Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks

Abstract

The main objective of this paper is to use the frequent measurements of mud density, Marsh funnel viscosity, and solid percent to predict the rheological properties (plastic viscosity, apparent viscosity, yield point, flow behavior index, and consistency index of the drilling fluid) by developing empirical correlations based on 3000 field data measurements of KCl–polymer mud using artificial neural network. In this paper and for the first time, the solid percent will be included in the prediction of the drilling fluid rheological parameters. The common procedure on the well site is to perform a rheological test twice a day and measure mud density, Marsh funnel viscosity, and solid percent frequently every 15–20 min. The artificial neural network (ANN) black box was converted to white box to extract the mathematical model that can predict the rheological parameters. The average absolute error for all correlations was less than 6%, and the correlation coefficient was greater than 90%. Using the developed correlations in predicting the drilling fluid properties with high accuracy will eliminate the need for tedious laboratory measurements, and real-time properties can be obtained. This technique will help the drilling engineers better monitor the drilling fluid properties and control the drilling operations by avoiding the common problems, such as pipe sticking, loss of circulation, and hole cleaning issues.