errors which give rise to the risks of catastrophic incidents. Such risks ultimately escalate into chronic and sporadic incidents. This is akin to Fig. 4 where asset integrity is driven by organizational culture for identification, assessment and control of major accident hazards, as well as leadership and capacity building. The models are supported by the findings of Vinnem and Røed (2014) that the root causes of hydrocarbon leaks on the Norwegian Continental Shelf could be traced to human and organizational factors. These underlying cultural and climatic weaknesses result in human error and failure in verification of critical activities which

5. Safety-related indices of the offshore oil and gas sector The literature review in Section 4 includes studies of offshore safety 349

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streams are related in the sense that the former is affected by density, pressure, energy and combustibility of the latter (Shariff et al., 2012). Some indices are concerned primarily with the properties of chemicals and are particularly useful for research or at early stages of process designs. The Environmental Health and Safety (EHS) Index for instance correlates with the degree of hazard a chemical poses on the environment as well as health and safety of users (Färe et al., 2004). The Runaway Reaction Index, on the other hand, indicates how likely a runaway reaction can occur by referring to energy factors affecting heat of reactions (Vílchez and Casal, 1991). Emergence of fuzzy logic has also led to integration of few indices with the tool. A typical example is the Fuzzy Inherent Safety Index (Gentile et al., 2003). The index discussed thus far are process safety indices measuring the inherent safety as well as toxicity. The EHS is chemical-specific. The PRI, however, considers the combustibility of each chemical in a mixture. These indices are generally used to rank the hazard or risk related to processes which are not limited to those of the oil and gas sector. Their roles are confined primarily to the conceptual and design stages, in contrast to the indicators reviewed in the previous section with primary focus on operational performance of oil and gas installations. Despite the indices above, literature review has revealed limited effort to develop performance-based safety index for oil and gas installations which examines operational performance of the facilities. Multiple indicators have been proposed to measure and monitor various organizational, occupational and process facets of oil and gas operations but no index endeavors to bring all the operational facets together for performance measurement. A possible reason could be the practicability of having an overarching index which elicits the overall safety performance of an oil and gas platform as the operations thereon are usually complex (Cox and Cheyne, 2000; Vinnem et al., 2006). The oil and gas sector has conventionally used incident rates, including fatality and injury rates as indication of safety performance in safety reporting (Mearns et al., 2003). Though there have been opinions against the use of these lagging indicators as sole safety reporting indicators, these indicators are straightforward in presentation of crucial safety data and easy to understand (Payne et al., 2009). Calculations of the rates are well established and collections of data for the calculations are uncomplicated. Nonetheless, the indicators give a simplistic view of safety performance without making a relation to how well safety measures are executed (Manuele, 2009; Payne et al., 2009). Reiman and Pietikäinen (2012) promulgated the use of a combination of outcome, monitor and drive indicators to manage safety performance of a system posing critical risks where monitor and drive indicators are leading indicators to reflect the system’s well-being and drive safe behaviors and safety activities respectively. Leading indicators, albeit used particularly in relation to preventive maintenance, risk assessment, documentation and competence-building, are not usually reported in safety performance reporting of oil and gas platforms (Manuele, 2009; Reiman and Pietikäinen, 2012).

indicators which may or may not include a scoring framework using the indicators to generate indices. Hassan and Khan (2012) for instance, proposed a risk-based asset integrity index while Bergh et al. (2014) developed a psychosocial risk indicator framework. Other studies reviewed ended at different stages of indicators development with some identifying only the factors while others coming up with indicators or metrics. The report of Petroleum Safety Authority Norway (2016) presents the regional petroleum safety trends using a list of lagging personal, process and transportation safety indicators, as well as barrier-based indicators and leading indicators monitoring maintenance but does not propose a framework for generation of safety scores or indices based on the major domains of process and personal safety. A literature search of the offshore oil and gas safety indices reveals process safety indices which are not specific to the offshore sector but are used widely in the petrochemical sector. The review shows inherent safety index which selectively channels information yielded by process design simulator to measure inherent safety level in order that inherent safety principles can be employed effectively (Leong and Shariff, 2008). The index is named inherent safety index module (ISIM). Other indices of inherent safety have also been proposed but, unlike ISIM, they lack integration with process modelling software. Rathnayaka et al. (2014) developed the Risk-based Inherent Safety Index (RISI) combining two components, i.e. design risk and inherent safety risk, which allows international system-based units to be used in generation of index values. The RISI evolved from the Integrated Inherent Safety Index (I2SI) (Khan and Amyotte, 2004). While I2SI focuses on reducing hazard, RISI aims to reduce risk via application of inherent safety design principles throughout all stages of design life cycle. Toxicity hazard indices have also been established, the most popular ones being the Dow Fire and Explosion Index and the Mond Index (Khan et al., 2003). The Dow Index shows relative risks of fire and explosion resulted from a process. Higher index value indicates higher risks of fire and explosion, hence the hazards associated with a process. The index only measures risk of main process units, not the auxiliaries like utilities and storage facilities (Khan et al., 2003; Swuste et al., 2016). The Mond Index, on the other hand, is an improvisation of the Dow Index to cater for main processes as well as storage facilities, in addition to explosive chemicals (Khan et al., 2003). Besides, the Dow Chemical Exposure Index is employed in calculation of risks associated with acute toxicity, often during process hazard analysis (Khan et al., 2001). Unlike the other toxicity hazard indices which indicate risk levels, the Mortality Index demonstrates how lethal a hazardous material is by computing fatalities related to exposure to a fixed amount of the material (Khan et al., 2003; Rahman et al., 2005). The Safety Weighted Hazard Index (SWeHI) appeared in the literature search of safety index as areal representation of damage in relation to safety measures taken. A higher SWeHI implies a larger damage potential, hence higher hazard or vulnerability of a facility (Khan et al., 2001). Another index called the Hazard Identification and Ranking Index (HIRA) was proposed by Khan and Abbasi (1998) to augment the Dow Fire & Explosion Index which relies heavily on expert opinion instead of system properties. The HIRA combines expert opinions and system properties to provide damage radius resulted from system failure, as well as risk ranking. The Process Route Index (PRI) utilizes software simulations to generate risk calculations for different routes of synthesis. The calculation is based on level of explosiveness associated with a process route, which is affected by how combustible the chemical or chemical mixture in the route is (Leong and Shariff, 2009). The PRI is an extension of the ISIM, which takes into consideration the influence of process parameters such as pressure and temperature on combustibility of chemicals. This consideration is absent in the ISIM and many other inherent safety indices (Leong and Shariff, 2008; Leong and Shariff, 2009). An index closely related to the PRI is the Process Stream Index (PSI). The PSI highlights the process streams instead of process routes with potential for inherent safety improvement. Process routes and process

6. Recommendations for offshore safety indices Looking beyond the petrochemical sector, index has been devised to compare road safety performance of countries by integrating road safety data such as fatalities due to road accidents in every million citizens (Hermans et al., 2008). Quantitative performance-based index has also been observed in the airline sector to compare how different airline companies perform in terms of risk and safety competitiveness. Index value generation was facilitated by the use of fuzzy multi-attribute decision making (Chang and Yeh, 2004). The airline index has been refined by taking into consideration the correlations between the safety factors in weight determination (Liou et al., 2007). Casanovas et al. (2013) devised the Occupational Risk Index (ORI) for construction activities. The index assesses the design of a project and construction process by assigning weights to the process or equipment used based on the risks and likelihood of accidents resulting 350

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introducing leading aspects into the reporting practice is worth looking into to enable more effective performance benchmarking and continual improvement of safety management. The use of index also confers the flexibility of safeguarding data which is deemed confidential and allows the data to be presented in a different format required by the index.

from the process or equipment. While the index could be used for construction of an offshore installation, its applicability may be limited for operation of the installations. Mohamed (1999) developed a safety management index to evaluate contractors in a study related to safety performance in Australia. Another attempt to develop safety management index was made by Fang et al. (2004) and this index aimed to evaluate management of construction sites instead of contractor’s performance. Safety indices are not new to the nuclear sector and the indices commonly used are fuel reliability index, tightness index and recurrent failure index (Øien, 2001b). Karagiannis et al. (2010) proposed a risk-based approach to evaluate performance of industrial emergency plans in general. Though focusing on the specific area of emergency management, the study unveils a framework which translates scores of performance indicators into probability of failure such as inappropriate training. The probability of failure is subsequently combined with severity of failure to yield the risk index. The index relevant to offshore platforms is the icing index by Barabadi et al. (2016) based on how different ways of ice formation affect performance of offshore production facilities in the Arctic. The index evaluates the dependability and sustainability of the platforms without looking at safety in specific. In view of a lack of safety index for offshore oil and gas platforms, it is worthwhile to develop an index which facilitates performance comparison and benchmarking across the platforms. Based on the review in Section 4, an integrative approach is recommended for the safety index development combining the findings from various niches of study in offshore safety indicators development. The index should adopt SMARTER indicators which cover the cultural and climatic factors as well as major safety domains encompassing asset integrity and personal safety. The index can either be performancebased evaluating how well a platform performs in each safety indicator in relation to the performance targets and standards, or risk-based giving rise to aggregated risk scores or indices for the indicators of interest. Otherwise, the framework combining performance indicators and risk-based approach proposed by Karagiannis et al. (2010) can be adopted. However, the current hardware-focused system of asset integrity management may give rise to a large amount of data which need to be aggregated for risk index generation (Frens and Berg, 2014). Structural integrity also presents a wide scope covering the sub-sea and topside structures (Potty and Mohd. Akram, 2009). In view of this, a performance-based approach examining how the safety critical elements consisting of hardware and structures perform collectively could be considered. Ideally, both leading and lagging indicators are included in the index. The method of index development in Section 2 can be referred. Of late, safety indicators and indices have been linked to the fuzzy logic. With the use of the fuzzy inference system, the rules and expert’s experience can be captured in generation of crisp outputs which can be used as safety indices. As different indicators may have different importance to the safety system, weights can be assigned to the indicators via the method outlined in Section 2 prior to aggregation to yield a composite index.

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7. Conclusion The use of composite index has permeated the industrial and nonindustrial sectors encompassing different aspects such as performance, risk level and resilience. A search of the literature has revealed very limited attempt to develop a comprehensive safety performance index for the offshore oil and gas sector despite the availability of indicators for instance the lagging and leading indicators proposed by ANSI/ API (2016), CCPS (2011) and Petronas Safety Authority Norway (2016), and availability of frameworks for instance as proposed by Tang et al. (2017). This review highlights the potential and need for development of a composite safety index by utilizing the existing safety framework for offshore processes or aggregating the indicators available. As safety reporting currently has relied predominantly on lagging indicators, 351

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