Through use of the Bayesian network model, we validate the following sensor data: hook load, standpipe pressure, flow in, flow out, total mud volume, block position, torque and RPM.

The backend engine can detect and replace missing data, outliers, or biased (drifting) data with model-predicted values. Identification of missing data and outliers is elementary and is done first. We then use a physics-based Bayesian network model to validate the sensor values against the model-predicted values. When we determine drift or bias to be above…