Simulation modeling helps oil producer plan for the future



Abbas Sarmad, Vijay Agrawal, & Vineet Thakar, AECOM, Los Angeles, CA, USA



The results of simulation modeling have been applied successfully for over a decade to help petroleum companies meet their long-term strategic production goals. Simulation modeling has been used to optimize storage, pipelines, and the export facilities required to accommodate additional production capacity; to plan capital projects in anticipation of demand; and to evaluate the impact of crude segregation alternatives on future facility requirements.

While it can’t actually predict the future, simulation modeling is the best tool available to help oil production companies prepare for any eventuality that is likely to affect their complex operations.

Why not just use spreadsheets?

One major oil producer has used simulation modeling on two separate occasions to estimate the capital and operating costs of alternative facility layouts.

The first time was in 1997, when the company planned to expand its throughput of a single type of crude. Simulation modeling helped the company design the optimal configuration of additional storage tanks, berths, pipelines, and pumps. The model helped the company meet its strategic expansion goals by efficiently analyzing the complex interactions of the company’s operations.

Ten years later (in 2007), when the company was ready for its next expansion phase, it again used simulation modeling. The company was evaluating multiple types of crude and needed a reliable model to compare the costs of crude segregation alternatives and other infrastructure options.

Spreadsheets are too limited to analyze and model facility needs; they simply cannot account for the variability and complexity introduced by the many events – both scheduled and random – that are intrinsic to a firm’s operations. Because of the high cost of new facilities, the company wanted to make sure that it got its expansion plans right the first time. Simulation modeling is ideal for complex operations where spreadsheets are inadequate. Reliance on simplistic calculations could lead to underestimating capacity or cost, which ultimately results in higher costs. A simulation study, on the other hand, lets companies explore many different alternatives, pinpointing the benefits and disadvantages of various facility layouts with a high degree of accuracy.

The macro-level simulation modeling used by this oil producer (a confidential AECOM client) is different from the micro-level type of simulation modeling commonly used to help establish production schedules based on reservoir conditions, or to analyze specific refinery processes. Macro-level simulation modeling helps companies understand system bottlenecks, analyze future storage and export facility needs, and then design an optimized facility.

Another application of macro simulation modeling is to prepare for the challenge of having an infrastructure component, such as a pier or single point mooring (SPM) (see Figure 1), taken out of commission for an extended period of time (such as for rehabilitation or replacement). Modeling helps establish whether the remaining infrastructure is sufficient to accommodate the ongoing workload. If the answer is no, additional components are incorporated in the simulation until the desired results are achieved.

How simulation modeling works

The first step is to simulate the existing facilities, capacity, and production flow, using the best information currently available. This creates a modeling test case that can serve as a starting point. If (after iterative refinements) that model turns out to be a close match to the actual field experience of facility personnel, it verifies that the model is correctly designed, at least for the existing scenario. After establishing that the model is true to current circumstances, the key model inputs can be varied to reflect the different scenarios the company wishes to analyze. Figure 2 shows flow chart of the simulation modeling process.

Because the quality of a simulation’s output is determined by the quality of the input, the modeling has to be performed in close coordination with facility staff. Every facility is unique. The potential variables are many and diverse, such as the number of workdays per week, the length of time it takes to perform maintenance activities, and the best practices that are currently in place or planned (e.g., the amount of reserve that is kept in the storage tanks). It’s important to know exactly how a facility operates before assigning ratios and mathematical properties to components and events in order to run the simulation.

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