Design and use of an online drilling fluid pipe viscometer

New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

(2021) Ahmed Alsabaa et al.   FLOW MEASUREMENT AND INSTRUMENTATION

Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System

(2020) Ahmed Alsabaa et al.   SENSORS

Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network

(2020) Ibrahim Gomaa et al.   Journal of Natural Gas Science and Engineering

Viscosity Models for Drilling Fluids—Herschel-Bulkley Parameters and Their Use

(2020) Arild Saasen et al.   Energies

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

(2019) Ahmed Gowida et al.   Energies

Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

(2019) Emad A. Al-Khdheeawi et al.   Energies

Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks

(2019) Elkatatny   Sustainability

Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)

(2016) Salaheldin Elkatatny et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Static pressure measurement error at a wall tap of a flow nozzle for a wide range of Reynolds number

(2015) Noriyuki Furuichi et al.   FLOW MEASUREMENT AND INSTRUMENTATION