Sensitivity of Clay Suspension Rheological Properties to pH, Temperature, Salinity, and Smectite-Quartz Ratio

Understanding the rheological properties of clay suspensions is critical to assessing the behavior of sediment gravity flows such as debris flow or turbidity current. We conducted rheological measurements of composite smectite-quartz suspensions at a temperature of 7 degrees C and a salt concentration of 0.6M. This is representative of smectite-bearing sediments under conditions on the seafloor. The flow curves obtained were fitted by the Bingham fluid model, from which we determined the Bingham yield stress and dynamic viscosity of each suspension. At a constant smectite-quartz mixing ratio, the yield stress and the dynamic viscosity tend to increase as the solid/water ratio of the suspension is increased. In the case of a constant solid/water ratio, these values increase with increasing smectite content in the smectite-quartz mixture. Additional experiments exploring differing physicochemical conditions (pH1.0-9.0; temperature 2-30 degrees C; and electrolyte (NaCl) concentration 0.2-0.6M) revealed that the influence of temperature is negligible, while pH moderately affects the rheology of the suspension. More significantly, the electrolyte concentration greatly affects the flow behavior. These variations can be explained by direct and/or indirect (double-layer) interactions between smectite-smectite particles as well as between smectite-quartz particles in the suspension. Although smectite is known as a frictionally weak material, our experimental results suggest that its occurrence can reduce the likelihood that slope failure initiates. Furthermore, smectite can effectively suppress the spreading distance once the slope has failed

Terminal settling velocity of a single sphere in drilling fluid

The accurate prediction of terminal settling velocity of solid spheres in non-Newtonian liquids is important for various fluid-particle systems such as slurry pipelines, separation processes, hole-cleaning in drilling operations, and mineral processing. The standard practice for the prediction involves an implicit procedure that requires repeated iterations using Newtonian correlations. Wilson et al. developed an explicit method that allows direct (noniterative) prediction of the velocity in non-Newtonian liquids. Although very useful, the original Wilson model has an empirical constraint that limits its application. In this study, experiments are performed to measure the terminal settling velocity of precision spheres in Newtonian liquid (water) and non-Newtonian drilling fluids (Flowzan solutions). The Herschel–Bulkley three parameter model satisfactorily modeled the non-Newtonian rheology. Experimental data and similar measurements available in the literature are presented in this paper. The data exhibited the standard relationship between the drag coefficient and the Reynolds number. The original Wilson model was tested for these data points and was modified in this study to address its limitations. Consequently, it was observed that the modified version yielded more accurate results than the original model. Its prediction was especially better when the value of corresponding Reynolds number was more than 10.The authors would also like to acknowledge the start-up fund provided by Texas A&M University - Qatar. The authors are grateful to the Qatar Foundation, Texas A&M University-Qatar and Qatar University.Scopu

Predicting the pressure losses while the drillstring is buckled and rotating using artificial intelligence methods

The prediction of equivalent circulating density in realistic conditions is complex due to many parameters in effect. Drillstring configuration and motion can play a significant role on the pressure profile in the annulus. Eccentricity, rotation and axial position of the drillstring can cause distinct pressure losses. If an accurate prediction is desired, these effects need to be accounted for. In this study, the pressure losses of Yield Power Law fluids with various drillstring rotation speeds and configurations are analyzed. These configurations include eccentricity and various buckling configurations and rotation speeds of the drillstring. Neural networks are used to predict the pressure losses and the results are compared with the experimental results and existing models from the literature. The input to the neural networks is optimized by comparing using direct measurements and using dimensionless parameters derived from the measurements. The comparison shows that using direct measurements as input yield better results instead of using dimensionless parameters, considering the experimental data used in this study. The results of this study showed that using neural networks to predict the pressure losses in complex geometries and motion showed a better precision compared to the existing models from the literature. The results analysis show that predicting with neural networks can yield as low as 5% absolute average percent error while predicting using existing models can yield as high as 115% absolute average percent error. Using neural networks shows a strong potential to accurately predict the pressure losses especially considering complex fluids and geometries