Application of SVM Algorithm for Frictional Pressure Loss Calculation of Three Phase Flow in Inclined Annuli

Abstract

In Underbalanced Drilling (UBD) operation, the presence of three phases including, drilling fluid, air and cuttings, makes the estimation of equivalent circulation density more difficult. This study presents a novel computer-based model namely Lease Square Support Vector Machine (LS-SVM), for frictional loss calculation of two-phase gas based drilling fluids with the presence of cuttings as the third phase in inclined section of wellbore. This model is based on extensive experimental data collected from published literature. This model is account for in situ flow rate of each phase, Rate of Penetration (ROP), pipe rotation, and hole inclination. The results show that the proposed model is predicting frictional pressure losses in acceptable agreement with experimental data with very high correlation coefficient (>0.99) and small average relative error. Moreover, a trend analysis was carried out to check the validity of the developed model. Results of the present study show that implementation of this developed model can be incorporated in drilling simulators for accurate estimation of frictional pressure loss of three phase flow.

Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

“…Recently, different AI methods such as fuzzy logic, FL, support vector machine, SVM, genetic algorithm and artificial neural network, ANN, have been applied in petroleum engineering, and specifically in the field of drilling fluid engineering. Some of these applications include fluid flow patterns prediction in wellbore annulus [28], stuck pipe prediction [29], drilling hydraulics optimization [30], frictional pressure loss estimation [31], hole cleaning and prediction of cutting concentration [32], estimation of the static Poisson's ratio from log data [33].…”