Abstract
Accurate estimation of the pressure losses for non-Newtonian drilling fluids inside annulus is quite important to determine pump rates and select mud pump systems during drilling operations. Therefore, in this study, pressure losses of Herschel–Bulkley drilling fluids in concentric and eccentric annulus are predicted using simple, reliable, and cost-effective artificial neural network (ANN) method. The average relative error was less than 5% with correlation coefficient (R) of 0.999 for the prediction of pressure loss (ΔP) taking the ratio of pipe diameter to casing diameter (D i /D o ), eccentricity of annulus (ϵ), and properties of the non-Newtonian liquid, that is, flow behavior index (n), consistency index (K), yield stress (τ y ), and liquid flow rate (Q) as inputs to an ANN for Herschel–Bulkley fluids. Experimental data from the literature were used to train the ANN for predicting pressure losses in eccentric annuli.
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