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Drilling Systems Modeling & Automation, Part 3: Validating Model with Field Data

 the hoisting system model has been built using our best educated guesses for the system parameters. We used the datasheet of the AC motor and our engineering judgement for guessing values of the proportional & integral gains for the controller and ramp up speed for the drive. While manufacturers may provide values for some of these quantities, they are only approximate.  We want to estimate these parameters as precisely as possible for our digital twin using field data to ascertain whether it is an accurate representation of the actual AC motor system.

This is where Simulink Design Optimization plays a pivotal role in estimating parameter values. The Parameter Estimation tool is a user interface to perform parameter estimation, organize the estimation project, and save it for future work. The process consists of a few well-defined steps:

- Collect field data.
- Specify the parameters to estimate including initial guesses and parameter bounds.
- Configure your estimation and run an optimization algorithm. In this case, the objective function compares measured command speed with simulated speed for the drive.
- Validate the results against other data sets and repeat above steps if necessary.

This workflow is also the first step in creating digital twins. Digital twins use real-time drilling data to update the model. They can be used to evaluate the current condition of the rig and more importantly, predict future behavior, refine the control, or optimize operation.

Additional Resources:

- MATLAB for the Oil and Gas Industry: - Simulator codes on File Exchange: - Simulator codes on GitHub:

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