scholarly journals Experimental study of the effect of adding nanoparticles on the rheological properties of oil-based drilling fluids

2021 ◽  
Vol 2057 (1) ◽  
pp. 012120
Author(s):  
E I Mikhienkova ◽  
A V Minakov ◽  
A V Matveev ◽  
S V Lysakov
Keyword(s):  
Experimental Study ◽  
Rheological Properties ◽  
Systematic Study ◽  
Laboratory Experiments ◽  
Drilling Fluids ◽  
Average Size ◽  
Nanoparticle Additives

Abstract A systematic study of the effect of nanoparticles of various concentrations and sizes on the rheological properties of various oil-based drilling fluids with nanoparticle additives has been carried out. The concentration of nanoparticles in drilling emulsions varied from 0.25 to 2 wt%, and the average size of nanoparticles ranged from 18 to 100 nm. As a result of numerous laboratory experiments, formulations and technology for the preparation of stable oil-based drilling fluids with additives of nanoparticles have been developed. The effect of nanoparticles on the viscosity and rheological properties of these drilling fluids has been studied.

An Experimental Study of the Effect of Nanoparticle Additives on the Rheological Properties of a Suspension

2018 ◽  
Vol 44 (5) ◽  
pp. 367-370 ◽  
Author(s):  
A. V. Minakov ◽  
E. I. Mikhienkova ◽  
A. L. Neverov ◽  
F. A. Buryukin
Keyword(s):  
Experimental Study ◽  
Rheological Properties ◽  
Nanoparticle Additives

Deposition Prediction of Debris Flow Alluvial Fan Based on Experimental Study

2012 ◽  
Vol 518-523 ◽  
pp. 4819-4822
Author(s):  
Jin Feng Liu ◽  
Shun Yang ◽  
Guo Qiang Ou
Keyword(s):  
Experimental Study ◽  
Debris Flow ◽  
Statistical Method ◽  
Laboratory Experiments ◽  
Empirical Models ◽  
Alluvial Fan ◽  
Disaster Prevention ◽  
Average Accuracy ◽  
Hazardous Area ◽  
Debris Flow Disaster

The deposition prediction of debris flow hazardous area is very important for organizing and implementing debris flow disaster prevention and reduction. This paper selected the data base from laboratory experiments and applied the multiple regression statistical method to establish a series of empirical calculation models for delimiting the debris flow hazardous areas on the alluvial fan. The empirical models for predicting the maximum deposition length (Lc), the maximum deposition width (Bmax) and the maximum deposition thichness (Z0) under the condition of different debris flow volumes (V), densities (rm) and slopes of accumulation area (θd) were establised. And the verification results indicated that the established models can predict the debris flow hazards area with the average accuracy of 86%.

scholarly journals Investigation of regulating rheological properties of water-based drilling fluids by ultrasound

2021 ◽  
Author(s):  
Wei-An Huang ◽  
Jing-Wen Wang ◽  
Ming Lei ◽  
Gong-Rang Li ◽  
Zhi-Feng Duan ◽  
...  
Keyword(s):  
Rheological Properties ◽  
Drilling Fluids ◽  
Water Based

Annular Frictional Pressure Losses During Drilling—Predicting the Effect of Drillstring Rotation

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Arild Saasen
Keyword(s):  
Rheological Properties ◽  
Pressure Loss ◽  
Drilling Fluid ◽  
Axial Flow ◽  
Drilling Fluids ◽  
Unstable Flow ◽  
Pressure Losses ◽  
Shear Dependent Viscosity ◽  
Water Based ◽  
Dependent Viscosity

Controlling the annular frictional pressure losses is important in order to drill safely with overpressure without fracturing the formation. To predict these pressure losses, however, is not straightforward. First of all, the pressure losses depend on the annulus eccentricity. Moving the drillstring to the wall generates a wider flow channel in part of the annulus which reduces the frictional pressure losses significantly. The drillstring motion itself also affects the pressure loss significantly. The drillstring rotation, even for fairly small rotation rates, creates unstable flow and sometimes turbulence in the annulus even without axial flow. Transversal motion of the drillstring creates vortices that destabilize the flow. Consequently, the annular frictional pressure loss is increased even though the drilling fluid becomes thinner because of added shear rate. Naturally, the rheological properties of the drilling fluid play an important role. These rheological properties include more properties than the viscosity as measured by API procedures. It is impossible to use the same frictional pressure loss model for water based and oil based drilling fluids even if their viscosity profile is equal because of the different ways these fluids build viscosity. Water based drilling fluids are normally constructed as a polymer solution while the oil based are combinations of emulsions and dispersions. Furthermore, within both water based and oil based drilling fluids there are functional differences. These differences may be sufficiently large to require different models for two water based drilling fluids built with different types of polymers. In addition to these phenomena washouts and tool joints will create localised pressure losses. These localised pressure losses will again be coupled with the rheological properties of the drilling fluids. In this paper, all the above mentioned phenomena and their consequences for annular pressure losses will be discussed in detail. North Sea field data is used as an example. It is not straightforward to build general annular pressure loss models. This argument is based on flow stability analysis and the consequences of using drilling fluids with different rheological properties. These different rheological properties include shear dependent viscosity, elongational viscosity and other viscoelastic properties.

Influence of the Dispersion of the Size of the Si Nanocrystals on their Emission Spectra

1994 ◽  
Vol 358 ◽  
Author(s):  
J. B. Khurgin ◽  
E. W. Forsythe ◽  
S. I. Kim ◽  
B. S. Sywe ◽  
B. A. Khan ◽  
...  
Keyword(s):  
Systematic Study ◽  
Quantum Confinement ◽  
Emission Spectra ◽  
Emission Peak ◽  
Nanocrystal Size ◽  
Pl Spectra ◽  
Si Nanocrystals ◽  
Average Size ◽  
Size Dispersion ◽  
Confinement Model

ABSTRACTA systematic study of the PL spectra of Si quantum nanocrystals in the SiO2 matrix has been performed. The results have been fitted to a quantum-confinement model that includes the nanocrystal size dispersion rather than a specific size of the nanocrystal. This serves as a strong confirmation of the confinement-induced nature of the PL. It has been shown that if the dispersion is taken into account, the position of the emission peak as well as the PL width can always be correlated with the average size of the nanocrystal.

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

2021 ◽  
Author(s):  
Mehrdad Gharib Shirangi ◽  
Roger Aragall ◽  
Reza Ettehadi ◽  
Roland May ◽  
Edward Furlong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Drilling Fluid ◽  
Training Data ◽  
Drilling Fluids ◽  
Hole Cleaning ◽  
Digital Twins ◽  
Wide Range ◽  
Drilling Operations ◽  
Cuttings Bed

Abstract In this work, we present our advances to develop and apply digital twins for drilling fluids and associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series of interconnected models that incorporate the learning from the past historical data in a wide range of operational settings to determine the fluids properties in realtime operations. From several drilling fluid functionalities and operational parameters, we describe advancements to improve hole cleaning predictions and high-pressure high-temperature (HPHT) rheological properties monitoring. In the hole cleaning application, we consider the Clark and Bickham (1994) approach which requires the prediction of the local fluid velocity above the cuttings bed as a function of operating conditions. We develop accurate computational fluid dynamics (CFD) models to capture the effects of rotation, eccentricity and bed height on local fluid velocities above cuttings bed. We then run 55,000 CFD simulations for a wide range of operational settings to generate training data for machine learning. For rheology monitoring, thousands of lab experiment records are collected as training data for machine learning. In this case, the HPHT rheological properties are determined based on rheological measurement in the American Petroleum Institute (API) condition together with the fluid type and composition data. We compare the results of application of several machine learning algorithms to represent CFD simulations (for hole cleaning application) and lab experiments (for monitoring HPHT rheological properties). Rotating cross-validation method is applied to ensure accurate and robust results. In both cases, models from the Gradient Boosting and the Artificial Neural Network algorithms provided the highest accuracy (about 0.95 in terms of R-squared) for test datasets. With developments presented in this paper, the hole cleaning calculations can be performed more accurately in real-time, and the HPHT rheological properties of drilling fluids can be estimated at the rigsite before performing the lab experiments. These contributions advance digital transformation of drilling operations.

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