Simulation and Optimization of Sand Transportation for Fracking Operations

Simulation and Optimization of Sand Transportation for Fracking Operations

Tecpetrol is a leading energy company that specializes in the exploration, production, transportation, and distribution of oil and gas. One of the ways in which they do that is through fracking. This is the process of cracking rocks using a combination of sand and water at high pressure to release trapped gas.


The sand that is used in the fracking process is stored at different warehouses. To get this sand supply to the fracking site, trucks are used with containers, or hoppers. These trucks are loaded with sand and then travel to the site, often over uneven roads, for distances of up to 150 kilometers.

When the trucks arrive, their cargo is loaded into an empty container. This is done using a forklift, which moves the containers to supply the platforms and fill the stocks. The trucks then travel back to the warehouses, and the cycle starts again.

Sand transportation illustrated moving between the origin and the destination with the help of trucks and forklifts

The process of sand transportation for fracking operations

There are many complex decisions involved in this cycle. Trucks must be quickly turned around so that they can supply a constant amount of sand. Stock levels and empty and full containers need to be managed. The forklift is highly important and has many duties, such as arranging the containers in the best way possible while avoiding unnecessary movements, as well as monitoring stock, trucks, and platforms to make decisions.

Initially, all analysis was being done using Excel to make decisions. However, it was found that this method was too static and wasn’t able to answer key questions, such as how many trucks and containers were needed, what if a conveyor failed, and so on.


Tecpetrol worked with Eurystic to develop a simulation model of sand transportation to represent how all the resources interacted with each other. This was done to answer all the above-mentioned questions. The model also needed to be flexible to try other possible configurations in the future.

Eurystic chose to use AnyLogic because they had a lot of experience with it and knew that it worked very well, especially for time-dependent processes. So, they were able to use it to create a customizable UI, which allowed the users to get answers from the model very quickly. Finally, multiple iterations and simulations could be run.

The model overview showing how all the different systems worked together, including the inputs, the model, and the outputs

The model overview of sand transportation for fracking operations

The logistics model takes inputs from an Excel file that has been downloaded from the Tecpetrol database and from the user through the user configuration panel. From this one model, two experiments can be run: a parameter variation experiment and a simulation experiment.

First, the user chooses one specific site, sets the number of trucks and containers, along with the options for some other variables, and then runs the parameter variation experiment. Different scenarios are then generated, which can be appropriate or not.

The parameter variation experiment welcome screen displaying fracking equipment and an area for inputs The paremeter variation experiment model running showing graphs and results
The parameter variation experiment welcome screen and results (click to enlarge)

The user then chooses an appropriate or optimal scenario and inputs the parameters into the simulation experiment. This will then be able to answer further questions other than just how many trucks or containers are needed.

The simulation experiment welcome screen displaying fracking equipment and an area for inputs The simulation experiment model running showing graphs and results
The simulation experiment welcome screen and results (click to enlarge)

All the information that the model creates can then be exported to another Excel file or just shown to the user through the UI.


After eight months of using the model, there had been a reduction in non-production time, which had saved an estimated $500,000.

This model also has potential for the future because it is still in use. It can be used to compare technologies and negotiate transport contracts. It can also be employed for bottleneck analysis, especially when choosing a new contractor, or if there is a site far from the sand warehouse.

Finally, this flexible model can also be utilized in the future to evaluate new designs of platforms and containers. This is a potential investment of $3 million, but because they have the statistical analysis from the model, they can be more confident in their decisions when choosing new designs.

The case study was presented by Damian Marino, of Eurystic, and Chiara Dolci, of Tecpetrol, at the AnyLogic Conference 2022.

The slides are available as a PDF.

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