Parametric Generation of Explosion Scenarios for

Quantitative Risk Assessment of Gas Explosion in

Offshore PlantsYeongAe Heoa and Inwon Leeb

aDepartment of Civil Engineering Case Western Reserve University Cleveland OH 44106bGlobal Core Research Center for Ships and Offshore Plants (GCRC-SOP) Pusan National University Busan 46241 Korea

inwonpusanackr (for correspondence)

Published online 00 Month 2016 in Wiley Online Library (wileyonlinelibrarycom) DOI 101002prs11832

In this study probabilistic risk assessment has been carriedout for the prediction of gas explosion loads due to hydrocarbonleaks and subsequent explosions in the topside of offshore plat-forms In the initial phase of the risk assessment the effect ofvarious scenario parameters on the annual probability of gasexplosion was quantified via a MATLAB code For calculatingthe gas explosion frequency the hydrocarbon leak frequenciesand the ignition probabilities were derived from the HCR(HydroCarbon Release) database from the Health amp Safety Exec-utive (HSE UK) and the IP (Ignition Probability) report fromUKOOA (UK Offshore Operators Association) respectively TheMATLAB code has the algorithm to cope with the varying designpractice in either Front End Engineering Design phase ordetailed design phase User-definable parameter setup andspreadsheet data input provide the user with the flexibility inselecting relevant level of elaboration for such design parametersas the leak size distribution the hydrocarbon composition etcThese features of the code enable controlling the number ofexplosion scenarios without any parameter range remainingunaccounted for The present MATLAB code has been applied togenerate hydrocarbon leak scenarios and corresponding explo-sion probability for the topside process modules of a specific oilFloating Production Storage and Offloading Varying the num-ber of cases for each parameter leads to the variation of thenumber of explosion scenarios selected which are either 48 or24 in the particular case For each explosion scenario the gasleak and explosion simulation was carried out using the FLameAcceleration Simulator (FLACS) commercial SW package givingrise to the annual probability of exceedance for the explosionoverpressure Discussion of the influence of explosion scenarioselection method on the change of the overpressure exceedancecurves is made VC 2016 American Institute of Chemical Engineers Pro-

cess Saf Prog 000 000ndash000 2016

Keywords Keywords offshore plant topside gas explosionquantitative risk assessment explosion frequency exceedancecurve

INTRODUCTION

The construction of offshore platforms such as oil rigsand Floating Production Storage and Offloading (FPSOs)for deep sea exploitation of resources has been everincreasing due to prospective oil markets and the depletionof onshore and coastal oilfields These offshore platformshowever are vulnerable to vapor cloud explosion (VCE)which can lead to significant structural damages and casual-ties The historical offshore accidents from Piper Alpha dis-aster in 1988 down to Deepwater Horizon BOP accident in2010 showed us the severity of offshore explosion acci-dents Therefore the demand for detailed risk analysis forVCE for offshore projects has increased in order toadequately mitigate the risk

Geuroundel et al [1] developed a simple methodology toassess steel structural performance using hazard scenariosspecified in FEMA 426 [2] The FEMA 426 guidelines how-ever are limited to the threat from bomb terrorist attackswhereas VCEs due to hydrocarbon leaks are the major haz-ards in offshore oil and gas industry The intensity of VCEs isdetermined via the probabilistic assessment of diverse andrandom explosion scenario parameters Intensive effortshave been sparked to develop probabilistic approaches toevaluate explosion loads due to VCE where large uncertain-ties are inherent since early 1990s through various jointindustry projects (JIPs) sponsored by oil majors and the UKHSE (Health and Safety Executive) such as the Blast and FireEngineering for Topside structures JIP [3] and the Gas Explo-sion Engineering JIP [4]

For explosion response simple methodologies werewidely adopted until 1990s such as TNT equivalent method[5] BakerndashStrehlow method [6] and Multi-Energy method [7]Researchers [8ndash10] investigated blast waves generated by thesimple methodologies using three-dimensional (3D) compu-tational fluid dynamic (CFD) simulations Demand for 3DCFD explosion analysis has been abruptly increased with thetremendous improvement of computer performance as wellas growing attention to safety due to the regular occurrenceof offshore accidents Detailed procedures for the probabilis-tic prediction of gas explosion loads are described in inform-ative manners in international guidelines and standards[[11ndash14]] based on 3D CFD explosion analyses

This work was supported by Samsung Heavy Industry (SHI-GCRCjoint research project) Korea government [MEST through GCRC-SOP National Research Foundation of Korea (NRF) grant] (2011-0030013)

VC 2016 American Institute of Chemical Engineers

Process Safety Progress (Vol00 No00) Month 2016 1

It is critical to select an appropriate number of explosion sce-narios for reliable Explosion Risk Assessment (ERA) results Moredata produce better predictions in probabilistic approachesAlthough informative procedures are described in the abovementioned guidelines and standards no specific guidelines forthe selection of explosion scenarios for VCE have been preparedyet Hence most engineers reduce the number of scenarios inpractice due to computational cost and time according to theirown assumptions without sufficient scientific evidence whichleads to biased ERA results Also a literature survey indicates thatlittle attention has been paid to the generation of appropriateexplosion scenarios A robust explosion scenario generator canbe an effective tool to evaluate ERA results for different sets ofexplosion scenarios Such a numerical tool can also cope withfrequent 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 aview to systematically generate explosion scenarios and thecorresponding annual rate of occurrence for each explosionscenario For the annual rate of occurrence for explosion sce-narios HCR Leak Database [15] was used to calculate the leakfrequencies and the UKOOA IP Model [16] was adopted foroverall ignition probabilities considering the correlationbetween flammable gas cloud volume and operating conditionson offshore platforms The code was applied to the ERA for aspecific oil FPSO project where a process area is isolated fromthe accommodation area and utility area on the topside of theFPSO by two Fire and Blast walls In this particular ERA studyfive different sets of scenarios (hereinafter referred to as CASE)considering different leak conditions and wind conditions con-sisting of 24ndash48 scenarios each and with the correspondingexplosion probability for each scenario were generated inorder to show the effect of scenario selection on the result ofthe ERA study The explosion overpressure for each scenariowas then predicted by 3D CFD gas dispersion and explosionanalysis using FLACS The explosion overpressure exceedancecurve can be generated by accumulating the annual probabilityof exceedance for each scenario in the order of the explosionpressure intensities The significance of scenario selection inERA for VCE is investigated by comparison of the explosionoverpressure exceedance curves for five CASEs

CALCULATION OF GAS EXPLOSION FREQUENCY

Overall ProcedureThe project to which the present code was applied to the

topside of a specific oil FPSO which consists of variousmodules in the weather deck and the process deck The pro-cess modules have distinct functions such as turret separa-tion and stabilization gas compression dehydration fuel gasproduction flare and volatile organic compound recoveryproduced water treatment and water injection Each moduleconsists of a few isolatable sections which usually consist ofvarious equipment items such as pipes tanks and pumpsSince each section is isolated from adjacent sections by shut-down valves a section is considered as a basic unit of theleak frequency calculation

Owing to the random nature of various environmentalparameters governing the release and dispersion of hydrocar-bon it is inevitable to use a probabilistic approach Thusthe annual explosion frequency for the ith section is calcu-lated as the joint probability of various variables as follows

f iE5ki

L f i

HethdTHORN f iLLethX Y ZTHORN f i

LDethuTHORN f iWSethU THORN f i

WDethuTHORN f i

ILethX Y ZTHORN f iITethsTHORN f i

I ethmTHORN (1)

Here kiL is the annual gas leak frequency (timesyear) which

is a weighted sum of the annual gas leak frequency of eachequipment comprising the section f i

H is the leak hole size (d)probability f i

LL is the joint probability for the coordinates ofleak location (XYZ) f i

LD is the probability of leak direction(h) f i

WS and f iWD is the probability of wind speed (U) and

direction (h) f iIL and f i

IT is the probability of ignition location(XYZ) and time delay (s) and f i

I is the ignition probabilityThe independent variables in Eq (1) are random variables

with probability distribution In this study the field data inthe HydroCarbon Release (HCR) leak DB during 1992through 2012 was used for ki

L and f iH The IP Look-up Corre-

lation Model [16] which gives the ignition probability as afunction of leak rate was applied to f i

I Other random varia-bles were assumed to have uniform distribution Althoughthe ignition location and the time delay s between gas leakand ignition could significantly affect the explosion f i

IL and

Figure 1 Leak frequency page of HCR database [Color figure can be viewed in the online issue which is available atwileyonlinelibrarycom]

DOI 101002prs Process Safety Progress (Vol00 No00)2 Month 2016 Published on behalf of the AIChE

f iIT are treated as constants in this study This is in order tofocus on the leak and dispersion characteristics

Leak Frequency Based on the HCR Leak DBThe HydroCarbon Release Database (hereinafter referred

to as HCR DB) is a compilation of the leak accident datareported on North Sea offshore platforms since 1992 HCRDB which is under the supervision of the Offshore Division

of the HSE (Health and Safety Executive) of the British Gov-ernment is accessible through the internet (httpswwwhsegovukhcr3) As shown in Figure 1 search criteria ofleak data can be selected either by systems or equipmentFor the selected criterion the leak accidents are categorizedinto three minor (leak rate under 01 kgs) significant (leakrate 01ndash10 kgs) and major (leak rate over 10 kgs) leakDividing the number of leaks by the total equipment years

Figure 2 Annual leak frequencies for equipment categories [Color figure can be viewed in the online issue which is availableat wileyonlinelibrarycom]

Process Safety Progress (Vol00 No00) Published on behalf of the AIChE DOI 101002prs Month 2016 3

Figure 3 Calculated leak frequency for each isolated section [Color figure can be viewed in the online issue which is avail-able at wileyonlinelibrarycom]

DOI 101002prs Process Safety Progress (Vol00 No00)4 Month 2016 Published on behalf of the AIChE

(number of equipment items multiplied by the number ofyears) gives the annual leak frequency which is 21125 3

1024 times per year in the particular example in Figure 1In this study the equipment search criterion is selected as

this allows to better represent the range of equipment itemscomprised in each section Originally HCR DB with equip-ment criteria is divided into total of 124 tertiary equipmentcategories which is too detailed Thus the secondary equip-ment categories plus a few wildcard ones (84 in total) areemployed in this study Figure 2 exhibits the annual leak fre-quencies for 28 secondary categories out of 84 in total

Figure 3 illustrates how to calculate the leak frequency foreach section consisting of several types of equipment Forinstance the LP Compression System (Section ID 7) consistsof one centrifugal compressor one cooler and ten flanges andso on (note that the rows of Figure 3 only show the relevantcategories) The composition of each section column vector ingreen numbers in Figure 3 is a unique specification whichwill be treated as a file input to the calculation code The leakfrequencies for the equipment categories are given as a col-umn vector written in black Thus the inner product betweenthe section composition vector (green numbers) and the leakfrequency vector (black numbers) gives the annual leak fre-quency of the corresponding section written in red numbersFor example the annual leak frequency of the LP CompressionSystem (Section ID 7) is 43670 3 1022 times per year

Leak Size Probability Based on the HCR Leak DBLeak hole size is one of the major parameters that deter-

mine the leak rate of hydrocarbon The HCR DB compilesdata on the hole size for all reported leaks Clicking the

ldquoHolerdquo button in the leak frequency search window (Figure 1)displays the number of leaks in seven leak size ranges (lt10mm10ndash25 mm25ndash50 mm50ndash75 mm75ndash100 mmgt5100mmNA) for the specific equipment category as shown inFigure 4 The bottom row indicates the hole size distributionFigure 5 exhibits the leak size distribution according to the 84equipment categories used for a given leak frequency Simi-larly as the leak frequency calculation the leak size distribu-tion for the section is calculated as the weighted average ofthe leak size distribution for each equipment category by thenumber of equipment of that section

The hole size distribution of the HCR DB can be consid-ered as the probability of hole size defined for the seven dis-crete size ranges From this the discrete cumulativeprobability F(D) is defined as follows

FethDTHORN5PethD dJ THORN5XJ

i51

Pethdi D di11THORN5XJ

i

Pi (2)

Here D is the sample variable for the hole size dJ is the upperlimit of Jth size range in the HCR DB (eg d2 5 25 mm) and pi

is the probability for the ith size range For instance p2

(d 5 10ndash25 mm) for the ldquoBOP Stacks-surfacerdquo category is readas 02500 in Figure 5 Equation (2) implies that F(D) is theprobability of the hole size being smaller than the upper limitof Jth size range The diamonds connected by dotted line inFigure 6 show an example of such discrete probability calcu-lated for the Section ID 4 Test Separator It is worthwhile tomention a couple of points first the seventh range (NA)probability is neglected because the hole sizes were not

Figure 4 Hole size distribution display of the HCR DB [Color figure can be viewed in the online issue which is available atwileyonlinelibrarycom]

Process Safety Progress (Vol00 No00) Published on behalf of the AIChE DOI 101002prs Month 2016 5

reported for these events Second the upper limit of the sixthrange (gt5 100 mm) is arbitrarily set as 150 mm As such thevalue of F(D) at the maximum hole size of 150 mm becomes1 which means that hole size cannot exceed 150 mm

It is notable that the discrete cumulative probability in theabove is defined in a similar manner as the Cumulative prob-ability Distribution Function (CDF) for the continuous ran-dom variable In this study the CDF of hole size probabilityis obtained by curve fitting the discrete probability against alogarithmic function G(D) 5 alog(D) 1 b The probability of

hole size for an arbitrarily chosen size range (eg aD b)is simply given as G(b)-G(a) Compared with the fixed sizeranges in HCR DB this method provides the user highercase of flexibility in the risk assessment

Leak Rate and Ignition ProbabilityThe leak rate k (kgs) is determined from the hole size

and the material properties of the hydrocarbon inside the sec-tion The following Det Norske Veritas-Centre or Marine and

Figure 5 Hole size distribution for the equipment category [Color figure can be viewed in the online issue which is availableat wileyonlinelibrarycom]

DOI 101002prs Process Safety Progress (Vol00 No00)6 Month 2016 Published on behalf of the AIChE

Petroleum Technology (DNV-CMPT) formula was employedin this study

k 5 CdAPO

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffig

ZRgTO

2

g11

g11g21

vuut(3)

Here Cd is the discharge coefficient usually taken as 085 Ais the area of the leak hole c is the ratio of specific heatsc 5 CpCv Z is the compressibility factor RO is the gas con-stant and TO is absolute temperature Except A which isdetermined by the hole size mentioned in the previous sec-tion these parameters are determined by the average of the

Figure 6 Flow chart of Leakm

Process Safety Progress (Vol00 No00) Published on behalf of the AIChE DOI 101002prs Month 2016 7

properties of the gaseous hydrocarbon components inside

the particular section consideredThe ignition probability IP is calculated from the leak rate

based on the look-up correlation of the UKOOA IP report asfollows

log 10 IP5m log 10k1c (4)

m and c are the empirical constants determined by the typeof offshore platform and the leak rate In this study valuescorresponding to the ldquoOffshore FPSO liquidrdquo type are used

MATLAB CODE FOR THE CALCULATION OF GAS EXPLOSION FREQUENCY

Input DataBased on the procedures described in the previous sec-

tions a MATLAB code (Leakm) has been developed to gen-erate the gas explosion scenarios and to calculate thecorresponding explosion frequencies Emphasis has been

given on the flexible application of the HCR DB to copewith various topside module arrangements and sections com-position Core input data is contained in the followingspreadsheet files which are input to the code

Input1_Section_Info consists of four sheets containingthe following information1Number of equipment items in each section2 Volume of equipment items and length of pipes with

different diameters in each section3Material properties of hydrocarbon in each section4 Leak locations in each section Input2_HCR_Leak_DB annual leak frequency and hole

size distribution for each equipment category

Variation of Calculation Algorithm According to

Design PhaseThe flow chart of the present MATLAB code is presented

in Figure 6 The leak frequency and hole size probability

Figure 7 Algorithm for detailed design phase

Figure 8 Algorithm for FEED phase

Table 1 Some of explosion scenarios generated by Leakm

Section IDSection

Volume (m3)

ExplosionFrequency

(timesyear)Leak Frequency

(timesyear)Hole

Size (mm)HoleProb

Leak Rate(kgs) Ignition Prob

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

DOI 101002prs Process Safety Progress (Vol00 No00)8 Month 2016 Published on behalf of the AIChE

properties of the gaseous hydrocarbon components inside

the particular section consideredThe ignition probability IP is calculated from the leak rate

based on the look-up correlation of the UKOOA IP report asfollows

log 10 IP5m log 10k1c (4)

m and c are the empirical constants determined by the typeof offshore platform and the leak rate In this study valuescorresponding to the ldquoOffshore FPSO liquidrdquo type are used

MATLAB CODE FOR THE CALCULATION OF GAS EXPLOSION FREQUENCY

Input DataBased on the procedures described in the previous sec-

tions a MATLAB code (Leakm) has been developed to gen-erate the gas explosion scenarios and to calculate thecorresponding explosion frequencies Emphasis has been

given on the flexible application of the HCR DB to copewith various topside module arrangements and sections com-position Core input data is contained in the followingspreadsheet files which are input to the code

Input1_Section_Info consists of four sheets containingthe following information1Number of equipment items in each section2 Volume of equipment items and length of pipes with

different diameters in each section3Material properties of hydrocarbon in each section4 Leak locations in each section Input2_HCR_Leak_DB annual leak frequency and hole

size distribution for each equipment category

Variation of Calculation Algorithm According to

Design PhaseThe flow chart of the present MATLAB code is presented

in Figure 6 The leak frequency and hole size probability

Figure 7 Algorithm for detailed design phase

Figure 8 Algorithm for FEED phase

Table 1 Some of explosion scenarios generated by Leakm

Section IDSection

Volume (m3)

ExplosionFrequency

(timesyear)Leak Frequency

(timesyear)Hole

Size (mm)HoleProb

Leak Rate(kgs) Ignition Prob

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 2219 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1183 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1775 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 5917 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 8876 3 1028 1927 3 1022 5 08372 6793 3 1022 111 3 1022

1 1615 3 100 1479 3 1027 1927 3 1022 5 08372 6793 3 1022 111 3 1022

DOI 101002prs Process Safety Progress (Vol00 No00)8 Month 2016 Published on behalf of the AIChE

calculations are performed according to the proceduresdescribed earlier The most distinct feature of this code is theability to switch the procedure according to the designphase that is either the FEED phase or detailed designphase

The procedure described in the previous section is used tocalculate the hole size probability and the corresponding leakrate based on the user-specified hole size and position asshown in Figure 7 In the detailed design phase such specifi-cation is possible because the detailed information regardingthe modules and sections is available On the contrary thehole size and position can hardly be specified in the concep-tual design FEED phases in which the section and moduleinformation is unknown For these initial design phases thealgorithm is switched to specify the leak rate first and thencalculate the corresponding hole size as shown in Figure 8This switching algorithm helps to enhance the applicability ofthe developed code in various design environments

Results of Code ExecutionConsider an example for detailed design phase due to

hydrocarbon gas release as follows

1 three hole sizes of 5 15 and 135 mm2 three leak positions3 two leak directions of 08 and 458

4 three wind speeds ms with a 01 probability 20 ms with a 025 probability 25 ms with a 02 probability of occurrence

5 two wind directions 458 with a 02 probability of occurrence respectively 1358 with a 03 probability of occurrence respectively

This example leads to 108 explosion scenarios for eachsection and 1728 scenarios for the topside modules compris-ing 16 sectionsmdashzone of which is listed in Table 1

Each row in Table 1 corresponds to a single explosionscenario It is seen that the explosion frequency for the firstscenario is 5917 3 1028 (timesyear) implying that thisevent occurs about every 20 million years As can be foundin Eq (1) the explosion frequency is the product of the leakfrequency the ignition probability the hole size probabilityand the probabilities of various environmental parametersfor gas dispersion Uniform distributions are assumed forsuch gas dispersion parameters in this study As more num-ber of cases for each parameter are considered the numberof scenarios increases In this example 108 scenarios wereselected in total for each section

The density distribution of the explosion frequency for allof the selected scenarios is reported in the histogram shownin Figure 9 Here the horizontal axis is given as the loga-rithm of the explosion frequency so 25 indicates an explo-sion frequency of 1025 timesyear The explosion scenariosshould be carefully selected so that the explosion responsefor each scenario can be appropriately distributed to avoid abiased expectation on explosion probabilities Although it isbeyond the scope of this study it was observed that engi-neers select very coarse scenarios to save computational timeand cost in practice which generally yields an unreasonablyconservative design blast load The automatic scenario gener-ation capability of the proposed MATLAB code will leverageefficient assessment for explosion scenario selection

RESULTS OF FLACS EXPLOSION SIMULATION

Forty-eight explosion scenarios were selected for theFLACS gas dispersion and explosion CFD analysis Thereduction of the number of scenarios was carried out byselecting relevant isolated sections regarding gas leaks andreducing the number of cases for some parameters For eachexplosion scenario a FLACS simulation was then carried outfor the six modules of the process deck of the oil FPSO usedin this study As shown in Figure 10 the leaks were assumedto occur within Modules 3 and 4 with two hole sizes (75 and150 mm) selected Three wind speeds (25 75 and 12 ms)and two wind directions (08 and 158) were applied Table 2

Figure 9 Histogram of explosion scenarios in Table 1[Color figure can be viewed in the online issue which isavailable at wileyonlinelibrarycom]

Figure 10 Process area and scenario variables for CFD analysis [Color figure can be viewed in the online issue which isavailable at wileyonlinelibrarycom]

Process Safety Progress (Vol00 No00) Published on behalf of the AIChE DOI 101002prs Month 2016 9

shows the properties and composition of the gaseous hydro-carbons for each leak

The FLACS simulation of each scenario gives the explo-sion overpressure and plotting the annual explosion fre-quency against the explosion overpressure gives theexplosion overpressure exceedance curve The exceedancecurve obtained for the baseline 48 scenarios (CASE1) is plot-ted as the black curve in Figure 11 Dimensioning AccidentalLoad (DAL) is determined by intersecting a particular annualprobability level called ldquoAllowable Annual Rate of Occur-

rence or Return Periodrdquo for explosion and the explosionoverpressure exceedance curve According to internationalrules and regulations 1024 or 1025mdashreturning every 10 to20000 yearsmdashis commonly used depending on the standardsof each project In this study 1024 per year was selected asthe allowable limit

To investigate of the effect of scenario selection methodon the explosion overpressure exceedance curve the sce-nario selection method was varied from the baseline 48 sce-narios (CASE 1) For CASE2 through CASE5 24 scenarioswere randomly selected from the baseline scenarios as sum-marized in Table 3 Whereas one of two leaks was consid-ered in CASE2 and CASE3 CASE4 and CASE5 consideredonly one leak size either 75 or 150 mm These CASEs canbe compared in order to exhibit how the explosion scenarioselection affects the probabilistic hazard analysis to estimateexplosion DAL with respect to CASE1 as point ofcomparison

A closer inspection of Figure 11 and Table 4 indicates thatCASE2 leads to a lower DAL than CASE3 While CASE3 andCASE4 exhibit conservative results (71 and 44 higher thanthe reference DAL) CASE5 exhibits about 20 lower DALthan the reference value which will result in underdesignOn the other hand the estimation of CASE2 is very close toCASE1 Although it requires more comprehensive studieswith a bigger number of scenarios and more random sce-nario sets to appropriately categorize the types of scenarioselection that will cause overestimation or underestimation itis obvious that the scenario selection plays a critical role inblast load estimation and design

CONCLUSIONS

In order to investigate the importance of scenario selec-tion in ERA of VCE first a reference scenario set which con-tains 48 explosion scenarios was carefully selected so thatthe explosion pressure responses can be appropriately dis-tributed to avoid biased estimation of explosion probabilitiesTwenty-four explosion scenarios were then randomlyselected from the reference scenario set in four differentways for a comparative study The gas dispersion and explo-sion simulations were carried out using FLACS commercial SW package to compute pressure responses for each explo-sion scenario

An explosion scenario generator was developed usingMATLAB for explosion probability computation and scenarioselection evaluation purposes Explosion probability compu-tation is based on the Metocean data for the offshore sitethe HCR database from the UK HSE (Health amp Safety Execu-tive UK) and the IP (Ignition Probability) report fromUKOOA (UK Offshore Operators Association) Given inputparameters accounting for wind leak and ignition condi-tions for an offshore oil and gas production unit the pro-posed explosion scenario generator can efficiently generateexplosion scenarios and compute explosion probabilities forthe generated scenarios This program can also generateexplosion pressure exceedance curves providing CFD

Table 2 Gas properties and composition

Item Module 3 Module 4

Inventory volume (m3) 40129 13073Temperature (8C) 109 140Pressure (bar) 125 1450Mass density (kgm3) 149 1417Methane 03134 05248Ethane 01311 01636Propane 01943 01726i-Butane 00382 00210n-Butane 00796 00344i-Pentane 00218 00043n-Pentane 00250 00037C6 00188 00006C71 00191 00

Figure 11 Explosion exceedance curves [Color figure canbe viewed in the online issue which is available at wileyon-linelibrarycom]

Table 3 Five different cases for scenario selection

CASE1 CASE2 CASE3 CASE4 CASE5

No leak locations 2 1 (Module 3) 1 (Module 4) 2 2No leak sizes 2 2 2 1 (150 mm) 1 (75 mm)No leak directions 2 2 2 2 2No wind directions 2 2 2 2 2No wind speeds 3 3 3 3 3No of scenarios 48 24 24 24 24

DOI 101002prs Process Safety Progress (Vol00 No00)10 Month 2016 Published on behalf of the AIChE

analysis results for each scenario Hence the automatic pro-cess capability of the proposed program facilitates the com-parative study to highlight the importance of scenarioselection in ERA

The exceedance curves for four different scenario setsexhibit large variations in DAL estimation which indicatesthat current probabilistic risk assessment is prone to eitheroverdesign or underdesign depending on assumptionsadopted in explosion scenario selection It is clear that moresophisticated guidelines on explosion scenario selection inERA are necessary for more reliable and robust design loadestimation

Due to limitations in the time and resources of this studythe effect of ignition could not be included in discussionAlso a quite limited number of cases for each scenarioparameter and scenario set were used in this preliminarystudy The fundamental findings of this study will beenhanced by more comprehensive investigations in futurestudies

ACKNOWLEDGMENT

The CFD analyses for explosion pressure responses usedin this article were conducted by GexCon AS BergenNorway

LITERATURE CITED

1 M Geuroundel B Hoffmeister M Feldmann and B HaukeDesign of high rise steel buildings against terroristattacks Comput Aided Civ Infrastruct Eng 27 (2012)369ndash383

2 FEMA Reference Manual to Mitigate Potential TerroristsAttacks Against Buildings Risk Management SeriesFEMA-Report 426 Federal Emergency ManagementAgency (FEMA) Washington DC 2003

3 CA Selby and BA Burgan Blast and Fire Engineeringfor Topsides Structures Phase 2 Final Summary ReportSCI-P-253 The Steel Construction Institute Ascot UK1998 ISBN 1 85942 078 8

4 J Czujko Design of Offshore Facilities to Resist GasExplosion Hazard Engineering Handbook CorrOceanSandvika Norway 2001

5 MJ Steindler and WB Seefeldt ldquoA method for estimat-ing the challenge to an air-cleaning system resulting froman accidental explosive eventrdquo The 16th Department ofEnergy Conference on Nuclear Air Cleaning WashingtonDC and Boston MA 1980

6 RA Strehlow RT Luckritz AA Adamczyk and SAShimpi The blast wave generated by spherical flamesCombust Flame 35 (1979) 297ndash310

7 AC van den BERG The Multi-Energy Method ndash AFramework for Vapour Cloud Explosion Blast PredictionJ Hazard Mater 12 (1985) 1ndash10

8 MJ Tang and QA Baker A new set of blast curves fromvapor cloud explosion Process Saf Prog 18 (1999) 235ndash240

9 A Beccantini A Malczynski and E Studer ldquoComparisonof TNT-equivalence approach TNO multi-energyapproach and a CFD approach in investigating hemi-spheric hydrogen-air vapor cloud explosionsrdquo Proceed-ings of the 5th International Seminar on Fire andExplosion Hazards Edinburgh UK April 23ndash27 2007

10 A Sari Comparison of TNO Multienergy and BakerndashStrehlowndashTang Models AIChE Process Saf Prog 30(2011) 23ndash26

11 ISO 177762000 Petroleum and Natural Gas Industries ndashOffshore Production Installations ndash Guidelines on Toolsand Techniques for Hazard Identification and RiskAssessment International Organization for Standardiza-tion 2000

12 HSE PMTechnical12 - Fire Explosion and Risk Assess-ment Topic Guidance Health and Safety Executives Lon-don UK 2003

13 UKOOA Fire and Explosion Guidance Part 0 Fire andExplosion Hazard Management UK Offshore OperatorsAssociation Limited London UK 2003

14 Norsok Norsok Standard Z-013 Risk and Emergency Pre-paredness Assessment 3rd Edition Standards NorwayLysaker Norway 2010

15 HCR Leak Database Hydrocarbon Releases System Off-shore Division of Health and Safety Executive 1992ndash2012 Available at httpswwwhsegovukhcr3

16 UKOOA IP Model Ignition Probability Review ModelDevelopment and Look-Up Correlations EI ResearchReport Energy Institute London 2006 ISBN 978 0 85293454 8

Table 4 Design explosion load from different CASES

CASE1 CASE2 CASE3 CASE4 CASE5

Designexplosionload (bar)

181 159 309 261 144

Process Safety Progress (Vol00 No00) Published on behalf of the AIChE DOI 101002prs Month 2016 11