Real-time prediction of rheological properties of all-oil mud using artificial intelligence

Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The conventional tests that are usually be conducted by the mud engineers have limited resolution of the rheological data. The main objective of the paper is to relate the most frequent mud measurements as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV). Artificial intelligence (AI) is the best tool for modeling such a large number of recorded heuristic data from which the artificial neural networks (ANN) was chosen to be the optimization method. In addition, the study developed empirical correlations for determining the mud rheological properties. 369 real field measurements were used to build the ANN model which were collected from 56 different wells during drilling operations of different sections with different sizes. The results showed a correlation coefficient (R) that exceeded 0.9 between measured and predicted values and with an average absolute percentage error (AAPE) below 8%. The correlations may track on real-time the rheological properties for all-oil mud that allows better control for the drilling operation problems.