A Novel Prediction Model of the Drag Coefficient of Shale Cuttings in Herschel–Bulkley Fluid

Sun, Xiaofeng; Sun, Minghao; Li, Zijian

doi:10.3390/en15124496

Cited by 4 publications

(6 citation statements)

References 42 publications

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“…This aspect holds substantial importance for potential applications in cold environments, where conventional lubricants may encounter difficulties concerning their flow and effectiveness. The rheological characteristics of nanobiolubricant samples, characterized by their non-Newtonian behavior, were assessed using the Herschel-Bulkley model (Equation ( 1)) on the MCR-92 rheometer [38,39]. Each test case was performed twice to minimize the errors and generate robust data for analysis and comparison.…”

Section: Rheological Measurementsmentioning

confidence: 99%

Machine Learning-Based Predictive Model to Assess Rheological Dynamics of Eco-Friendly Oils as Biolubricants Enriched with SiO2 Nanoparticles

Hariharan,

Navada,

Brahmavar

et al. 2024

Lubricants

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Efficient machinery operation relies on the performance of high-quality lubricants. Currently, mineral oils of different grades are widely employed for lubricating machine components, but their environmental impact is a concern. Biolubricants are potential alternatives to mineral oils due to environmental factors. The present study focuses on assessing the rheological characteristics of SiO2 nanoparticle (NP)-enhanced ecofriendly biolubricants for near zero and high-temperature conditions. Pure neem oil, pure castor oil and a 50:50 blend of both oils were considered as the base oils. Nanobiolubricants with enhanced dispersion stability were prepared for varied concentrations of NPs using an ultrasonification method. Viscosity analysis was conducted using an MCR-92 rheometer, employing the Herschel Bulkley model to precisely characterize the viscosity behavior of bio-oils. Due to the fluid–solid interaction between SiO2 NPs and bio-oils, a crossover trend was observed in the flow curves generated for different base oils enriched with SiO2 NPs. For neem oil, a significant increase in viscosity was noted for 0.2 wt% of NPs. Using the multilayer perceptron (MLP) algorithm, an artificial neural network (ANN) model was developed to accurately predict the viscosity variations in nanobiolubricants. The accuracy of the predicted values was affirmed through experimental investigations at the considered nanoSiO2 weight concentrations.

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Section: Rheological Measurementsmentioning

confidence: 99%

Machine Learning-Based Predictive Model to Assess Rheological Dynamics of Eco-Friendly Oils as Biolubricants Enriched with SiO2 Nanoparticles

Hariharan,

Navada,

Brahmavar

et al. 2024

Lubricants

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“…On the basis of the proposed C D -Re s model, the Newton iteration approach can be adopted for calculating the sedimentation resistance parameter C D and sedimentation velocity, Vs, of the particles in the fluid. The iteration procedure is shown in Figure 3 [18].…”

Section: Theoretical Analysismentioning

confidence: 99%

A Review of the Settling Law of Drill Cuttings in Drilling Fluids

Hou,

Yuan,

Chen

et al. 2023

Processes

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During the drilling process, cuttings settle under the action of gravity, which easily results in the formation of a cuttings bed, which then results in wellbore cleaning problems. The settling law of cuttings in drilling fluid is essentially a problem of solid–liquid two-phase settling. This study analyzes and summarizes the effects of the wall effect, the rheology of the fluid, particle shape irregularity, and particle concentration on the settling rate of particles and clarifies the problems faced by current research on the settling rate of particles and the development direction. Studies have shown that walls exert additional blocking effects on particles, thus reducing their settling velocity. The shear thinning effect of non-Newtonian fluids such as power-law fluids and Herschel–Bulkley fluids will reduce the viscosity of the liquid, thus increasing the settling velocity of the particles. Compared with spherical particles, irregular particles will obtain higher resistance in the fluid, leading to a decline in the particle settling velocity. The mutual interference between particles will result in an increase in the drag force on the particles and a decline in the settling velocity. However, when the particles are aggregated, the settling velocity will increase. This study can provide theoretical guidance for predicting the migration law of cuttings during the drilling of horizontal wells, and it has important significance for enriching the theory of solid–liquid two-phase flow.

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“…Experimental data for 114 spherical particle groups were brought into Formulas ( 5) and (7), and the values of CD and Res-p were calculated, respectively. The relationship between CD and Res-p was drawn in logarithmic coordinate form, referring to Figure 5.…”

Section: Spherical Particle Settling In the Power-law Fluidmentioning

confidence: 99%

“…According to Formula (4), the resistance coefficient CD can be calculated according to the properties of fluid and particles and the settling velocity, vs. The expression is as follows [7]: F D is the force from the fluid if a single particle moves in the fluid. It is the key coefficient describing the momentum and energy transfer between the fluid and the particle.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids

Hou,

Jiang,

Chen

et al. 2023

Processes

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The settlement drag coefficient of irregular particles in power-law fluids is a crucial parameter in the field of petroleum engineering. However, the irregular shape of the particle and the non-Newtonian rheological properties of the fluid make it challenging to predict the settlement drag coefficient. In this study, the spherical and irregular particle sedimentation processes in power-law fluids have been analyzed using a visual device and a high-speed camera system. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of 114 spherical particle experimental results, and a prediction model of the drag coefficient of spherical particles in the power-law fluid was established with a mean relative error of 3.85%. On this basis, considering the influence of geometric shape on the law of particle sedimentation, a new irregular particle sedimentation resistance coefficient model in power-law fluid is established via the incorporation of the parameter circularity of 2D shape description c into the spherical particle sedimentation resistance coefficient predictive model. The parameters in the new irregular particle sedimentation resistance coefficient predictive model can be obtained via nonlinear data fitting of the 211 groups of irregular particles using experimental results in the power-law fluid. The model has high prediction accuracy for the drag coefficient of irregular particles in power-law fluid, with a mean relative error of 4.47, and expands the scope of engineering applications, which is of great significance for fracturing scheme design and wellbore cleaning.

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A Novel Prediction Model of the Drag Coefficient of Shale Cuttings in Herschel–Bulkley Fluid

Cited by 4 publications

References 42 publications

Machine Learning-Based Predictive Model to Assess Rheological Dynamics of Eco-Friendly Oils as Biolubricants Enriched with SiO2 Nanoparticles

Machine Learning-Based Predictive Model to Assess Rheological Dynamics of Eco-Friendly Oils as Biolubricants Enriched with SiO2 Nanoparticles

A Review of the Settling Law of Drill Cuttings in Drilling Fluids

A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids

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