Abstract
Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel–Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.
Highlights
Non-Newtonian fluids have been used extensively in drilling oil and gas wells
Wellbore hydraulic modeling is a key component for a successful drilling operation as it enhances the rate of penetration (ROP) but minimizes the risk of potential problems encountered during drilling operations such as stuck pipe, kicks, loss circulation, and other various activities leading to non-productive time (NPT)
This study comprises an analysis of four different algorithms (SVM, artificial neural network (ANN), Bayesian Neural Network (BNN), and random forest (RF)) to predict the pressure drop in Herschel–Bulkley fluids flowing through eccentric and concentric horizontal annuli
Summary
Non-Newtonian fluids have been used extensively in drilling oil and gas wells. The accurate simulation of such complex fluid models aid in energy conservation and efficient operation management.During drilling operation drilling fluid or mud is circulated through drill string and annular space and carries out drilled cutting from wellbore which results in significant pressure loss. Non-Newtonian fluids have been used extensively in drilling oil and gas wells. The accurate simulation of such complex fluid models aid in energy conservation and efficient operation management. During drilling operation drilling fluid or mud is circulated through drill string and annular space and carries out drilled cutting from wellbore which results in significant pressure loss. Hershel and Bulkley [1] proposed a three-parameter model which is considered to be a near optimum model for rheological behavior of Non-Newtonian fluids, and it has been known to replace Bingham and power-law models in recent years [2,3,4,5,6,7,8,9]. Calculation of pressure drop in a wellbore is a critical parameter in
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