Production performance of an offshore system by applying AltaRica

20e Congrès de maîtrise des risques et de sûreté de fonctionnement - Saint-Malo 11-13 octobre 2016

Disponibilité de production du système en mer en utilisant AltaRica 3.0 Production performance of an offshore system by applying AltaRica 3.0

Benjamin Aupetit IRT SystemX 91120 Palaiseau benjamin.aupetit@ irt-systemx.fr

Leïla KLOUL DAVID Université de Versailles St-Quentin-en-Yvelines [email protected]

Huixing MENG LIX Ecole Polytechnique huixing.meng@ polytechnique.edu

Antoine RAUZY IPK Norwegian University of Science and Technology [email protected]

Résumé Dans cet article, nous présentons une étude destinée à évaluer la disponibilité d'une unité flottante de production, de stockage et de déchargement (FPSO: Floating Production Storage and Offloading unit) réalisée avec le langage de modélisation AltaRica 3.0. L'objectif principal de notre travail était d'identifier les paramètres sensibles et les composants critiques à l'égard de la disponibilité de la production. Prendre en compte les paramètres sensibles et s’assurer du bon fonctionnement des composants critiques peut permettre de garantir une disponibilité et une production élevées du système de production offshore. Summary In this article, we show the application of the AltaRica 3.0 modeling language for assessing the production availability of a Floating Production Storage and Offloading system (FPSO). The main objective of our study is to identify the sensitive parameters and crucial components with respect to the production availability. Taking care of the sensitive parameters and ensuring the well working status of crucial components can guarantee a relative high production availability of the offshore production system.

1 Introduction

The huge amount of the offshore oil and gas are promising to meet the increasing worldwide energy demand. However, the offshore operational activities are surrounded by an incidents-prone environment, which includes a harsh natural environment (offshore wind, wave, current and hydrate, high pressure, high/low temperatures) and complex working processes (complicated systems and procedures).

The offshore oil and gas activities are always accompanied with a high risk. First, the low probability/high consequence incidents, like severe accidents (fire, explosion, blowout, leakage, collision...), are constantly attracting high attention of the stakeholders from both industry and society domains. Second, the high probability/low consequence incidents (Signoret, 2010), like production losses, are of frequent occurences, which also require further focus from the stakeholders of the production systems.

The offshore production is an important stage in the offshore development processes. The objective of the offshore production process is to transport and preproccess the crude oil and gas from the platforms or wells to the onshore, for further storage or refinery. Because of the dynamic and complex working conditions, the offshore production operations are facing shifting production outputs and losses. The FPSO (Floating Production Storage and Offloading) system is one of the most commercially viable systems in offshore production activities (Shimamura, 2002).

In this paper, we are interested in production availability analysis of FPSO system. Indeed, production availability is a feasible measurement for evaluating the changing production yields for production systems. It is defined as the ratio of the actual production to the planned production (field capacity), over a specified period of time (Aven, 1987; NORSOK, 1988 ; ISO, 2008). Production availability can combine both RAM (Reliability, Availability and Maintainability) indicators and production expectations.

We choose AltaRica 3.0 as the modeling language for this study. AltaRica is a modeling language devoted to performance evaluation and safety analyses. It is currently employed as internal representation language by several safety analyses workshops: Cecilia OCAS (Dassault Aviation), Simfia (EADS Apsys), Safety Designer (Dassault Systemes) and AltaRica Studio (LaBRI) (Lipaczewski et al., 2015). AltaRica data-flow, the 2.0 version of this language, has already been used for evaluating the production availability of a designed production case (Boiteau et al., 2006). Unlike AltaRica 2.0, which is an object-oriented language, AltaRica 3.0 is a prototype-oriented language (Prosvirnova et al., 2013; Batteux et al., 2015). Moreover, the mathematical foundations of AltaRica 2.0 and 3.0 are different. The former is based on the mode automata (Rauzy, 2002) and the latter on the guarded transition systems (GTS) (Rauzy, 2008). GTS makes the new version of the language possible to handle systems with instant loops and to define acausal components.

AltaRica 3.0 can attain the goal that one (input) model can be analyzed using several tools. Indeed, there are several assessment tools which could serve for AltaRica 3.0 model. The tools include Markov chain generator, Fault tree compiler, Stepwise simulator, and the Stochastic simulator, which is currently the most powerful one, especially when the other tools cannot work (Lipaczewski et al., 2015). Indeed, the stochastic simulation is an important tool for safety and reliability analyses of the systems, which could generate reasonable results for safety and reliability indicators (Batteux et al., 2013 ; Zio, 2013). The underlying principle of stochastic simulator in AltaRica environment is to run many pseudo-random simulations, equivalent to the histories of the behaviors of the system, and finally carry out statistics on them (Batteux et al., 2013).

In this article, our work covers two folds: first, we assess the production availability of the offshore systems using AltaRica 3.0; second, we assess the sensitiveness of the production availability to the related parameters (failure rates, repair rates) and the importance of the system components, in view of the production availability of the production system.

The remainder of this article is as follows. We discuss the related works in the next section. Afterwards, we briefly introduce the FPSO system. Then, we present the AltaRica 3.0 model of the system. Finally, we discuss the sensitivity analysis results we obtained and conclude the article.

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2 Related works

In the realm of the production perfromance studies, NORSOK Z-016 (NORSOK, 1988) and ISO 20815 (ISO, 2008) are two important standards, which propose the general framework to conduct production availability assessment. Production availability has been assessed using several techniques, such as Monte Carlo simulation (Zio et al., 2006; Chang et al., 2010), Stochastic Petri nets (Bris et al., 2006; Meng et al., 2015), and AltaRica data-flow (Boiteau et al., 2006).

There are several programs available for availability assessment of production systems. Maros (Maintainability, Availability, Reliability, Operability Simulation) is a software distributed by DNV˖GL for RAM analysis in upstream oil and gas industry. GRIF (GRaphical Interface for reliability Forecasting) is a software platform distributed by TOTAL/SATODEV for determining essential indicators of dependability. The simulation package of GRIF, which includes Petro, BStok and Petri modules, can be employed for production-performance analyses.

Maros and GRIF/BStok (Stochastic block diagrams or RBD (reliability block diagram) driven Petri Nets)) are compared (Buvry et al., 2015). The results show that both tools can generate similar results in light of production performance, which meet the industrial needs. A drawback of Maros is the “black box” aspect that results from it. BStok, due to the underlying Petri nets, can provide more modeling flexibility in exchange of higher expertise (Buvry et al., 2015). Bstok can only be used for modeling system with one flow type, such as only one oil flow (hence no gas and water) in a production system. Petro, a production block diagram module in GRIF, is also based on stochastic block diagrams. But Petro can analyze systems with multiple types of flows. Bstok and Petro both employ the same simulation engine (Moca-RP), and can be exported into equivalent Petri nets.

The low production availability could directly influence the profitability of the production facilities, especially in a low oil price era. Thus it is necessary to identify the sensitive parameters and crucial components regarding the production availability of the production systems. The sensitivity analysis methods, like Birnhaum's measure, improvement potential, risk achievement worth, risk reduction worth, criticality importance and Fussell-Vesely's measure (Rausand et al., 2004) can provide ideas to conduct the sensitivity analysis for the production availability.

There are few reports related to the production availability analysis of FPSO (Meng et al., 2015). However, several studies have been conducted about the production availability of the offshore production system (Zio et al., 2006 ; Bris et al., 2006; Chang et al., 2010). Several sensitivity studies related to FPSO are considering the structural reliability, like the hull girder reliability (Chen, 2016). A sensitivity analysis is conducted to predict the relative motion and probability of contact between FPSO and shuttle tanker (Chen et al., 2004). However, there are few sensitivity analysis related to the system reliability of FPSO.

3 Case study

The FPSO system in our case study is realistic. It is serving in an offshore oil field in South China Sea with the water depth of 90m. This FPSO includes the crude oil processing system, a single point mooring system, a crude oil storage and ballast system, a fire protection and lifesaving system, and a power and instrumental system (CNOOC, 2007). We focus here on the production availability of crude oil processing system. For the sake of simplicity, we shall still call it the FPSO system. The FPSO system consists of four sub-systems: platforms A, B, and C, and the FPSO subsystem, as shown in Figure 1. Platform A transfers the oil to a buffer tank in plaform B. Together with the output oil of platforms B and C, the overall oil is transported to the heat exchangers on the FPSO subsystem. In Figure 1, the arrows with solid lines represent the main streams of the system, the crude oil flows, in which we are interested. The dashed arrows stand for the flows of separated gas or waste water. The three platforms work in parallel, and are relatively independent. The required data (expected crude oil outputs, preventive maintenance intervals/durations and failure/repair rates) are available in (Meng et al., 2015).

Figure 1. The flow diagram of the FPSO production system.



4 The AltaRica model of the FPSO system

Figure 2 reveals the component behaviors in the FPSO production system. There are 19 components in the FPSO system, which can be classified into three groups according to their behaviors : repairable components, repairable components with preventive maintenance, standby components with corrective maintenance.

The repairable components have three states, which are WORKING, FAILED, UNDER_REPAIR. The component is initially in WORKING state. Once a failure occured, the component turns into FAILED state. If a corrective repair crew is available, the component state becomes UNDER_REPAIR. Subsequently, the component returns to WORKING state until the repair operation is completed.

The repairable components with preventive maintenance adds preventive maintenance to repairable components. Once a preventive interval is reached and a preventive maintenance crew is available, the component becomes UNDER_MAINTENANCE. The component returns to WORKING state until the preventive maintenance operation is completed.

The standby components with corrective maintenance adds the STANDBY state to repairable components.The initial state of the component is STANDBY. When there is a demand, the component becomes WORKING. After repair, the component returns to STANDBY state. Note that here we assume that there are no failure on demand (from STANDBY to FAILED with a demand) and stop (from WORKING to STANDBY) actions.

Figure 2. Component behaviors in the FPSO production system.

We take the platforms as example to show how to model FPSO system in AltaRica. First, we propose AltaRica classes for modeling platforms (Figure 3). The repairable components with preventive maintenance and repairable components are established in one class. When the Boolean parameter PreventiveMaintenance is true, the class is repairable components with preventive maintenance. Otherwise, the class describes the repairable components. Second, we model the three platforms by instantiating the classes (Figure 4). The flows are connected according to the flow diagram in Figure 1. Eventually, the flows from the three platforms are merged together and sent to the FPSO subsystem.



Figure 3. The AltaRica classes for modeling platforms.

We model the component behaviors by instantiating the predefined classes, and connecting the components as we did in modeling platforms. Finally, we obtain the whole AltaRica model, which includes 38 state variables, 72 flow variables and 109 transitions.

Now we can compute the production availability in the AltaRica model: First, we define the observer (quantities to be observed, defined in AltaRica code) in the AltaRica model. Second, we compile the AltaRica model into the guarded transition systems (GTS) model. Third, we employ the stochastic simulator to analyze the GTS model. Finally, we obtain the production availability from the results of the simulations.



Figure 4. The AltaRica code for modeling platforms.

5 Sensitivity analysis of the FPSO production availability

Based on the constructed AltaRica 3.0 model, we carried out the sensitivity analysis to determine the parameters (failure rates and repair rates) to which the system is sensitive and crucial components of the FPSO system, in view of its production availability. In the experiments, we consider 87,600h (10 years) as the mission time and 20,000 as the simulation histories (Monte Carlo simulations).

5.1 Sensitivity analysis of the system parameters

The production availability of the FPSO system is mainly affected by the failure rates and repair rates of the production system. The failure rates and repair rates used in the experiments are multiplied with the practical values and a coefficient in {0.5,1.0,1.5,2.0}.

Figure 5 shows the production availabilities of the FPSO system under different failure and repair rates. The results show that the production availability of the FPSO system decreases with the increase of the failure rates. Simultaneously, the production availability of the FPSO system rises with the enhancement of the repair rates. The benchmark point (second point on blue line with circle) of our test is the time when both the failure rate and repair rate multiply by 1.0. The production availability of the production system at this point is 0.9447, which shows that the system holds a relative high production availability.

Our experiments denote that when the repair and failure rates are high, the production availability changes little with the variations. Whereas, when both parameters are low, the production availability varies a lot once the parameters are modified. Take the blue line as an example (failure rate : 1.0λ), when the repair rate grows from 0.5µ to 1.0µ, the production availability increases with a rate 5.83 %. However, the production availability improves with the rates 1.63% and 0.76% in the two subsequent intervals.



Figure 5. Production availabilities of the FPSO system under different levels of failure and repair rates.

5.2 Crucial components for the production availability

By identifying the crucial components for the production availability, the stakeholders of the production system can adopt effective measures to maintain the system at a relatively high production availability. We determine the crucial components of the FPSO system by varying the failure and repair rates of each component, separately. The experiments can partially be mutually validated.

Figure 6 depicts the production availabilities of the FPSO system when alternating the failure rates of each component on the platforms. The production availability of the FPSO system decreases along with the increase of the failure rates of each component. Clearly, EPB1, EPC, EPA and EPB2 have strong influence on the production availability of the FPSO system.

Figure 7 shows the production availabilities of the FPSO system when changing the failure rates of each component on the FPSO subsystem. The curves in Figure 7 share the same tendency with those in Figure 6. Component SWC has heavy impact on the production availability of the FPSO system.

From a global view, as it is shown in Figure 8, components EPB1, SWC, EPC, EPA and EPB2 (EPB1 > SWC ≈ EPC > EPA ≈ EPB2) show important but decreasing influence on the production availability of the FPSO system.

Figure 6. Production availabilities of the FPSO system under different levels of failure rates (platforms).

Figure 7. Production availabilities of the FPSO system under different levels of failure rates (FPSO subsystem).



Figure 8. Production availabilities of the FPSO system under different levels of failure rates.

Figure 9 presents the production availabilities of the FPSO system when modifying the repair rates of each component on theplatform. The production availability of the FPSO system rises along with the increase of the repair rates of each component. Once again, we can see that components EPB1, EPC, EPB2 and EPA have strong influence on the production availability of theFPSO system. The results in Figure 9 can validate those in Figure 6.

Figure 10 exhibits the production availabilities of the FPSO system when alternating the repair rates of each component on theFPSO subsystem. The curves in Figure 10 share the same trend with those in Figure 9. Component SWC has important impact on the production availability of the FPSO system. The outcome from Figure 10 supports the one in Figure 7.

Figure 9. Production availabilities of the FPSO system under different levels of repair rates (platforms).

Figure 10. Production availabilities of the FPSO system under different levels of repair rates (FPSO subsystem).

Finally, the results in Figure 11 validate the results in Figure 8 as they show that components EPB1, SWC, EPC, EPA and EPB2 (EPB1 > EPC ≈ SWC > EPA ≈ EPB2) have a strong impact, which decreases as the repaire rate increases.



Figure 11. Production availabilities of the FPSO system under different levels of repair rates.

6 Discussion

Maros and GRIF provide graphic formalisms, which are very convenient to show partial views of the model (Rauzy, 2008). Maros and GRIF support The drawing of graphic models can convey complex system in a simple manner, but they cannot capture all aspects of the model (Rauzy, 2008). For instance, we cannot judge directly from the block diagrams in GRIF/Petro, whether the components are perfect or repairable. However, the textual modeling languages can describe the system behaviors in detail by codes. The hierarchy way and object-oriented design of the textual languages can improve the readability of the codes.

Maros and GRIF/Petro provide the libraries for selecting appropriate samples during modeling the specific systems. Such libraries prevent giving blank for the modelers, thus can easy the modeling processes. However, AltaRica 3.0 has not yet provided such a library for evaluating the performance of the systems in process industry, which is exactly our next-step work.

The main difficulties we encountered in the AltaRica model of FPSO system are: firstly to reasonably describe the system behaviors in AltaRica language; secondly to show the model (classes and blocks) in an explicit hierarchy way.

Actually, there is no such an acceptable level of production availability amongest oil and gas specialists. However, there are requirements of production performance from the stakholders at the design/operation phases. In literatures (Zio et al., 2006 ; Chang et al., 2010 ; Buvry et al., 2015), the production availability of the systems are 0.88~0.97. When computing the production availability, it needs to firgure out the components behaviors (failure/repair rates, preventive maintenance intervals/durations, structural relationship), production inflows, as well as expected productions in AltaRica model.

The result of production availability here (0.9447, benchmark point) is comparable with the Stochastic Petri nets model of the same FPSO system in Reference (Meng et al., 2015), where is 0.9636. The percentage change is (0.9447-0.9636)/0.9636=-1.96%, which shows that we obtain nearly the equal results from the Petri module in GRIF tool and the AltaRica 3.0. Stochastic Petri nets are difficult to master when the system under study becomes complex (Meng et al., 2015). And AltaRica 3.0 language is powerful in terms of modeling flexibility and understandability (Meng et al., 2015), which is convenient to update and revise.

7 Conclusion

In this paper, we aim at determining the sensitive parameters and crucial components of the FPSO system by conducting the sensitivity analysis, in view of the production availability. Our results show that both failure rates and repair rates exert heavy influences on the production availability of the FPSO system. Moreover, the failure and repair rates with high values (1.0~2.0 times of the initial values) impose lighter impact on the production availability than the small values (0.5~1.0 times of the initial values). The results also reveal that components SWC (Sea Water Cooler), EPB1 (Efflux Pump 1 on platform B), EPC (Efflux Pump on platform C), EPA (Efflux Pump on platform A) and EPB2 (Efflux Pumps 2 on platform B) have significant influence on the production availability of the FPSO system. Thus the stakeholders of the FPSO system could take measures to improve or hold a relative high production availability level. We show that AltaRica 3.0 language allows us to evaluate the performance of the offshore production system.



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Production performance of an offshore system by applying AltaRica