_________________________________________________________________________ 1 Ph.D, Risk and Reliability Engineer - PETROBRAS 5 Mechanical Engineer – PETROBRAS 2 Chemical Engineer – PETROBRAS 6 D.Sc., Reliability Engineer – DNV 3 M.Sc., Mechanical Engineer – PETROBRAS 7 M.Sc., Production Engineer – DNV 4 MSc., Reliability Engineer - PETROBRAS 8 Chemical Engineer – DNV

RELIABILITY MODELLING – PETROBRAS 2010 INTEGRATED GAS SUPPLY CHAIN

Denise Faertes 1, Luciana Heil 2, Leonardo Saker 3, Flavia Vieira 4, Francisco Risi 5, Joaquim Domingues 6,

Tobias Alvarenga 7, Eduardo Carvalho8, Patrícia Mussel 8

Abstract The purpose of this paper is to present the innovative reliability modeling of Petrobras 2010 integrated gas supply chain. The model represents a challenge in terms of complexity and software robustness. It was jointly developed by PETROBRAS Gas & Power Department and Det Norske Veritas. It was carried out with the objective of evaluating security of supply of 2010 gas network design that was conceived to connect Brazilian Northeast and Southeast regions. To provide best in class analysis, state of the art software was used to quantify the availability and the efficiency of the overall network and its individual components (such as gas processing units, city-gates, compressor stations, pipelines). The study requests, as input, a lot of information, that should be provided from different sectors of Petrobras, since it is supposed to depict reliability performance of all gas supply chain players. Information such as 2010 design configurations, gas offer and demand profiles, shedding priority, pressure delivery conditions, supply contract and associated penalties, commodity prices, etc., should be raised and addressed. More than twelve processing plants were modeled, in a detailed manner, and have their performance indicators compared. Different gas sources were considered in the study, such as offshore platforms, onshore fields, Bolivia pipeline and LNG ships. Detailed failure probability and repair data were addressed. Contingency plans, for each identified scenario, were made, in order to evaluate probable losses that constitute an input for the reliability modeling. Therefore, two additional software’s were used for their validation: - Pipeline studio – TGNET (from Energy Solutions), to check pressure conditions; and an in-house Petrobras software (PLANAGE), used for best gas allocation purposes. Experienced operational team opinion was incorporated on that contingency plans elaboration. This work constitutes a powerful tool for Petrobras planning and optimization of gas supply chain future configurations. The simulation provides propositions for investments prioritization, based on cost benefit analysis, and as a by-product, contingency plans, that were developed, considering different failure scenarios. 1. Introduction

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PETROBRAS together with DNV has performed an analysis of the Brazilian integrated gas supply network for the 2010 period. The main objective of the study was to evaluate the following points:

• Overall gas network supply efficiency; • The average number and duration of incurred supply shortfalls for each customer over the

considered period; • Contingency plans based on critical failure scenarios; • Key contributors to the incurred supply shortfalls; • Recommendations for network performance improvement.

The performance of the Petrobras integrated gas supply network has been quantified in terms of gas supply network efficiency, which is defined as the ratio between achieved gas supply and the forecasted gas demand. The achieved gas supply includes random failures and scheduled maintenances.

100customers) (all Demand Gas

customers) (allSupply Gas Achieved Efficiency ProductionNetwork ×=

Gas supply network efficiency, as opposed to availability, is used since the concept of availability more readily describes systems that exist in binary states (i.e. functioning or non-functioning), thus availability refers to the proportion of time that the system can perform its intended function. In addition, availability does not take into account aspects such as production boosting, buffering etc. Please note that for a system that is demand driven, the potential production is limited by demand and the gas sales agreement.

Gas supply network efficiency fully accounts for periods of degraded operation resulting from equipment failures and product sales profiles, boosting, etc. In addition the performance of the gas supply network is also quantified in terms of the production efficiency, the frequency and duration (average, minimum and maximum times) of shortfalls of gas supply to the specific consumers or group of consumers.

This study was made, based on the configuration conceived for 1st trimester 2011, as well as offer and demand profiles related to that period of time.

The whole supply chain performance was tracked, from gas sources, through chain components (gas processing units, compressor stations, pipelines, city-gates) to final consumers. Main contributors to losses and ‘bottle-necks’ were highlighted. Reliability modeled considered detailed equipment failure rates, associated time to repair and maintenance and inspection policies.

This study incorporated reliability modeling that has been previously developed for more than fifteen (15) individual gas processing units that compose Brazilian gas supply chain.

Brazilian integrated gas network is supplied from different gas sources:

(1)

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- from exploration and production fields (E&P sector:- UN-RNCE, E&P UN-SEAL, E&P UN-BA, E&P UN-ES, E&P UN-BC, E&P UN-RIO, E&P UN-BS ) and from GASBOL and a LNG vessels, located in Pecém and Rio de Janeiro.

Those suppliers feed several power plants and city-gates, which deliver gas for distribution

companies: Northeast: BAHIAGAS, SERGAS, ALGAS, POTIGAS, PBGAS, COPERGAS and CEGAS. Southeast: CEG, CEG-RIO, COMGAS, CEMIG and BR (ES).

Gas that comes from E&P sector is treated in gas processing units before supplying gas to

several customers, as mentioned above. Part of this gas is consumed by those plants for internal use and absorption purposes.

That analysis was developed for updated gas supply and demand profiles, as provided by PETROBRAS and gas delivery allocation was made, utilizing an optimization software, developed in house (named PLANAGE), that provides the maximization of revenues, considering financial aspects and logistic restrictions.

Thermo fluid hydraulic software Pipeline Studio – TGNET (developed by Energy Solutions)

was used for gas network simulations, taking into account steady state and transient state, which was based on main failure scenarios. The software allowed the evaluation and validation of best allocation, verifying pressure and delivery conditions for final consumers. Results provided by PLANAGE and TGNET runs, are then used as input for reliability modeling.

2. About Reliability Modeling Software - TARO

This analysis was performed using the software TARO - Total Asset Review & Optimization, developed by Jardine Technologies, a British company. TARO enables raw units data (e.g. configuration, equipment reliability, etc.) to be integrated into a coherent computer simulation model of each Unit, e.g., a “virtual plant”. This model can then be analyzed, parameters altered, performance impact assessed to enable optimizations of key performance drives.

At its core, TARO functions in a similar way to Jardine’s MAROS software (well known in the upstream oil & gas sector), by creating typical life-cycle scenarios of proposed systems, employing event driven simulation techniques. The main strength of TARO is the ability to handle multiple feedstock and product streams.

Briefly, a life-cycle scenario is a chronological sequence of events which typify the behavior of a system in real-time. TARO can create an infinite number of such scenarios for any given system, each one being unique, yet sharing the commonality of being a feasible representation of how the system will behave in practice. Events are the fundamental occurrences within a system's life, which determine the effectiveness of the system. The events are generated from elements data using random sampling techniques. The elements data comprise equipment failure data, planned activities and its logical operation.

Before life-cycle scenarios can be created for a system, it is necessary for TARO to understand some basic system details; this is achieved via a system logic model, which identifies the main elements of the system and their configuration, e.g. series, parallel, standby, etc.

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This simulation technique is commonly referred to as a direct simulation method. The digital system model moves from one distinct state to another governed by the occurrence of a sequence of events. The state of the model at any point in (simulated) time is represented by a series of variables; as each new event occurs, one or more of the variables representing the system changes. The progress of the simulation is in steps, from the occurrence of one event to the occurrence of the next until the simulated time exceeds the specified design life of the system being modeled.

3. Brazilian Network and new enterprises

Brazilian gas network study boundaries were established considering gas supply sources and delivery points to consumers (including city-gates). The network is shown in Figure 1.

The following gas network facilities were modeled in the study: • Natural gas processing units • All pipelines and shutdown valves • Gas compression stations • Existing and new city gates

The following new enterprises were considered for December 2010 configuration: • LNG facility at PECÉM and Baia de Guanabara • FAFEN-SERGAS branch, GASCAC Pipeline and others new pipelines • Jundia, Catu, Prado, Taubaté and Guararema Compression Stations • Modernization of existing city-gates

Figure 1. Brazilian Integrated Network Mesh

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4. General Assumptions and Shedding Priority

In the occurrence of gas supply shortages, gas supply is managed by reducing flow to customers that present the lowest supply priority addressed. Supply priority definition is based on potential loss of income and on penalties incurred, as defined in contract agreements. Shedding priority list is presented on the Table 1 below.

Shedding priority defines that interruptive gas parcels are the first to be shed, in case of the

occurrence of shortfalls.

For gas allocation purposes, an optimization software (developed in house, named PLANAGE) was used, which provides the best possible allocation for gas delivery, based on the maximization of revenues, considering financial aspects and logistic restrictions.

Priority Gas Supply Contract

1 Interruptive demand (Gas distribution Companies) 2 Flexible demand (PETROBRAS Refineries) 3 Thermal demand (Thermal Electric Units) 4 Flexible demand (Gas distribution Companies) 5 Inflexible demand (PETROBRAS Refineries / Gas

distribution Companies)

Table 1- Shedding Priority (Highest priority to shed shown first)

That software considers offer profile, logistics configuration and restrictions, volumes to be delivered to thermo generation units (with priority levels previously defined), commodity prices, gas production costs, LNG costs, contracts and penalties, due to contract shortfalls.

Every identified failure scenarios (gas sources, processing units, compressor stations

failures, etc.), was simulated and best gas delivery allocation made. All possible maneuvers were considered, based on experienced operational team opinion.

Then, TGNET (thermo-fluid-dynamic software) was used to validate allocation results, checking if the minimum required delivery pressures and conditions were accomplished.

As an example, in case of occurrence of compression station loss, TGNET provides the evaluation of pressure reduction along gas pipelines, on a time basis. It also provides the evaluation of the period of time, that the network will be able to sustain minimum delivery pressures for final consumers.

Failure impacts were evaluated from TGNET simulations in terms of time and volumes to be shed, considering the existing line packing. Losses (gas reduction or total shed to consumers) can then, be evaluated.

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5. Assumptions

The following assumptions were made:

1. Failure data were collected based on operational experience and from international reliability data banks;

2. Line packing was considered on pipeline modeling; maximum line packing capacities were calculated using TGNET;

3. Gas processing units and compression stations were modeled in TARO in a detailed way (all equipment failure modes and time to repair);

4. Maintenance, spare parts and inspection policies were considered; 5. Failure data quantification for pipeline shutdown valves (SDVs) was based on in house

historical data and from EGIG data bank (European Gas pipeline Incident data Group); 6. City-gates were divided into groups, according to their configuration and modeled as

standard models for each of them; 7. LNG vessel at Pecém was modeled in TARO, considering its maximum capacity

(7MMsm3/day) with an estimated availability value of 99%; 8. Shed criterion was applied whenever a failure occurs; according to the specific failure

scenario, several consumers could simultaneously be shed or have their flow reduced.

6. Contingency Plans

Contingency plans were elaborated for failure scenarios, in a detailed manner, during the study. Those scenarios, and the associated contingency plans, were identified and validated by an experienced team, composed by engineers and operators from different PETROBRAS sectors. The key issue of concern was the analysis of possible undesired scenarios that could imply on contract shortfalls, the evaluation of possible maneuvers, taking into account best gas delivery allocation. Different softwares, as mentioned above, were used for the simulation of best gas supply allocation and for checking delivery pressure and conditions for final consumers.

Contingency plans, elaborated for failure scenarios, provide among other issues, time and

volumes to be shed. Those parameters compose input formally used for gas network reliability studies. Figures 2 and 3 below illustrate some of the contingency plans that were made, considering basic cases and failure scenarios.

Those studies are being developed by Petrobras, in order to evaluate gas chain security of

supply, pointing out its vulnerable points (´bottle-necks´) and proposing optimization measures to be adopted.

Complex reliability models are used to model gas supply network, from gas sources to final

consumers, tracking events or failures scenarios that could occur and result on undesired losses, contract shortfalls, and penalties.

For each identified failure mode, probability density functions are addressed, as well as time

to repair distributions. Impacts, expressed in terms of gas volumes losses, were also evaluated. That evaluation was made, considering, besides other variables, all possible operation

maneuvers.

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Figure 2 - Contingency Plan – Basic Case

Figure – 3 – Contingency Plan – Failure Scenario

The ability of being capable of dealing with undesired or crisis scenarios, based on suitable

anticipation levels, is, nowadays, a highly valuable attribute to be presented by competitive corporations, for best crisis management and prompt recovery response. Contingency plans, compose a by-project of the reliability gas supply study. It is being used by Gas & Power Gas Operation Control Center, and constitute an essential input for reliability modeling of gas supply chain. 7. Failure and Repair Data

→→→→ Processing Unit (UPGN 1) Failure:

Area 2

UPGN 3

INJECTION

RIC

H G

AS

Refinery 2 (725)

POLO

City-gate(35)

(1250)

(400)

(210)

(1870)

(1096)

(3176)

SC

OM

P A

Comp. / Inj. / G. Lift

UPGN 1

(2659)

Refin

ery1

City-gate

RAQ

City-gate

City-g

ate(350)

UPGN 2 Area 3

Field 1

Area 1

UTE 1

UTE 2

(0)

City-gates

ED

G UPGN 4

Internal Consumer

All values are considered in Msm3/day.

ETCC

Valve Limit (A) = 14040 Msm3/day

Valve Limit (B) = 1404 Msm3/day

Valve Limit (C) = 936 Msm3/day

Valve Limit (D) = 936 Msm3/day

(984

)

(456

)

(280

8)

(0)

(0)

(850)

(382)

(245)

(85)

(1380)

(1849)

(220)(220)

(1384)

(914)

(30)

(0)

(2735)

(0)

(41)

(41)

(1404)

(1404)

(128)

Interconnection

(max. 1500)(120)

(140

4)

(2808)

(140

4)(9

36)

(468) (75)

COMPR

G. LIFT(1956)

(0)

(A)

(B)

(D)

(C)

SC

OM

P B

Max.

5600

Max.

2000

ED

G A

(150)

Max.

6600

closed

Field 2Field 3

Field 4

→→→→ Basic Case:

Area 2

UPGN 3

INJECTION

RIC

H G

AS

Refinery 2 (1405)

POLO

City-gate(35)

(1250)

(400)

(210)

(1870)

(1096)

(3176)

SCOM

P A

Comp. / Inj. / G. Lift

UPGN 1

(659)

Refin

ery1

City-gate

RAQ

City-gate

City-gate

(350)

UPGN 2 Area 3

Field 1

Area 1

UTE 1

UTE 2

(5037)

City-gates

EDG UPGN 4

Internal Consumer

All values are considered in Msm3/day.

ETCC

Valve Limit (A) = 14040 Msm3/day

Valve Limit (B) = 1404 Msm3/day

Valve Limit (C) = 936 Msm3/day

Valve Limit (D) = 936 Msm3/day

(984

)

(456

)

(251

8)

(0)

(0)

(850)

(672)

(245)

(85)

(1380)

(1559)

(400)(400)

(914)

(914)

(30)

(4077)

(2735)

(200

0)

(41)

(41)

(1404)

(1404)

(800)

Interconnection

(max. 1500)(120)

(140

4)

(7555)

(111

4)(9

36)

(178) (75)

COMPRG. LIFT

(2211)

(960

)

(A)

(B)

(D)

(C)

SCOM

P B

Max.

5600

Max.

2000

EDG

A

(30)

Max.

6600

closed

Field 2Field 3

Field 4

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Failure and repair data were obtained from operational historical reports (from 2006 to August of 2009) and from well known international data banks, as OREDA, SINTEF, EGIG.

For each one of the assets to be studied, there was a formal commitment from local

managers and from operation and maintenance teams, in order to provide information concerning failure and repair data, during regular appointments that occur according to a scheduled previously agreed.

Before those appointments happen, all the information, documents and data were previously

analyzed by the reliability team, and after brain storming with the asset team, ‘in loco’, data colleted were statistically treated and improved.

The variable mean time to repair (MTTR) was collected, considering specific logistics,

recommended maintenance intervals, the existence of spare parts, as well as repair and maintenance technical support.

8. Results and Conclusions

TARO results show an average production efficiency of 99.50% for overall integrated Gas

network.

Figure 4 below presents main contributors for efficiency losses. Major contributors to network losses refer to City-Gates (36,1 %) followed by gas gas processing units (26,1 %).

Local and network inserted average efficiencies of gas processing units are shown in Figure

5 below. Local efficiency was calculated considering individual unit performance, e.g., as an

individual player in the whole gas supply chain. Then, unit performance was evaluated, considering its inclusion on network modeling and its performance under offer and demand profile conditions.

The reliability performance of each one of the processing plant units, that compose Brazilian

supply chain, was evaluated and compared. As an example, for one of the processing plants, average local efficiency calculated value was 96.98%.

Figure 6 presents critical elements for one of the processing gas units analyzed. Those

represent the ones that most contribute to local efficiency loss. Turnaround policies, valves, re-boiler, horizontal vessel, external supply energy and furnace failures compose critical elements or major contributors to losses.

Reliability modeling was performed in a detailed manner for every processing units,

compression station, city-gate and pipeline that constitute gas supply chain.

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Figure 4 - Main Contributors (per asset type) to the Efficiency Losses - Brazilian Network 2010

30%

40%

50%

60%

70%

80%

90%

100%

Annual Network Average Efficiency (%) 98,83% 98,04% 90,75% 98,54% 96,75% 97,25% 95,65% 98,10%

Annual Local Average Efficiency (%) 96,98% 98,66% 97,04% 98,83% 96,77% 99,96% 97,96% 98,28%

Scheduled FUT (%) 76,32% 100,00% 100,00% 75,57% 77,83% 48,02% 46,00% 78,51%

UPGN 1 (DPP) *

UPGN 2 UPGN 3 UPGN 4 UPGN 5 UPGN 6 UPGN 7 UPGN 8

*DPP = Dew Point Plant

Figure 5 - Values of Average Productive Efficiency and FUT of Processing Units

As mentioned above, major contributors to overall Brazilian network supply chain losses are associated to city-gates, followed by the gas processing units and gas supply sources. Improvements should be implemented in order to reduce those losses.

Falha de UPGN3.66E+04

26.1%

Falha de Fontes2.61E+04

18.6%

Falha de Gasoduto1.75E+04

12.5%Falha de GNL7.77E+03

5.5%Falha de Gasbol

1.41E+031.0%

Falha da Compressão4.39E+02

0.3%

Falha da City-Gate5.06E+04

36.1%

Processing Units 26,1%

Gas Sources 1

18,6%

Gas Pipeline 12,5%

City-Gates

36,1%

Compression Units 0,3%

Gas Sources 2 1,0%

LGN 5,5%

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Inlet valve0,03%1,06%

Flame Detectors0,03%0,84%

Horizontal Cold Vessel0,02%0,82%

Others0,38%

12,67%

V-513501 Control Valve0,03%0,83%

V-123101 Two Phases Vertical Vessel0,02%0,82%

P-123103 Temperature Sensor0,02%0,81%

Valves0,30%9,89%

Turnaround0,61%20,33%

V-123101 Three Phases Horizontal Vessel0,02%0,82%

T-Z-123301 Reboiler0,25%8,37%

Horizontal Vessel0,17%5,77%

Energy External Supply0,16%5,13%

Furnace0,15%4,97%

Glycol flash vessel0,11%3,52%

Glycol Heater0,11%3,53%

Gas Heater0,11%3,71%

TZ-123301 Top Condenser0,12%3,84%

P-123102 Temperature Sensor0,03%0,83%

V-123204 Sensor Level0,05%1,73%

Gas Exchanger0,04%1,35%

Pressure Sensor0,05%1,66%

TEG Dehydration Tower0,06%2,05%

TEG Recuperation Tower0,07%2,30%

Propane Refrigerator0,07%2,37%

Figure 6 - Critical Elements of UPGN 1

After the identification of critical contributors to losses, a sensitivity analysis was elaborated in order to evaluate the efficiency gains that improvement measures could provide.

For each one of the proposed improvement measures, TARO software was used and cost benefit analysis was carried out, considering investment costs, associated with the proposed measure, revenue losses and penalties.

Cost benefit analysis is being used with the purpose of prioritizing investments, promoting best allocation of resources and loss reduction.

It is important to highlight that the reliability modeling of 2010 gas supply chain requires, as input, lots of information and also comittment from different Petrobras and Transpetro sectors. Therefore, that study constitutes, besides a powerful tool for decision making processes, a way of positive integration through all Petrobras gas supply chain.

Every proposed optimization measure was carefully evaluated by the design team and by all gas supply chain players, as there are different asset owners in Petrobras system, as well as sectors, responsible for the operation of each asset that compose the chain (for example, Exploration and Production and Refinery Departments, as well as Transpetro, are responsible for gas processing units operation).

Those measures were negotiated in a high management level, during regular appointments that gather all gas supply players.

SUBTITLE: Element Relative Loss (%) Absolute Loss (%)

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Those kind of studies promote the verification, between players, of all documentation, data, operational assumptions and procedures that could be used during normal and failure situations, as well as, a better comprehension and training of operational team and design engineers.

Contingency plans that were intended merely to be an input for reliability study, have now turned into a tool, systematically requested by control centre operational team, as a powerful support for decision making and for the establishment of strategies, based on the anticipation of failure scenarios.

The purpose is to carry out those kind studies in a continuous and systemic manner, thinking about future gas network design and optimized operation performance. 9. Acknowledgements

The authors would like to thank to all the people, listed below, who have contributed to this paper:

o Homero Fenner Filho, Leonardo Ayres, José Luiz Andrade, Gustavo Mauricio, Marco do Valle (PETROBRAS)

o Marcos Bruno, Andre Franca, (TRANSPETRO) o Thomas de Carvalho, Luiz Pires (Pontífice Universidade Católica)

10. References

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, RPR062/2003, October 2007.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Atalaia, Review1, RPR098/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Catu, Review1, RPR099/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Manati, Review1, RPR100/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Pilar, Review1, RPR101/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report s – Lubnor, Review1, RPR102/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Candeias, Review1, RPR103/2007, January 2008.

• DNV, Reliability Analysis of the Processing Units that supplies the Northeast Network, Modeling and Results Report – Guamaré, Review1, RPR104/2007, January 2008.

• Jardine - DNV, Security of Supply Analysis of Northeast Network, Doc no 0524, July 2004.

• Jardine and Associates Ltd., Software MAROS, version 8.00.04, 2007 • Jardine and Associates Ltd , software TARO, v,rsion 4.00.08y, 2008 • OREDA, Offshore Reliability Data Handbook, Published by OREDA Participants,

Prepared by SINTEF Industrial Management and Distributed by Det Norske Veritas, 2rd Edition, 1992.

• OREDA, Offshore Reliability Data Handbook, Published by OREDA Participants, Prepared by SINTEF Industrial Management and Distributed by Det Norske Veritas, 4rd Edition, 2002.

• SINTEF Technology and Society, Reliability Data for Safety Instrumented Systems - PDS Data Handbook, Trondheim, Norway, 2006.

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• IEEE Guide to the Collection and Presentation of Electrical, Electronic, Sensing Component, and Mechanical Equipment Reliability Data for Nuclear-Power Generating Stations, Std 500-1984, New York, 1983.

• PETROBRAS, Software PLANAGE, 2009. • Hollnagel, E., ‘, Barriers and Accident Prevention Woods, Ashgate, USA, 2004 • Faertes, D.; Heil, L.; SAKER, L. F.; Vieira, F.; Risi, F.; Domingues, J.; Alvarenga, T.;

Mussel, P.; Petrobras Northeast Gas Security of Supply Study, RPC 2009 – Rio Pipeline Conference, Rio de Janeiro – RJ, 2009.

• Faertes, D.; SAKER, L. F.; Heil, L.; Vieira, F.; GALVÃO, J.; Gás Allocation Plans Base on Failures Scenarios – PETROBRAS – Gas & Power Sector, RPC 2009 – Rio Pipeline Conference, Rio de Janeiro – RJ, 2009.