Drilling fluid rheology properties play a critical role in the drilling operations. These properties such as, plastic viscosity, yield point, apparent viscosity, flow behavior and flow consistency should be monitored during the drilling operation. In normal operation, these properties are measure twice a day, while the mud density, Marsh funnel viscosity, and solid content, which are measured every 10 to 20 minutes. Previous models were introduced only to predict the apparent viscosity of the drilling fluid from the Marsh funnel viscosity with large errors.
The objective of this research is to introduce a new model to predict the drilling fluid rheological properties from the Marsh funnel viscosity, solid content, and density measurements in real time. A developed mathematical model was obtained from the weights, biases, and the transfer functions used in the Artificial Neural Networks (ANNs). The ANNs black box was converted to white box to obtain a visible mathematical model that can be used to predict the drilling fluid rheological properties only using Marsh funnel viscosity, solid content, and density.
Field measurement for 9000 drilling fluid samples were used for model training and testing, the viscometer readings at 300 and 600 rpm were predicted using the visible mathematical model from the ANNs. The rheological parameters such as yield point, plastic viscosity, apparent viscosity, and consistency index were determined from the viscometer readings at 300 and 600 rpm. The predicted rheological parameters were compared with the measured ones from the field and the match was very good. The average absolute error for the various parameters ranges from 1 to maximum 5 compared to 60 if we used the previously developed correlations. The developed model is a robust technique and tool that can be used to predict the real time drilling fluid rheological parameters that are essential for the drilling hydraulics design and also to predict the performance of drilling fluid.