Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

Abstract

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

Research on measurement method of drilling fluid rheological parameters based on helical pipe

“…Laminar flow: (9) Turbulent flow: (10) Where: λc is friction resistance coefficient of bending pipe; λs is the straight pipe friction resistance coefficient, λs =64/Re (laminar flow), λs =0.3164/R0.25 (turbulent flow); where: (11) Experimental results [18] indicate that the transitional Reynolds number for the transition from laminar to turbulent flow in helical pipes can be expressed as: (12) Where: Rc' is the modified radius of curvature of the Helical pipe, Dem is called the modified Dean number, Rc is the radius of curvature of the Helical pipe, m; d is the diameter of the Helical pipe, m; h is the pitch of the Helical pipe, m.…”

“…Kamran Valizadeh et al [7] employed simulation and developed a nonlinear relationship between Reynolds numbers and friction factors under different flow velocities, showing that the fluid flow remains constant within the fully developed region of the helical pipe at certain Reynolds numbers. Khaled Al-Azani et al [8][9][10][11] utilized artificial neural networks to create predictive models that determine drilling fluid rheological properties based on mud weight, Marsh funnel viscosity, and solid content. To enhance monitoring efficiency and downsize monitoring equipment, a key strategy is to reduce the length of the piping.…”

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

“…Tomiwa established an ANN model with a single hidden layer that contains 15 neurons; the model could be utilized for estimating apparent viscosity, plastic viscosity, and yield point of modified biopolymer WBM based on 100 actual datasets, water volume, biopolymer, and bentonite concentrations, which were selected as input features. Gowida developed another ANN model for predicting WBM plastic viscosity and apparent viscosity using mud weight and marsh funnel viscosity as input parameters, and 200 actual datasets were employed to develop such a model. Gomaa used 814 actual datasets to develop the WBM apparent viscosity ANN predictive model using mud density and marsh funnel viscosity as input parameters.…”