Simulating optimal tank farm design

Development of a computerised model incorporating Monte Carlo operational risk simulation with the optimisation power of linear programming

Michael D Stewart, Foster Wheeler North America
L Dean Trierwiler, Haverly Systems Inc

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Article Summary

Linear programming (LP), a technology first applied during the Second World War to help solve troop-supply problems, continues to be the premier tool for determining the optimal distribution of limited resources. Nowhere is this more evident than in the petroleum refining industry. As barriers that once separated individual refineries continue to fall in an attempt to improve overall industry health, LP is used to identify the synergies and operational improvements that result.

Inventory disruptions
Historically, the way to alleviate product or feedstock supply problems in petroleum refining was to “build another tank”. These tanks provide extra storage capacity, which effectively reduces the time element in operations planning, lessening the impact of disruptions, both planned and unplanned. Tanks allow stocks to be more readily available before they are needed, or held when their transfer is delayed. They give refiners more time to bring their contents to specification quality or to prepare for expected outages. Tanks also provide for easier stock segregations when enough exist to designate each in limited services. However, new tanks are expensive to build ($26.50–40.00 per barrel installed) and maintain, and difficult to justify both economically and environmentally. In addition, most refiners are faced with limited or no available real estate. Therefore, even though throughputs and product diversification are on the rise, refiners are challenged to operate within the same or less available inventory capacities.    

So how much tankage capacity is enough? How much is excessive? Foster Wheeler (FW) recently completed a detailed tankage and hydraulic study for the Kuwait National Petroleum Company (KNPC). This assessed the adequacy of existing tank farms at all three KNPC refineries for operations through 2010, and developed an optimised solution to the refineries’ tank farm needs. In addition to dealing with routine operations such as planned turnarounds, the study had to assure that the tank farm capacity would be sufficient to handle unplanned events such as ship delays, power failures, weather problems and unit outages, as well as an expected increase in refined product diversification. This would allow for optimum utilisation of the available storage capacity, while also minimising capital cost for the upgrading facilities.

Inter-refinery supply study
KNPC operates three refineries in Kuwait with a total crude throughput of approximately 900 000bpd. The refineries are Mina Al Ahmadi (MAA), Mina Abdulla (MAB) and Shuaiba (SHU). While SHU is KNPC’s oldest refinery, MAB is KNPC’s most modern. MAA, however, is the largest and most complex, with FCC, hydrocracker and petrochemical feedstock preparation units. These refineries have developed in stages over a long period of time, with the last modernisation completed in 1988.

Finished products from the refineries are transferred to tankers berthed at the North Pier, New South Oil Pier, SHU Oil Pier, and Sea Island. MAA products are exported to North Pier and South Pier, MAB products to Sea Island and SHU products to SHU Oil Pier. All of the refineries have been integrated for better feedstock management and product sharing through six inter-refinery transfer (IRT) lines. These include two 24in lines for black oil products, two 20/24in lines for white oil products, one 14in line for motor gasoline components and one 20in line for naphtha/ kerosene. The refineries continuously exchange products for use in their various process activities or blending operations in order to create products that are ready to export. KNPC defined the objectives of the study as follows:
• Outline an interim solution for tankage deficiencies in KNPC refineries before anticipated upgrading and expansion projects are completed in 2008/2010
• Carry out a simulation study to assess the future tankage requirements for KNPC
• Check for each refinery, and for KNPC in general, the adequacy of existing intermediate and finished product storage capacity, number of tanks, IRT systems, unit charge systems, blending and dispatch facilities, and identify any modifications required
• Check the adequacy of the hydraulics and flow metering systems to ensure efficient operation of the refineries and export operations
• Identify the additional storage capacity, tankage requirements and other associated facilities required to meet overall objectives
• Assess the benefit of on-line blending and industry best practices for storage and handling
• Prepare process design packages for any new tanks and identified modifications, with budgetary cost estimates for implementing such recommendations.

To accomplish these objectives, FW performed the following tasks:
• Collected all pertinent inventory and refinery data/documents
• Prepared a study design basis
• Developed an appropriate tank farm simulation model
• Simulated the refineries’ existing mode of operation in the 2008/2010 time frame
• Assessed alternative tank farm system improvements
• Performed preliminary hydraulic/ flow analysis
• Issued a final report with recommendations for tank farm system improvements.

Tank farm simulation model
A simplified block diagram of a typical single refinery configuration is shown in Figure 1. The tank farm simulation model must adequately address the issues contained in the area indicated by the dashed oval line.

The tank farm simulation model served as the basis for identifying the bottlenecks, additional storage capacity and modification required in product receipt, blending, and unit charge and dispatch facilities. It was used to answer the question, “What is the adequate tank storage capacity required for anticipated refinery operations?” To effectively answer this question, the model incorporated statistical risk techniques to best reflect the realities of refinery off-site tank farm operations. In addition to dealing with routine operations such as planned unit or plant turnarounds, the model accounted for unplanned events including ship delays, power failures, weather problems and unit outages.

Operating any industrial facility involves risks. Some risks are fairly common but have a low consequence. Others may have a low probability but can be quite serious. Whatever the risks, they can be quantified and easily understood. This field of study is known as probabilistic risk assessment and helps companies and government departments to assess whether they have adequately identified the risks and potential consequences involved with operating these facilities. The concepts were developed over 40 years ago, but recent advances in computing software and power have increased both the use of such analysis and the confidence in them.

The concept of probabilistic risk assessment is that simulation can help determine the chances of a particular outcome, or set of linked outcomes, based on what is known or estimated about the smaller variables that lead to these outcomes. Historical data is used to estimate the relative frequency of those variables and then applied in random order to models to determine the impact. By defining the known linkages within a system, the simulation model is unconstrained by complexity and quite accurate.

This statistical modelling method (also called stochastic simulation or discrete event simulation) is a powerful and accurate method for solving systems engineering problems. It is not constrained by simplifying assumptions as with more traditional analytical modelling, but instead runs many “histories” concurrently, each with a different stochastic behaviour, and aggregates the results. In other words, it is a method that fairly accurately predicts expected system behaviour and variation. The Monte Carlo simulation effectively overlaid and adjusted the LP model to reflect events such as emergency unit outages, unplanned unit maintenance and shipping complications.

Such use of LP in probabilistic modelling was impractical until recently. Attempts to integrate it with technologies such as Monte Carlo simulation often produced uncontrollable models, which were exceedingly large, fragile and slow, difficult to interpret, and overwhelming in terms of their data consumption and production. However, with technical advances both in the hardware abilities of computer processing and the software abilities of LP solvers and data management tools, larger problems are solved faster, from better-managed data, to produce more stable, understandable results.  

The LP model for this study was developed using Haverly System’s proprietary generalised refining-transportation-marketing planning system (GRTMPS). This model represented the planned refinery operations in 2010 with current tankage condition at all three refineries. It incorporated all current interconnections between the refineries, and was initially populated with current tankage constraints, planned unit outages, planned tank maintenance and pumping limitations.

The focus of the LP model was on the tank farms, blending and shipping operations within the KNPC refineries, and not on specific refinery process unit operations. As such, it retained the ability to blend to final product specifications from material in the inventory, although it was limited to the major blend specification properties (for example, sulphur, gravity, octane for gasoline, diesel index for gas oil/diesel and viscosity for fuel oil).

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