Parametric Generation of Explosion Scenarios for Quantitative Risk Assessment of Gas Explosion in Offshore Plants

Parametric Generation of Explosion Scenarios for Quantitative Risk Assessment of Gas Explosion in Offshore Plants YeongAe Heo, a and Inwon Lee b a Department of Civil Engineering, Case Western Reserve University, Cleveland, OH, 44106 b Global Core Research Center for Ships and Offshore Plants (GCRC-SOP), Pusan National University, Busan, 46241, Korea; inwon@pusan.ac.kr (for correspondence) Published online 00 Month 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/prs.11832 In this study, probabilistic risk assessment has been carried out for the prediction of gas explosion loads due to hydrocarbon leaks and subsequent explosions in the topside of offshore platforms. In the initial phase of the risk assessment, the effect of various scenario parameters on the annual probability of gas explosion was quantified via a MATLAB code. For calculating the gas explosion frequency, the hydrocarbon leak frequencies and the ignition probabilities were derived from the HCR (HydroCarbon Release) database from the Health & Safety Executive (HSE, UK), and the IP (Ignition Probability) report from UKOOA (UK Offshore Operators Association), respectively. The MATLAB code has the algorithm to cope with the varying design practice in either Front End Engineering Design phase or detailed design phase. User-definable parameter setup and spreadsheet data input provide the user with the flexibility in selecting relevant level of elaboration for such design parameters as the leak size distribution, the hydrocarbon composition, etc. These features of the code enable controlling the number of explosion scenarios without any parameter range remaining unaccounted for. The present MATLAB code has been applied to generate hydrocarbon leak scenarios and corresponding explosion probability for the topside process modules of a specific oil Floating Production, Storage and Offloading. Varying the number of cases for each parameter leads to the variation of the number of explosion scenarios selected, which are either 48 or 24 in the particular case. For each explosion scenario, the gas leak and explosion simulation was carried out using the FLame Acceleration Simulator (FLACS) commercial S/W package, giving rise to the annual probability of exceedance for the explosion overpressure. Discussion of the influence of explosion scenario selection method on the change of the overpressure exceedance curves is made. VC 2016 American Institute of Chemical Engineers Process Saf Prog 000: 000 000, 2016 Keywords: Keywords: offshore plant; topside gas explosion; quantitative risk assessment; explosion frequency; exceedance curve This work was supported by Samsung Heavy Industry (SHI-GCRC joint research project); Korea government [MEST; through GCRC- SOP; National Research Foundation of Korea (NRF) grant] (2011-0030013). VC 2016 American Institute of Chemical Engineers INTRODUCTION The construction of offshore platforms such as oil rigs and Floating Production, Storage and Offloading (FPSOs) for deep sea exploitation of resources has been ever increasing due to prospective oil markets and the depletion of onshore and coastal oilfields. These offshore platforms, however, are vulnerable to vapor cloud explosion (VCE) which can lead to significant structural damages and casualties. The historical offshore accidents from Piper Alpha disaster in 1988 down to Deepwater Horizon BOP accident in 2010 showed us the severity of offshore explosion accidents. Therefore, the demand for detailed risk analysis for VCE for offshore projects has increased in order to adequately mitigate the risk. G undel et al. [1] developed a simple methodology to assess steel structural performance using hazard scenarios specified in FEMA 426 [2]. The FEMA 426 guidelines, however, are limited to the threat from bomb terrorist attacks whereas VCEs due to hydrocarbon leaks are the major hazards in offshore oil and gas industry. The intensity of VCEs is determined via the probabilistic assessment of diverse and random explosion scenario parameters. Intensive efforts have been sparked to develop probabilistic approaches to evaluate explosion loads due to VCE where large uncertainties are inherent since early 1990s through various joint industry projects (JIPs) sponsored by oil majors and the UK HSE (Health and Safety Executive) such as the Blast and Fire Engineering for Topside structures JIP [3] and the Gas Explosion Engineering JIP [4]. For explosion response, simple methodologies were widely adopted until 1990s such as TNT equivalent method [5], Baker Strehlow method [6], and Multi-Energy method [7]. Researchers [8 10] investigated blast waves generated by the simple methodologies using three-dimensional (3D) computational fluid dynamic (CFD) simulations. Demand for 3D CFD explosion analysis has been abruptly increased with the tremendous improvement of computer performance as well as growing attention to safety due to the regular occurrence of offshore accidents. Detailed procedures for the probabilistic prediction of gas explosion loads are described in informative manners in international guidelines and standards [[11 14]] based on 3D CFD explosion analyses. Process Safety Progress (Vol.00, No.00) Month 2016 1

Figure 1. Leak frequency page of HCR database. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] It is critical to select an appropriate number of explosion scenarios for reliable Explosion Risk Assessment (ERA) results. More data produce better predictions in probabilistic approaches. Although informative procedures are described in the above mentioned guidelines and standards, no specific guidelines for the selection of explosion scenarios for VCE have been prepared yet. Hence, most engineers reduce the number of scenarios in practice due to computational cost and time according to their own assumptions without sufficient scientific evidence, which leads to biased ERA results. Also, a literature survey indicates that little attention has been paid to the generation of appropriate explosion scenarios. A robust explosion scenario generator can be an effective tool to evaluate ERA results for different sets of explosion scenarios. Such a numerical tool can also cope with frequent design changes at both Front End Engineering Design (FEED) phase and the detailed design phase in practice. In this study, a MATLAB code has been developed with a view to systematically generate explosion scenarios and the corresponding annual rate of occurrence for each explosion scenario. For the annual rate of occurrence for explosion scenarios, HCR Leak Database [15] was used to calculate the leak frequencies, and the UKOOA IP Model [16] was adopted for overall ignition probabilities considering the correlation between flammable gas cloud volume and operating conditions on offshore platforms. The code was applied to the ERA for a specific oil FPSO project where a process area is isolated from the accommodation area and utility area on the topside of the FPSO by two Fire and Blast walls. In this particular ERA study, five different sets of scenarios (hereinafter referred to as CASE) considering different leak conditions and wind conditions, consisting of 24 48 scenarios each, and with the corresponding explosion probability for each scenario, were generated in order to show the effect of scenario selection on the result of the ERA study. The explosion overpressure for each scenario was then predicted by 3D CFD gas dispersion and explosion analysis using FLACS. The explosion overpressure exceedance curve can be generated by accumulating the annual probability of exceedance for each scenario in the order of the explosion pressure intensities. The significance of scenario selection in ERA for VCE is investigated by comparison of the explosion overpressure exceedance curves for five CASEs. CALCULATION OF GAS EXPLOSION FREQUENCY Overall Procedure The project to which the present code was applied to the topside of a specific oil FPSO, which consists of various modules in the weather deck and the process deck. The process modules have distinct functions such as turret, separation and stabilization, gas compression, dehydration, fuel gas production, flare and volatile organic compound recovery, produced water treatment, and water injection. Each module consists of a few isolatable sections which usually consist of various equipment items such as pipes, tanks, and pumps. Since each section is isolated from adjacent sections by shutdown valves, a section is considered as a basic unit of the leak frequency calculation. Owing to the random nature of various environmental parameters governing the release and dispersion of hydrocarbon, it is inevitable to use a probabilistic approach. Thus, the annual explosion frequency for the ith section is calculated as the joint probability of various variables as follows; fe i 5ki f i L H ðdþf LL i i ðx; Y ; ZÞfLD ðuþf WS i ðu Þf WD i ðuþ fil i i ðx; Y ; ZÞfIT ðsþ f I i ðmþ (1) Here, k i L is the annual gas leak frequency (times/year), which is a weighted sum of the annual gas leak frequency of each equipment comprising the section. fh i is the leak hole size (d) probability, fll i is the joint probability for the coordinates of leak location (X,Y,Z), fld i is the probability of leak direction (h), fws i and f WD i is the probability of wind speed (U) and direction (h), fil i and f IT i is the probability of ignition location (X,Y,Z) and time delay (s), and fi i is the ignition probability. The independent variables in Eq. (1) are random variables with probability distribution. In this study, the field data in the HydroCarbon Release (HCR) leak DB during 1992 through 2012 was used for k i L and f H i. The IP Look-up Correlation Model [16], which gives the ignition probability as a function of leak rate, was applied to fi i. Other random variables were assumed to have uniform distribution. Although the ignition location and the time delay s between gas leak and ignition could significantly affect the explosion, fil i and 2 Month 2016 Published on behalf of the AIChE DOI 10.1002/prs Process Safety Progress (Vol.00, No.00)

Figure 2. Annual leak frequencies for equipment categories. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] fit i are treated as constants in this study. This is in order to focus on the leak and dispersion characteristics. Leak Frequency Based on the HCR Leak DB The HydroCarbon Release Database (hereinafter referred to as HCR DB) is a compilation of the leak accident data reported on North Sea offshore platforms since 1992. HCR DB, which is under the supervision of the Offshore Division of the HSE (Health and Safety Executive) of the British Government, is accessible through the internet (https://www. hse.gov.uk/hcr3/). As shown in Figure 1, search criteria of leak data can be selected either by systems or equipment. For the selected criterion, the leak accidents are categorized into three; minor (leak rate under 0.1 kg/s), significant (leak rate 0.1 1.0 kg/s), and major (leak rate over 1.0 kg/s) leak. Dividing the number of leaks by the total equipment years Process Safety Progress (Vol.00, No.00) Published on behalf of the AIChE DOI 10.1002/prs Month 2016 3

Figure 3. Calculated leak frequency for each isolated section. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] 4 Month 2016 Published on behalf of the AIChE DOI 10.1002/prs Process Safety Progress (Vol.00, No.00)

Figure 4. Hole size distribution display of the HCR DB. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] (number of equipment items multiplied by the number of years) gives the annual leak frequency, which is 2.1125 3 10 24 times per year in the particular example in Figure 1. In this study, the equipment search criterion is selected as this allows to better represent the range of equipment items comprised in each section. Originally, HCR DB with equipment criteria is divided into total of 124 tertiary equipment categories, which is too detailed. Thus, the secondary equipment categories plus a few wildcard ones (84 in total) are employed in this study. Figure 2 exhibits the annual leak frequencies for 28 secondary categories out of 84 in total. Figure 3 illustrates how to calculate the leak frequency for each section consisting of several types of equipment. For instance, the LP Compression System (Section ID #7) consists of one centrifugal compressor, one cooler and ten flanges, and so on (note that the rows of Figure 3 only show the relevant categories). The composition of each section, column vector in green numbers in Figure 3, is a unique specification which will be treated as a file input to the calculation code. The leak frequencies for the equipment categories are given as a column vector written in black. Thus, the inner product between the section composition vector (green numbers) and the leak frequency vector (black numbers) gives the annual leak frequency of the corresponding section, written in red numbers. For example, the annual leak frequency of the LP Compression System (Section ID #7) is 4.3670 3 10 22 times per year. Leak Size Probability Based on the HCR Leak DB Leak hole size is one of the major parameters that determine the leak rate of hydrocarbon. The HCR DB compiles data on the hole size for all reported leaks. Clicking the Hole button in the leak frequency search window (Figure 1) displays the number of leaks in seven leak size ranges (<10 mm/10 25 mm/25 50 mm/50 75 mm/75 100 mm/>5100 mm/n.a.) for the specific equipment category, as shown in Figure 4. The bottom row indicates the hole size distribution. Figure 5 exhibits the leak size distribution according to the 84 equipment categories used for a given leak frequency. Similarly as the leak frequency calculation, the leak size distribution for the section is calculated as the weighted average of the leak size distribution for each equipment category by the number of equipment of that section. The hole size distribution of the HCR DB can be considered as the probability of hole size defined for the seven discrete size ranges. From this, the discrete cumulative probability F(D) is defined as follows; FðDÞ5PðD d J Þ5 XJ i51 Pðd i D d i11 Þ5 XJ i P i (2) Here, D isthesamplevariablefortheholesize,d J is the upper limit of Jth size range in the HCR DB (e.g., d 2 5 25 mm) and p i is the probability for the ith size range. For instance, p 2 (d 5 10 25 mm) for the BOP Stacks-surface category is read as 0.2500 in Figure 5. Equation (2) implies that F(D) is the probability of the hole size being smaller than the upper limit of Jth size range. The diamonds connected by dotted line in Figure 6 show an example of such discrete probability, calculated for the Section ID #4, Test Separator. It is worthwhile to mention a couple of points; first, the seventh range (N.A.) probability is neglected because the hole sizes were not Process Safety Progress (Vol.00, No.00) Published on behalf of the AIChE DOI 10.1002/prs Month 2016 5

Figure 5. Hole size distribution for the equipment category. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] reported for these events. Second, the upper limit of the sixth range (>5 100 mm) is arbitrarily set as 150 mm. As such, the value of F(D) at the maximum hole size of 150 mm becomes 1, which means that hole size cannot exceed 150 mm. It is notable that the discrete cumulative probability in the above is defined in a similar manner as the Cumulative probability Distribution Function (CDF) for the continuous random variable. In this study, the CDF of hole size probability is obtained by curve fitting the discrete probability against a logarithmic function G(D) 5 alog(d) 1 b. The probability of hole size for an arbitrarily chosen size range (e.g., a D b) is simply given as G(b)-G(a). Compared with the fixed size ranges in HCR DB, this method provides the user higher case of flexibility in the risk assessment. Leak Rate and Ignition Probability The leak rate k (kg/s) is determined from the hole size and the material properties of the hydrocarbon inside the section. The following Det Norske Veritas-Centre or Marine and 6 Month 2016 Published on behalf of the AIChE DOI 10.1002/prs Process Safety Progress (Vol.00, No.00)

Figure 6. Flow chart of Leak.m. Petroleum Technology (DNV-CMPT) formula was employed in this study; vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g11 u g21 t g 2 k 5 C d AP O (3) ZR g T O g11 Here, C d is the discharge coefficient, usually taken as 0.85. A is the area of the leak hole, c is the ratio of specific heats, c 5 C p /C v, Z is the compressibility factor, R O is the gas constant, and T O is absolute temperature. Except A which is determined by the hole size mentioned in the previous section, these parameters are determined by the average of the Process Safety Progress (Vol.00, No.00) Published on behalf of the AIChE DOI 10.1002/prs Month 2016 7

Figure 7. Algorithm for detailed design phase. Figure 8. Algorithm for FEED phase. Table 1. Some of explosion scenarios generated by Leak.m. Section ID Section Volume (m 3 ) Explosion Frequency (times/year) Leak Frequency (times/year) Hole Size (mm) Hole Prob. Leak Rate (kg/s) Ignition Prob. 1 1.615 3 10 0 5.917 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 8.876 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.479 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 2.219 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.183 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.775 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 5.917 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 8.876 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.479 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 2.219 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.183 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.775 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 5.917 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 8.876 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.479 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 2.219 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.183 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.775 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 5.917 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 8.876 3 10 28 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 1 1.615 3 10 0 1.479 3 10 27 1.927 3 10 22 5 0.8372 6.793 3 10 22 1.11 3 10 22 properties of the gaseous hydrocarbon components inside the particular section considered. The ignition probability IP is calculated from the leak rate based on the look-up correlation of the UKOOA IP report as follows; log 10 IP5m log 10 k1c: (4) m and c are the empirical constants determined by the type of offshore platform and the leak rate. In this study, values corresponding to the Offshore FPSO liquid type are used. MATLAB CODE FOR THE CALCULATION OF GAS EXPLOSION FREQUENCY Input Data Based on the procedures described in the previous sections, a MATLAB code (Leak.m) has been developed to generate the gas explosion scenarios and to calculate the corresponding explosion frequencies. Emphasis has been given on the flexible application of the HCR DB to cope with various topside module arrangements and sections composition. Core input data is contained in the following spreadsheet files, which are input to the code. Input#1_Section_Info: consists of four sheets containing the following information. 1 Number of equipment items in each section 2 Volume of equipment items and length of pipes with different diameters in each section 3 Material properties of hydrocarbon in each section 4 Leak locations in each section Input#2_HCR_Leak_DB: annual leak frequency and hole size distribution for each equipment category Variation of Calculation Algorithm According to Design Phase The flow chart of the present MATLAB code is presented in Figure 6. The leak frequency and hole size probability 8 Month 2016 Published on behalf of the AIChE DOI 10.1002/prs Process Safety Progress (Vol.00, No.00)

calculations are performed according to the procedures described earlier. The most distinct feature of this code is the ability to switch the procedure according to the design phase, that is, either the FEED phase or detailed design phase. The procedure described in the previous section is used to calculate the hole size probability and the corresponding leak rate based on the user-specified hole size and position, as shown in Figure 7. In the detailed design phase, such specification is possible because the detailed information regarding the modules and sections is available. On the contrary, the hole size and position can hardly be specified in the conceptual design FEED phases, in which the section and module information is unknown. For these initial design phases, the algorithm is switched to specify the leak rate first and then calculate the corresponding hole size, as shown in Figure 8. This switching algorithm helps to enhance the applicability of the developed code in various design environments. Results of Code Execution Consider an example for detailed design phase due to hydrocarbon gas release as follows: 1. three hole sizes of 5, 15, and 135 mm 2. three leak positions 3. two leak directions of 08 and 458 Figure 9. Histogram of explosion scenarios in Table 1. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] 4. three wind speeds m/s with a 0.1 probability 2.0 m/s with a 0.25 probability 2.5 m/s with a 0.2 probability of occurrence 5. two wind directions 458 with a 0.2 probability of occurrence, respectively 1358 with a 0.3 probability of occurrence, respectively This example leads to 108 explosion scenarios for each section and 1,728 scenarios for the topside modules comprising 16 sections zone of which is listed in Table 1. Each row in Table 1 corresponds to a single explosion scenario. It is seen that the explosion frequency for the first scenario is 5.917 3 10 28 (times/year), implying that this event occurs about every 20 million years. As can be found in Eq. (1), the explosion frequency is the product of the leak frequency, the ignition probability, the hole size probability, and the probabilities of various environmental parameters for gas dispersion. Uniform distributions are assumed for such gas dispersion parameters in this study. As more number of cases for each parameter are considered, the number of scenarios increases. In this example, 108 scenarios were selected in total for each section. The density distribution of the explosion frequency for all of the selected scenarios is reported in the histogram shown in Figure 9. Here, the horizontal axis is given as the logarithm of the explosion frequency, so 25 indicates an explosion frequency of 10 25 times/year. The explosion scenarios should be carefully selected so that the explosion response for each scenario can be appropriately distributed to avoid a biased expectation on explosion probabilities. Although it is beyond the scope of this study, it was observed that engineers select very coarse scenarios to save computational time and cost in practice, which generally yields an unreasonably conservative design blast load. The automatic scenario generation capability of the proposed MATLAB code will leverage efficient assessment for explosion scenario selection. RESULTS OF FLACS EXPLOSION SIMULATION Forty-eight explosion scenarios were selected for the FLACS gas dispersion and explosion CFD analysis. The reduction of the number of scenarios was carried out by selecting relevant isolated sections regarding gas leaks and reducing the number of cases for some parameters. For each explosion scenario, a FLACS simulation was then carried out for the six modules of the process deck of the oil FPSO used in this study. As shown in Figure 10, the leaks were assumed to occur within Modules 3 and 4 with two hole sizes (75 and 150 mm) selected. Three wind speeds (2.5, 7.5, and 12 m/s) and two wind directions (08 and 158) were applied. Table 2 Figure 10. Process area and scenario variables for CFD analysis. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Process Safety Progress (Vol.00, No.00) Published on behalf of the AIChE DOI 10.1002/prs Month 2016 9

Table 2. Gas properties and composition. Item Module 3 Module 4 Inventory volume (m 3 ) 401.29 130.73 Temperature (8C) 109 140 Pressure (bar) 12.5 145.0 Mass density (kg/m 3 ) 14.9 141.7 Methane 0.3134 0.5248 Ethane 0.1311 0.1636 Propane 0.1943 0.1726 i-butane 0.0382 0.0210 n-butane 0.0796 0.0344 i-pentane 0.0218 0.0043 n-pentane 0.0250 0.0037 C6 0.0188 0.0006 C71 0.0191 0.0 Figure 11. Explosion exceedance curves. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] shows the properties and composition of the gaseous hydrocarbons for each leak. The FLACS simulation of each scenario gives the explosion overpressure and plotting the annual explosion frequency against the explosion overpressure gives the explosion overpressure exceedance curve. The exceedance curve obtained for the baseline 48 scenarios (CASE1) is plotted as the black curve in Figure 11. Dimensioning Accidental Load (DAL) is determined by intersecting a particular annual probability level called Allowable Annual Rate of Occurrence or Return Period for explosion and the explosion overpressure exceedance curve. According to international rules and regulations, 10 24 or 10 25 returning every 10 to 20,000 years is commonly used depending on the standards of each project. In this study, 10 24 per year was selected as the allowable limit. To investigate of the effect of scenario selection method on the explosion overpressure exceedance curve, the scenario selection method was varied from the baseline 48 scenarios (CASE 1). For CASE2 through CASE5, 24 scenarios were randomly selected from the baseline scenarios as summarized in Table 3. Whereas one of two leaks was considered in CASE2 and CASE3, CASE4 and CASE5 considered only one leak size, either 75 or 150 mm. These CASEs can be compared in order to exhibit how the explosion scenario selection affects the probabilistic hazard analysis to estimate explosion DAL with respect to CASE1 as point of comparison. A closer inspection of Figure 11 and Table 4 indicates that CASE2 leads to a lower DAL than CASE3. While CASE3 and CASE4 exhibit conservative results (71% and 44% higher than the reference DAL), CASE5 exhibits about 20% lower DAL than the reference value, which will result in underdesign. On the other hand, the estimation of CASE2 is very close to CASE1. Although it requires more comprehensive studies with a bigger number of scenarios and more random scenario sets to appropriately categorize the types of scenario selection that will cause overestimation or underestimation, it is obvious that the scenario selection plays a critical role in blast load estimation and design. CONCLUSIONS In order to investigate the importance of scenario selection in ERA of VCE, first a reference scenario set which contains 48 explosion scenarios was carefully selected so that the explosion pressure responses can be appropriately distributed to avoid biased estimation of explosion probabilities. Twenty-four explosion scenarios were then randomly selected from the reference scenario set in four different ways for a comparative study. The gas dispersion and explosion simulations were carried out using FLACS commercial S/ W package to compute pressure responses for each explosion scenario. An explosion scenario generator was developed using MATLAB for explosion probability computation and scenario selection evaluation purposes. Explosion probability computation is based on the Metocean data for the offshore site, the HCR database from the UK HSE (Health & Safety Executive, UK) and the IP (Ignition Probability) report from UKOOA (UK Offshore Operators Association). Given input parameters accounting for wind, leak, and ignition conditions for an offshore oil and gas production unit, the proposed explosion scenario generator can efficiently generate explosion scenarios and compute explosion probabilities for the generated scenarios. This program can also generate explosion pressure exceedance curves providing CFD Table 3. Five different cases for scenario selection. CASE1 CASE2 CASE3 CASE4 CASE5 No. leak locations 2 1 (Module 3) 1 (Module 4) 2 2 No. leak sizes 2 2 2 1 (150 mm) 1 (75 mm) No. leak directions 2 2 2 2 2 No. wind directions 2 2 2 2 2 No. wind speeds 3 3 3 3 3 No. of scenarios 48 24 24 24 24 10 Month 2016 Published on behalf of the AIChE DOI 10.1002/prs Process Safety Progress (Vol.00, No.00)

Table 4. Design explosion load from different CASES. Design explosion load (bar) CASE1 CASE2 CASE3 CASE4 CASE5 1.81 1.59 3.09 2.61 1.44 analysis results for each scenario. Hence, the automatic process capability of the proposed program facilitates the comparative study to highlight the importance of scenario selection in ERA. The exceedance curves for four different scenario sets exhibit large variations in DAL estimation, which indicates that current probabilistic risk assessment is prone to either overdesign or underdesign depending on assumptions adopted in explosion scenario selection. It is clear that more sophisticated guidelines on explosion scenario selection in ERA are necessary for more reliable and robust design load estimation. Due to limitations in the time and resources of this study, the effect of ignition could not be included in discussion. Also, a quite limited number of cases for each scenario parameter and scenario set were used in this preliminary study. The fundamental findings of this study will be enhanced by more comprehensive investigations in future studies ACKNOWLEDGMENT The CFD analyses for explosion pressure responses used in this article were conducted by GexCon AS, Bergen, Norway. LITERATURE CITED 1. M. G undel, B. Hoffmeister, M. Feldmann, and B. Hauke, Design of high rise steel buildings against terrorist attacks, Comput Aided Civ Infrastruct Eng 27 (2012), 369 383. 2. FEMA. Reference Manual to Mitigate Potential Terrorists Attacks Against Buildings, Risk Management Series, FEMA-Report 426, Federal Emergency Management Agency (FEMA), Washington DC, 2003. 3. C.A. Selby and B.A. Burgan, Blast and Fire Engineering for Topsides Structures Phase 2, Final Summary Report, SCI-P-253, The Steel Construction Institute, Ascot, UK, 1998, ISBN 1 85942 078 8. 4. J. Czujko, Design of Offshore Facilities to Resist Gas Explosion Hazard, Engineering Handbook, CorrOcean, Sandvika, Norway, 2001. 5. M.J. Steindler and W.B. Seefeldt, A method for estimating the challenge to an air-cleaning system resulting from an accidental explosive event, The 16th Department of Energy Conference on Nuclear Air Cleaning, Washington, DC and Boston, MA, 1980. 6. R.A. Strehlow, R.T. Luckritz, A.A. Adamczyk, and S.A. Shimpi, The blast wave generated by spherical flames, Combust Flame 35 (1979), 297 310. 7. A.C. van den BERG, The Multi-Energy Method A Framework for Vapour Cloud Explosion Blast Prediction, J Hazard Mater 12 (1985), 1 10. 8. M.J. Tang and Q.A. Baker, A new set of blast curves from vapor cloud explosion, Process Saf Prog 18 (1999), 235 240. 9. A. Beccantini, A. Malczynski, and E. Studer, Comparison of TNT-equivalence approach, TNO multi-energy approach and a CFD approach in investigating hemispheric hydrogen-air vapor cloud explosions, Proceedings of the 5th International Seminar on Fire and Explosion Hazards, Edinburgh, UK, April 23 27, 2007. 10. A. Sari, Comparison of TNO Multienergy and Baker Strehlow Tang Models, AIChE Process Saf Prog 30 (2011), 23 26. 11. ISO 17776:2000, Petroleum and Natural Gas Industries Offshore Production Installations Guidelines on Tools and Techniques for Hazard Identification and Risk Assessment, International Organization for Standardization, 2000. 12. HSE, PM/Technical/12 - Fire, Explosion and Risk Assessment Topic Guidance, Health and Safety Executives, London, UK, 2003. 13. UKOOA, Fire and Explosion Guidance, Part 0: Fire and Explosion Hazard Management, UK Offshore Operators Association Limited, London, UK, 2003. 14. Norsok, Norsok Standard Z-013, Risk and Emergency Preparedness Assessment, 3rd Edition, Standards Norway, Lysaker, Norway, 2010. 15. HCR Leak Database, Hydrocarbon Releases System, Offshore Division of Health and Safety Executive, 1992 2012, Available at https://www.hse.gov.uk/hcr3/. 16. UKOOA IP Model, Ignition Probability Review, Model Development and Look-Up Correlations, EI Research Report, Energy Institute, London, 2006, ISBN 978 0 85293 454 8. Process Safety Progress (Vol.00, No.00) Published on behalf of the AIChE DOI 10.1002/prs Month 2016 11