Decision Support for Storage and Shipping Using Discrete Event Simulation Part 2. Javier Vazquez-Esparragoza and Jason Chen.

Transcription

1 Decision Support for Storage and Shipping Using Discrete Event Simulation Part 2 By Javier Vazquez-Esparragoza and Jason Chen KBR Technology Houston, TX USA 1 SUMMARY Over the past two decades, KBR has been using discrete event simulation (DES) to study and recommend shipping and storage solutions as an integral part of project execution. These simulations studies are conducted throughout all the phases of a process facility's design, construction and operation. A successful solution optimizes life cycle costs by balancing the capacity for material movement and storage capacity against risks, unforeseen events and the overall project schedule. Section 2 presents a KBR s logistics process four-step simulation-based methodology, Section 3 shows a third example of the estimation of the storage volumes and shipping capacity for refinery operations in Africa and the Middle East. As in Part 1, the project background, the study basis, objectives and the key deliverables are discussed. Conclusions are listed in Section 4 and a brief introduction of additional fields where simulation has been applied by KBR is also presented.

2 2 KBR S SIMULATION METHODOLOGY The traditional spreadsheet based techniques and deterministic mathematics models, in general, are inadequate to handle the inherent complexity and uncertainty in the logistics systems. Instead, discrete event simulation (DES) that uses statistical distributions to model the variations is an ideal tool to provide accurate solutions for decision making right at the first time. It is also very flexible to model a system with different level of details from the plant level down to the operations of a single pumping station, trucks or ships. KBR s logistics study follows a four-step simulation-based methodology, as shown in Figure 2-1. Figure 2-1: KBR s Simulation Methodology

3 2.1 Plan This phase defines the study scope, modeling objectives, project objectives, decision variables, performance measures, and critical uncertain factors. The project objectives specify the duration of simulation project and the detail level of the visual display and animation. The modeling objectives state the purpose of simulation modeling, usually the problem to be solved. Process mapping is applied to capture the key steps and decision points. It is the blueprint for developing the structure of a computer model. It is also necessary to identify the stakeholders to facilitate the data collection and model validation, which in turn, enhance the quality of models and the credibility of the outcomes. 2.2 Simulate The data to feed the simulation model are collected in this phase. Depending on the modeling level, the factors listed in Section 2.2 of previous Part 1 article are selected for specific needs. The statistical distributions are then generated for the collected data to represent the variability in the system. The computer model for the baseline scenario is developed using ARENA, a DES application licensed by Rockwell Automation. Before applying the computer model for subsequent analysis, it is necessary to verify and validate the model. Model verification ensures the computer program is correct in syntax while validation is to guarantee a satisfactory range of accuracy. Animation model is developed for two purposes. One goal is to facilitate monitoring the flow of entities to ensure the model logic is error free. Another purpose is to visualize the model using dashboards, graphs, and 2D/3D objects for the best communication with the users team. Figure 2-2 is a snapshot of 3D animation for a LNG facility when a vessel is approaching the berth.

4 Figure 2-2: 3D animation for a LNG facility 2.3 Perform what-if analysis What-if analysis uses the baseline model as a test-bed to evaluate various alternatives. Any parameters of the decision variables, assumptions and the basic input data can be adjusted to fit the study purposes. Each scenario is run for multiple replications, using different random seeds, to obtain the performance with meaningful statistical characteristics. 2.4 Identify the solutions and implement results This phase organizes the outcome of what-if analysis and applies the techniques of statistical analysis to compare the performance among scenarios. The scenarios that yield the best financial benefits without compromising the operational performance are identified as recommendations. The final solution is then selected by the decision makers for implementation. To achieve the continuous improvement, the actual performance of the solution is monitored and evaluated that may lead to model refinement and modification.

5 3 CASE STUDY 3.1 Refinery Operations The objective of this study was to estimate the total volume of intermediate and product tanks required, along with a confirmation of the blender rates and the maximum blend sizes for the gasoline and diesel products, in a refinery plant in Africa. The facility imports the crude oils as raw materials and exports the refined liquid products and bulk solid by-products Study Basis and Objectives Figure 3-1 shows the echelon structure of the intermediate components and the final products in the study scope. Three grades of gasoline are produced from eight components via the blending operations. Similarly, seven components are blended by two blenders to produce three grades of diesel. The blending processes for different product are simulated with different run down rates and recipes. The study basis is listed below: All import/export shipping operations at the refinery marine terminal can be handled by two berths, solids berth and liquids berth. Except the liquefied petroleum gas (LPG) is restricted at the solids berth, other products could be handled at either of these two berths; The product tank requires one-day testing before loading to the ships; A pre-determined refinery capacity is given in the unit of barrels per stream day (BPSD) A pre-determined capacity for each blender; The ships may arrive up to 4 days late; 24 hours operations for product transfer at the harbor.

6 Intermediate Components Products Butanes Alkylate Grade 1 Gasoline Light Naphtha Heavy CAT Naphtha Blender Grade 2 Gasoline HC Light Naphtha Reformate Grade 3 Gasoline Refinery Isomerate Sat/Unsat LPG Mix LPG Shipment Caustic Treated Kero HC Kero Grade 1 Diesel LCO SR Diesel Blender Grade 2 Diesel HT Diesel HT Light Distillate Grade 3 Diesel HT Heavy Distillate Jet Fuel Figure 3-1: Storage topology diagram Among various factors that influence sizing the storage capacity, three key issues are taken into account: The flow rate and recipes of intermediate components for producing the final products; The blend batch size, either 150,000 bbl or 300,000 bbl, for each final product; The uncertainty of delay in ship arrivals: It is necessary to examine the system performance when changing the maximum delay time ranging from two days to four days. A total of five factors, including the recipe type, the blend batch size of three grades of final product, the maximum delay days, and whether to constrain the total capacity of the final products, are identified as

7 the independent variables for the what-if analysis. Twelve cases for the gasoline products and sixteen cases for the diesel products are designed for the analysis of the tankage system. For illustrative purposes, Table 3-1 gives the description of three cases for gasoline: Table 3-1: Three of twelve cases for gasoline products Case Product Recipe Blend Batch Blend Batch Blend Batch Maximum Constrained for Grade 1 for Grade 2 for Grade 3 ships delay capacity of days final products 1 Gasoline No 2 Gasoline No 3 Gasoline No Analysis and Results Figure 3-2 shows the time-based tank levels of some of the intermediate components and the Grade 1 Gasoline for one of the runs. By observing the variability of the tank levels during the simulation run, the minimum and maximum amounts of components and products volumes across all the cases are obtained. These values are then used to determine the required storage volume for each component and product tanks to maintain refinery operations. Alike to the previous examples, other key performance measurements, including the estimation of the utilization of the berths and blenders, and the waiting time of shipping for loading are also captured by the simulation.

8 Figure 3-2: Time-based tank level of intermediate components and Grade 1 Gasoline

9 Table 3-2 summarizes the suggested tank sizes for gasoline products and their intermediate components (simulation figures are in blue), comparing with the preliminary design specifications proposed by a value engineering study. The two sets of results overall are aligned well, except the storage for the saturated/unsaturated LPG mix and the Grade 2 gasoline (figures in red). The tankage for the saturated/unsaturated LPG mix is recommended to be larger while one 300,000-bbl tank is sufficient for the Grade 2 gasoline. The similar observations for the diesel products and their component are shown in Table 3-3. The tanks for Grade 1 diesel, Grade 2 diesel and the Jet Fuel are recommended to be one unit less than the preliminary design. The simulation study effectively improved the fidelity of engineering design for a large-scale refinery plant. In addition, it achieved the intense cost-savings by identifying the accurate storage capacity and minimizing the risks of oversizing. Table 3-2: Storage capacity for gasoline products and their components Gasoline and its Preliminary capacity by Value Suggested capacity by Simulation components Engineering (Simulation Results) Butanes 30,000 30,000 (29,950) LT Naphtha 150, ,000 (125,550) HC LT Naphtha 240, ,000 (184,230) Isomerate 180, ,000 (165,745) HVY CAT Naphtha 320, ,000 (316,701) Reformate 360, ,600 (360,019)

10 Sat/Unsat LPG Mix 29,000 32,000 (32,030) Grade 1 Gasoline 2 * 300,000 2 * 300,000 (455,000) Grade 2 Gasoline 3 * 300,000 1 * 300,000 (300,000) Grade 3 Gasoline 1 * 300,000 1 * 300,000 (300,000) LPG 3* 30,000 3* 30,000 (77,000) Table 3-3: Storage capacity for diesel products and their Components Diesel and its Preliminary capacity by Value Engineering Suggested capacity by Simulation components (Simulation Results) Caustic Treated Kero 240, ,400 (225,032) HC Kero 240, ,408 (222,322) SR Diesel 63,000 63,000 (57,192) HT Diesel 340, ,000 (226,700) HC LT Distillate 240, ,400 (206,700) HC HVY Distillate 160, ,300 (145,286) Grade 1 Diesel 3 * 300,000 2 * 300,000 (595,355) Grade 2 Diesel 2 * 300,000 1 * 300,000 (300,000) Grade 3 Diesel 1 * 300,000 1 * 300,000 (300,000) Jet Fuel 2* 300,000 1* 300,000 (300,000)

11 4 CONCLUSIONS Making a sound decision on the number of ships used for product transportation and volumetric storage capacity is one of the major challenges in logistics design and management in the Oil and Gas industry. The problem involves intensive capital-investment, inflexible supply chain and high complexity. Moreover, numerous production and transportation risks, such as unscheduled maintenance, weather variations, travelling speed, and harbor availability require a sophisticated modeling tool capable of handling the complexity and uncertainty of the transportation of products by fleets of ocean vessels. These types of studies accurately evaluate the performance of logistics operations as well as proactively identify potential bottlenecks and improvement opportunities. Besides the shipping and storage studies, KBR provides logistics simulation and traffic simulation to optimize the materials movements during the construction phase. Freight profiles, discrete-event models and traffic models are developed to examine the supply chain capacity of civil infrastructure to ensure the planned freight arrivals can be accommodated in different construction phases. KBR s simulation practices to support construction logistics will be discussed in a future article Acknowledgments Authors wish to acknowledge Dr. Jeffrey Feng for the support and supervision on preparing the paper and KBR for granting permission to publish it. Additional text: Under Study Basis,

12 Note that events times are specified by the use of Probability Distributions. For example, if the travel time of a ship through a channel is in average three hours, the model generates time periods for each ship on the simulation following a Triangular Distribution as TRIA(Min, Mode, Max) or TRIA(2.4,3.0,3.6) hours. Meaning each ship could spend from 2.4hr to 3.6hr to navigate the channel. Other probability distributions (UNIFORM, WEIBULL ) are also used for events like traveling time, tidal or wind time, maintenance time, etc.