Modeling and Optimization of Oil Production Using AnyLogic Fluid Library

Problem:




Canada is the third largest country to have oil reserves. However, most of the oil is in oil sand – the mixture of sand, oil and water – which has to be heated up with steam to emit the oil. Though operational costs for extraction of the substance are high, capital investments in this sphere reached $26 billion in 2014, which proved its prospects in the future.


To produce the oil from sand, a complex system of wells, pipes, steam generators, and other equipment is needed. It is costly to maintain such a distribution system, and outages may lead to disruptions in steam injection and oil production. To optimize expenditures and capture production lags, Stream Systems company applied AnyLogic simulation modeling. With previously employed spreadsheets, engineers could model the working process of 10-20 of wells. Simulation approach allowed them to model the production facility with hundreds of wells.


Solution:




The oil production process was presented with three major elements:

  • Central processing facility (CPF)
  • Reservoirs, which act as the source of the oil sand
  • Stand-alone wells and well pads (set of wells), extracting the oil

The simulation model consisted of smaller models of the system’s elements. This approach allowed engineers to monitor how the working process of a particular component might affect other elements.

Operational Planning Simulation


Natural Disaster Simulation Model

Because of the model’s complexity, AnyLogic multimethod approach (a mix of agent-based, system dynamic, and discrete event modeling) was applied. The system acted as a liquid carrier, and the lag in one component might lead to lags in other ones. AnyLogic fluid library was used to capture these lags, including in cases of emergency.


AnyLogic was seamlessly integrated with external data sources, which allowed modelers to use any types of files to input the data into the model. To manage additional calculations and make the model more realistic, external Java libraries were incorporated. The model’s input data included:

  • Operational data - infrastructure, layouts, configurations of system’s components, seasonality, etc.
  • Production profiles
  • Excel spreadsheets and text files

The model consisted of wells that acted as individual agents. Each well had a particular behavior and was connected with pipes and other flowchart components. It was easy to add and adjust components in the model if needed.


Apart from looking at specific parts of the model, one could also observe the production process, at a high level of perspective, to make operational and strategic plans. At both levels, it was possible to set parameters and run various experiments to perform the model’s optimization. Dashboards showed statistical data in order to visualize the changes in the system.


Output data accounted for:

  • Throughput of wells and pads
  • Steam-oil ratio
  • Steam, water, and oil production rate
  • Number of pads and wells per pad
  • Product quality

Decision Making Simulation Model


Outcome:




AnyLogic simulation modeling helped connect parts of the system, located above and below the ground. With the multimethod simulation modeling approach, it became possible to run multiple scenarios, including Monte Carlo and what-if experiments, and analyze variabilities in terms of scheduling and maintenance, providing the system’s optimization.


Fluid library helped represent each part of the model with high granularity, and display the ripple effect and breakages in the system. Moreover, it helped consider dynamic changes and their impact on the system. With this approach, it became possible to make real time decisions and capture the quality of the oil.


The model also contributed to decision making on future capital investments and reducing them, finding out when to replace or provide maintenance to the components of production process.


AnyLogic software was successfully implemented in the company’s other projects.

 

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