DEVELOPMENT OF A SIMULATION TOOL TO ASSESS A PETROLEUM COMPANY SALES & OPERATION PLANNING

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1 DEVELOPMENT OF A SIMULATION TOOL TO ASSESS A PETROLEUM COMPANY SALES & OPERATION PLANNING Luiz Cláudio M. Paschoal 1 *, Daniel V. Chiarini 2, Ivan de Pellegrin 2, Juliana S. G. Yonamine 3 1 Gerência de Distribuição, Logística e Transporte CENPES PETROBRAS 2 Grupo de Produção Integrada COPPE Universidade Federal do Rio de Janeiro 3 COPPEAD - Universidade Federal do Rio de Janeiro Abstract. It is known that steady-state and dynamic simulation are important tools for representing continuous processes. Event driven simulation has the same role for discontinuous processes as those of discrete manufacturing or logistics operations. Nowadays many authors are defending the combined utilization of simulation with optimization techniques. It makes possible to evaluate the optimal solution found using simulation models or to optimize specific points of the processes during the simulation phase. This paper discusses the development of an event driven simulation model. It will be used with optimization tools (LP and MILP) to assess the Sales and Operations Planning of a petroleum company. The input data come from the petroleum allocation system. The goal of this solver consists of determining a supply, transport, production and distribution logistics schedule to minimize the total cost to satisfy end-product demand. The simulation model represents the logistics operations from oil tankers arriving to coast terminals to the fuel products distribution to the local markets. The pipelines, bases and refineries are also represented. Definition of the level of the operations details is critical since all supply chain is been simulated. The simulation is run subject to supply limitations, transformation constraints, transportation mode, capacities, stock limitations and other logistic constrains. The user will change these properties that characterize the simulation scenario using a user friendly interface when the tool development ends. As a first step the simulation of operations in São Paulo region was made. This region comprises four refineries, five bases and two coast terminals. The pipeline network is the most complex of PETROBRAS system. The results were validated against 2002 operation data and the model showed good agreement with reality. The next step is to expand the model to cover the whole country and create the user interface. To sum up the combination of both techniques allows evaluate the actual feasibility of supply planning considering all operations restrictions and variability of the logistic network. Keywords: Event-Driven Simulation, Sales & Operations Planning, Logistics Operations. 1. Introduction One of the first and most fruitful applications of operations research was for the petroleum industry. It was a linear programming based optimization tool. It comprises the optimization of oil extraction, refining, blending and distribution. The objective function was the maximization of the profits considering refineries configurations with constrains and the costs of crude production, transport and utilities. Since then many others works has been presented using optimization techniques for oil and gas industry problems (Neiro and Pinto, 2003, Lasschuit and Thijssen, 2003, Aires et al., 2004). Other application that became very important in industrial systems was the event-driven simulation. It is used to representing a portion of the real world, such that experiments in the simulation model can predict what will happen in the reality (Hollocks, 1992). Basically, a simulation model gives support to the decision-making, allowing the reduction of risks and costs involved in a process (Vieira, 2004). It is used to reproduce the material flux in a supply chain or in manufacturing processes. It takes into account not only the restrictions of recourses but also the stochastic characteristics of the events. * To whom all correspondence should be addressed. Address: PETROBRAS/CENPES Cidade Universitária Quadra 7 Sala 9109, Rio de Janeiro Brazil lclaudio@petrobras.com.br 1

2 In general linear programming tools are utilized to identify best values for a set of decision variables (April et al., 2004). They find the optimal operation point. But they are unable to deal with the uncertainties of many real world problems. The event-driven simulation is used to assess operations considering these uncertainties and the complexity of the process. Many authors (Azadivar, 1999; Fu et al., 2000; April et al., 2003; Cheng and Duran, 2003) are trying to combine optimization and simulation techniques in the same tool. The objectives are: Include stochastic variables in optimization problems; Support simulation in specific points where decisions should be made to contour problems that arise because of the variability considered; Evaluate the real feasibility of the results of optimization process. Nevertheless the efforts realized, there is no available technique that can be commercially used until now. Bush et al. (2003) also suggested the utilization of a combination of the two techniques but using different tools. The intention was to obtain the more feasible solution for barge transportation in the Mississipi River. This solution considers aspects related to time and sequencing of operations. Figure 1 shows the logic utilized by authors to solve their problem. Solve optimization model Send current solution and parameters to simulation model Run simulation model YES Is current solution realistic feasible? NO Is current solution final? NO Modify optimization parameters YES Determine infeasible parameters Determine which parameters to modify Modify optimization model Final solution Fig. 1. Flow between Simulation and Optimization (Bush et al., 2003) 2

3 2. Petroleum Supply Chain The Oil & Gas industry is vertically integrated dealing with a very large range of activities extending from petroleum and gas exploration to refining and distribution. Figure 2 illustrates a high level view of petroleum supply chain. The crude can be bought or produced in the company s field s. It needs to be transported to the refineries to be transformed in fuels and others products. Mainly two modals are utilized for its transportation: oil tankers and pipelines. After being produced the fuels are distributed by a pipeline network to the depots and to coast terminals. In these terminals ships transport the products to distant markets. The major companies that have several refineries actuating in regions closer to each other used to develop Sales & Operations Planning that balances market demand with resource capability. This planning takes in account the influences and synergies between the assets of the company considered. According to Hill (2003) the plans consider projections made by the sales and marketing departments, the resources available from manufacturing, engineering, purchasing and finance, and are directed toward hitting the company s objectives. They are done on an aggregate level and cover a sufficient time to ensure that the necessary resources are available. Fig. 2. Petroleum Industry Supply Chain Without a reliable Sales & Operation Planning, companies are missing a vital link between the longer-range business strategy and the operational activities that must support the strategy. PETROBRAS utilizes an in-house optimization tool, PLANAB, to define its Sales & Operations Planning with the optimal balance between demand and resources. PLANAB is a linear programming tool that incorporates an model with 11 refineries and its 64 process units, 80 available crude, 25 final and 50 intermediate products, 13 terminals, 62 pipelines and 20 market regions. But its necessary to assure that the plan is enough robust to accommodate the uncertainties not considered in the optimization process like, for instance, no 3

4 programmed shutdowns in pipelines or process units, delays in the ships arrival and changes in market demand. So PETROBRAS and COPPE/GPI began a project to develop a simulation model to assess the company Sales & Operations Planning generated using PLANAB. The idea is to follow the logic suggested by Bush et al. (2003) and showed in Figure Simulation Model The model was developed in the commercial tool Arena Professional Edition 5.0. It was choose to focus in the logistics operations of São Paulo region. In this region is produced and consumed almost 60% of brazilian demand of fuels. It has also the most complex pipeline network of the PETROBRAS system. Figure 3 gives a view of PETROBRAS assets in the region where there are 4 refineries, 5 depots, 2 coast terminals and the pipeline network the installations with each other and with the local markets. A good explanation of PETROBRAS operations in the region is provided by Neiro e Pinto (2003). Fig. 3. PETROBRAS system in São Paulo region Chiarini et al. (2004) describe the logic utilized in the model. It was divided in two sections: crude supply and products distribution. The refineries that transform the petroleum into fuels connect the sections. It was necessary to simplify as much as possible the representation since the PETROBRAS supply chain incorporates too many process and activities. The various types of crude were grouped in families regarding to its properties. The defined crude families were Marlim (heavy brazilian crude), RAT Craq (crudes that produce crackable atmospheric residuum), BTE (low sulfur crudes), Injeção (condensates) and Asfáltico (crudes that yield asphalt). The products were also grouped. The model works with the following product groups: LPG, gasoline, kerosene, 4

5 diesel, fuel oil and asphalt. The refineries are modeled as black boxes that converts each crude family in groups of products following a predefined conversion factor. The variability was considered in the frequency of oil tankers arrivals in terminals and volume of crude transported, in the markets demands and the availability of resources. Three important points of decision are the order of pipelines utilization, since they are all connected in a network and transport different crudes or products following an programming of the batches, the definition of refineries campaigns based in the crude stock and how to dispatch the products to the various markets. Logic algorithms were created for each of them (Chiarini et al., 2004). 4. Model Validation The model development was based in the operations realized in So the first validation was against 2002 operation data. Tables 1 and 2 show the differences between the model results and operation data collect for crude consumption and products yield in each refinery. Considering petroleum processing, the greater difference was in the consumption of Asfáltico crude in REPLAN. The magnitude of the difference can be explained by the little quantity of this kind of crude processed in the refinery. As the consumption is small, any difference in volume represents a great relative deviation. Table 1. Relative deviation of crude consumption CRUDE REVAP REPLAN RECAP RPBC Marlim -0,9% -1,7% -1,1% Rat Craq 2,0% 2,5% -0,4% 3,0% Asfáltico 13,8% Injeção 1,8% 4,0% 2,1% 3,3% BTE -2,0% -1,2% -2,3% Analyzing products yields it is possible to notice that all differences were acceptable showing good agreement between the model and the reality. Table 2. Relative deviation of products yield PRODUCTS REVAP REPLAN RECAP RPBC LPG -0,4% -0,4% -0,4% -0,3% Gasoline -0,2% -0,5% -0,5% -0,3% Kerosene 0,0% 0,3% Diesel -0,3% 0,0% 0,2% -0,1% Fuel Oil -0,5% -0,6% 0,0% -0,7% Asphalt 0% -1% Similar conclusion can be obtained by analysis of the pipeline utilization showed in Table 3. The greatest relative deviation was -12,8% for the pipeline with the lesser utilization. 5

6 Table 3. Crude pipelines utilization PIPELINE MODEL 2002 DATA São Sebastião Guararema 86,6% 90,6% Guararema REVAP 32,7% 37,5% Guararema REPLAN 86,2% 85,6% São Sebastião Cubatão 82,3% 77,6% Cubatão RECAP 90,0% 88,0% Cubatão - RPBC 64,3% 57,7% By other side, the average crude inventory in each refinery showed poor results for model calculation. This is presented in Table 4. Part of the difference was relative to the initial inventory level defined in the model input data. But other part was due to the utilization of the same time for crude tank preparation, relative to drainage and chemical analysis, in all refineries not considering historical data. This problem did not invalidate the analysis made with the model, but indicates that this part of the system should be better modeled to allow conclusions over crude inventory management. That is an advantage of simulation models since the comparison with actual operations indicates clearly where should be spent more time to improve the representation of the system. Table 4. Relative deviation of average crude inventory REVAP REPLAN RECAP RPBC Total Crude 11,9% 22,9% 44,6% 7,8% The validation described above indicates that model developed represents well what occurred in past with the logistic system. It doesn t say anything about how the model will work when simulating the future in situations significantly different from the past modeled. It is necessary to test the model running different scenarios and fell like it reacts to each of them. So the model was tested against a possible future scenario with brazilian exploitation of petroleum following its current trend. It was considered an increase in production of Marlim crude with an equal decrease in the volume of imported crudes. As expected the yield of light products decreased and the production of fuel oil increased. This happen because Marlim is heavier than the imported oils and the refineries cannot maintain the same yield profile without modifications in its hardware. It was not more possible balance markets demand with fuel production and the number of stock-outs increased as can be seen in Figure 4 for two depots of the region. The solution is to transfer or import light products to São Paulo and increase exportation of fuel oil to avoid the saturation of its tankage in the refineries. The answer of the model was the expected by PETROBRAS experts. This confirmed that it is possible to use the model developed to assess the company supply chain. Nevertheless it is necessary to make more efforts to improve its intelligence level. This will afford to reach an equilibrated solution from an unbalanced initial situation without too many human interventions. 6

7 Guarulhos Depot Stockout m3 Kerosene day 400 São Caetano Depot Stockout m3 LPG day Fig. 4. Stock-out of products 5. Conclusions As showed the Sales and Operations Planning provides a single, integrated and communicated company plan that balances the forecast market demand, the purchase of raw materials and the available resources. It needs to be realistic and achievable. But the optimization tools utilized to generate the plan cannot guarantee that it will really be achievable because this technique does not consider the uncertainties of the processes. The utilization of event-driven simulation fill this blank and allow to assess the plan generated taken into account all variability. This motivates PETROBRAS to develop joined with COPPE/GPI an simulation tool to be used in conjunction with PLANAB to make its Sales and Operations Planning more robust. The development of a pilot project to simulate PETROBRAS operations in São Paulo region showed that the model created has a great potential to be the base of the proposed tool. The next step will be feed the simulation model with PLANAB output data. The variability will be determined using historical operation and market data. In this phase the most important task will be correlate the simulation output with the reliability degree of the Sales and Operations Planning generated. The simulation modules with more influence over the success of the plan will be more detailed where necessary. But it is important to maintain the model as simple as possible avoiding increases too much its complexity. After that the model will be expanded to represent all downstream operations of PETROBRAS in the country. A user-friendly interface should be designed to really transform the simulation model in a logistic planning tool for the company. 7

8 References Aires, M., Lucena, A., Rocha, R., Santiago, C., Simonetti, L. (2004). Optimizing the Petroleum Supply Chain at PETROBRAS. In Proceedings of the 2004 ESCAPE. Oporto, Portugal April, J., Glover, F., Kelly, J., Laguna, M. (2003). Pratical Introduction to Simulation Optimization. In Proceedings of the 2003 Winter Simulation Conference. New Orleans, USA, 71. April, J., Better, M.,Glover, F., Kelly, J. (2004). New Advances and Applications for Marrying Simulation and Optimization. In Proceedings of the 2004 Winter Simulation Conference. Washington, USA, 80 Azadivar, F. (1999). Simulation Optimization Methodologies. In Proceedings of the 1999 Winter Simulation Conference. USA, 93. Bush, A., Biles, W. E., DePuy, G. W. (2003). Iterative Optimization and Simulation of Barge Traffic on an Inland Waterway. In Proceedings of the 2003 Winter Simulation Conference. New Orleans, USA, Cheng, L., Duran, M. A. (2003). World-Wide Crude Transportation Logistics: a Decision Support System Based on Simulation and Optimization. In Proceedings Foundations of Computer-Aided Process Operations Chiarini, D., V., Yonamine, J. S. G., Pereira, B. C., Caulliraux, H. M., Pellegrin, I., Paschoal, L. C. M., (2004). Desenvolvimento de um Modelo de Simulação para Análise da Cadeia de Suprimentos de Petróleo e Distribuição de Derivados da PETROBRAS na Região de São Paulo. Anais da Rio Oil & Gas Expo and Coference Rio de Janeiro, Brazil Fu, M. C. et al. (2000). Integrating Optimization and Simulation: Research and Pratice. In Proceedings of the 2000 Winter Simulation Conference. USA, 610 Hill, A. V. (2003). The Encyclopedia of Operations Management Terms. University of Minnesota. Minneapolis, USA Hollocks, B. (1992). A Well-Kept Secret: Simulation in Manufacturing Industry Review. OR Insight. 5,(4), 12 Lasschuit, W., Thijssen, N. (2003). Supporting Supply Chain Planning and Scheduling Decisions in the Oil & Chemical Industry. In Proceedings Foundations of Computer-Aided Process Operations Neiro, S. M. S., Pinto, J. M. (2003). Supply Chain Optimization of Petroleum Refinery Complex. In Proceedings Foundations of Computer-Aided Process Operations Vieira, G. E. (2004). Ideas for Modeling and Simulation of Supply Chains with ARENA. In Proceedings of the 2004 Winter Simulation Conference. Washington, USA,

SNF Report No. 26/10 Integrated planning of producttion, inventory and ship loading at refineries Jens Bengtsson Patrik Flisberg Mikael Rönnqvist

SNF Report No. 26/10 Integrated planning of producttion, inventory and ship loading at refineries Jens Bengtsson Patrik Flisberg Mikael Rönnqvist Integrated planning of production, inventory and ship loading at refineries by Jens Bengtsson Patrik Flisberg Mikae el Rönnqvist SNF Project No. 7985 Collaboration StatoilHydro The project iss financed