Real-world impact of simulation

Disruptive simulation technologies enabled a large oil pipeline company to maximize profits. Twenty percent savings in capital projects were realized, amounting to $85 million. An additional $24 million in benefit was achieved in additional annual, recurring revenue.

01/18/2016


Figure 1: Example of a typical terminal model. Courtesy: Stream SystemsSimulation is a disruptive technology that changes the nature of the decision-making game. The oil and gas industry faces the challenge of optimizing large capital assets and extracting maximum value from investments. But the difficulty to do this lay in analyzing the components acting as an integrated system (network), rather than silos. 

A large transnational crude oil pipeline experienced this dilemma. The company managing this asset understood that traditional modeling methods were unable to resolve the problem because of the system constraints moved and changed across the network as operating conditions changed. In addition, the combination of variables became exponential considering the high system complexity. The system contains:

  • Five major terminals
  • 73 storage tanks
  • 47 commodity types
  • 16 commodity pools
  • 20 default routes
  • 37 business rules
  • 1,200 connections
  • 26,000 km of pipelines.

In addition, the system processes 14 million barrels of oil daily in 400 concurrent batches.

In light of these constraints, management decided to try simulation modeling to maximize profits and optimize scheduling.

A disruptive technology

Simulation replicates the key elements of a problem or opportunity to mimic reality in a controlled, virtual environment. In this way, a reasonable representation of the problem is created, and any number of variable inputs and outputs can be manipulated. Decisions that would otherwise be avoided or ignored can be tested for feasibility because players-from technical staff to executives-are operating in a risk-free, virtual environment.

These gamification principles make simulation effective and fun at the same time. Users are able to drag-and-drop various components and events-such as economic, regulatory, and taxation-and run simulations to obtain real-world output potentials. By running the simulation multiple times, optimal decisions can be determined for the short- (1 to 3 month) and long-term (10 year) horizons.

Innovative real-world results

This oil pipeline company was growing quickly. However, it continued to experience a chronic shortfall in its throughput and revenue performance. In addition, a structural change in the regulatory environment forced the company to increase throughput while pushing for cost savings and capital efficiency.

Figure 2: Example of a typical user interface. Courtesy: Stream Systems

After deciding to move forward with simulation, a multi-commodity terminal located in the U.S. Midwest was chosen as a candidate for modeling. The terminal consisted of more than 40 medium- and large-diameter oil storage tanks with many interconnecting manifolds, associated meters, and instrumentation. 

The first step in the process was to determine the data available within the organization. Data were collected from the analytics team, which served as the umbrella group for collecting operational information for the entire company. In addition, batch data were collected from the scheduling department. Prior to this, the operations and batch departments had worked in isolation. The simulation exercise facilitated collaboration between the two groups and created departmental integration.

After collecting and analyzing the data, an assessment of operating performance relative to industry benchmarks was performed. Over the course of this assessment, operating gaps and areas for improvement were identified.

The objectives of the modeling exercise were to:

  • Determine whether existing tankage at the upstream terminal would be adequate to store deferred batches without shutdowns.
  • Quantify the impact of customer outages on the overall system volume throughput.
  • Determine whether planned expansion would be adequate to mitigate future outages.

With the data collected and objectives identified, an initial high-level model was created. Working with the operations and batch departments, features and functionalities were developed to gain greater fidelity and alignment with real-world operations. After a working model was confirmed, simulations were implemented accounting for real-world variables.

Overcoming implementation challenges

The simulation implementation was not without its challenges. First, learning statistical methods and moving away from reliance on spreadsheets were required to implement the models. With no prior experience using whole-terminal models or large complex network simulations, analysts looked at the problem in a micro rather than macro sense. In addition, a distrust in the validity of the simulation model existed at first, but it gained increasing acceptance as analysts obtained working experience with the model. 

Figure 3: Example of a typical simulation output. Courtesy: Stream Systems

Finally, the organization did not have robust analytical capabilities or underlying support systems for a comprehensive data management strategy. Furthermore, operating data were scattered and incomplete. This mentality changed after the importance of comprehensive data collection was realized.

Significant savings

As a result of the modeling, the organization determined that it was possible to eliminate one large storage tank, numerous piping manifolds, and associated valves and instrumentation.

Figure 4: Example of a typical simulation output. Courtesy: Stream Systems

A year after implementing the simulation findings, there was a 20% savings in capital projects, which amounted to $85 million. An additional $24 million in annual, recurring revenue was achieved. Besides this example, companies typically realize an average of between 10% and 20% capital savings after implementation of simulation recommendations.

What makes simulation effective?

These results are not specific to this particular case or industry. In a study by MIT Sloan Management Review, top corporate performers in more than 30 major industries indicated they were twice as likely to use simulation and advanced technologies to guide their decisions compared to their competitors (see "Five simulation effectiveness criteria" below).

Simulation is here to stay

Maximizing efficiency in the oil and gas industry is difficult due to the scale and integrated nature of operations across departments; problems do not exist in silos. In addition, system considerations, such as the economy, changing regulatory environments, and taxation framework impact the effectiveness of potential decisions. This is why simulation is innovative in its ability to create disruptive and material change. Moreover, it will only continue to be adopted by organizations that seek to decrease their overhead costs and increase efficiency. Simulation accounts for big-picture issues and allows for testing in a risk-free environment. It is through this creative, out-of-the-box thinking, that change can occur.

Five simulation effectiveness criteria

There are many ways that simulation can transform complex and dynamic decision-making in the oil and gas industry. Here are five of them:

1. Asset planning tool: Simulation is an asset planning tool, not a scheduling application tool. It optimizes large capital assets and extracts maximum return from those investments. Therefore, the outcomes are comprehensive in scope, taking into consideration the most important elements of the problem.

2. Forward looking: Simulations are validated using actual real-time data, but the data are not extrapolated into the future to make decisions. With simulation, data patterns are determined from past behaviors, and the simulations are run taking into consideration the possibility of and complexity of future activities and events.

3. Time: Whereas models of this complexity typically take 4 to 6 months to configure using conventional software, simulations typically take between 4 and 6 weeks. This is possible because of proprietary software, algorithms, and because the model is fit-for-purpose with the appropriate level of details to solve the problem. It is also possible because of the processing power of today's computers. Gut-based decision making is eliminated because real-world answers become evident in a short period of time.

4. Collaboration: Collaboration across disciplines is required to achieve optimal decision making. Departments no longer operate in silos, and they become interdisciplinary as data are shared. This collaboration results in robust outcomes as the entire system is considered.

5. Team advantage: The team behind the simulation is crucial. Data scientists draw patterns from past data and determine the most important variables to include and manipulate in the simulation. It is important that the team have experience, not only running traditional simulations, but also working on the core operating activities in the oil and gas industry. It is also important to find a balance between individuals with strong technical skills and those who are systems thinkers.

Allan Chegus is president and cofounder of Stream Systems. Image courtesy: Stream Systems

Allan Chegus is president and cofounder of Stream Systems, a product and multidisciplinary consulting firm specializing in business simulations, advanced optimization, and predictive analytics serving the oil and gas industry. Chegus has extensive project and program experience in business simulations and advanced decision-making systems including feasibility and business case development for multi-million-dollar energy-related business projects. Edited by Eric R. Eissler, editor-in-chief, Oil & Gas Engineering, eeissler@cfemedia.com



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