**5. Results and discussions**

#### **5.1. Characterization of graphene oxide**

**Tables 1** and **2** show the product specification of graphene oxide paste and vegetable oil by the local supplier respectively.


**Table 1.** Product specification of graphene oxide paste.


**Table 2.** Product specification of hydrogenated oil-based fluid.

Two characterizations were used to characterize graphene oxide paste, namely FTIR analysis and TEM analysis. **Figures 4** and **5** show FTIR spectra analysis and TEM imaging of graphene oxide paste respectively.

At 3500 cm−1 range, O-H group is present in graphene oxide as shown in **Figure 4** and is further supported by the findings of Farbod et al. [17]. Absorption peak between 1630 and

**Figure 4.** FTIR spectra analysis of graphene oxide.

**Parameters Specifications**

Carbon content (%) >99.8 Oxygen content (%) <0.05 Thermal conductance (W/m K) 2800 X-Y dimensions (μm) 0.06–1 Z dimensions (μm) 0.002–0.005

Initial boiling point (°C) <300 Final boiling point (°C) <330 Flash point (°C) 90

**Table 2.** Product specification of hydrogenated oil-based fluid.

) 780 (at 15°C)

/s) 2–2.6 (at 40°C)

1.3 mm diameter × 60 mm length) which complies with ASTM D5334-14 standards. The parametric studies in thermal conductivity analysis are divided into three categories, mainly the effect of temperature, the effect of nanoparticle concentrations and the effect of nanoparticle types. The temperature parameter in this study is set within the ranges of 30–50°C with a 5°C increase at each interval step. The presence of nanoparticles suspended within each sample move freely under elevated temperature, prompting fluctuations in thermal conductivity results. Therefore, each sample was repeated three times to ensure mean thermal conductivity is obtained. Further detailed explanation on the method can be

The parametric studies considered for the rheological properties analysis are viscosity values and shear stress values of hydrogenated base oil nanofluid with respect to shear rate and temperature. Rheological properties were measured using Malvern Bohlin Gemini II Rheometer

**Tables 1** and **2** show the product specification of graphene oxide paste and vegetable oil by

Density (kg/m<sup>3</sup>

Kinematic viscosity (mm<sup>2</sup>

found in our earlier work [48].

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**4.3. Rheological properties analysis**

**5. Results and discussions**

the local supplier respectively.

**5.1. Characterization of graphene oxide**

**Table 1.** Product specification of graphene oxide paste.

following the method discussed in our earlier work [56].

**Figure 5.** TEM image of graphene oxide at 31,500× (left) and 80,000× (right) magnifications.

1730 cm−1 is assigned to C=O stretching of carboxylic and specific carbonyl functional groups [41]. The remaining peaks confirms the presence of carbon–carbon bonds which constitutes primarily from graphene sheets.

Stacking of graphene oxide sheets were outlined at 31,500× magnification in **Figure 5** that shows folds and bends existing on the surface of graphene oxides. Schniepp et al. [42] explained that functionalized graphene sheets are distinctively different from graphene with the attachments of epoxy, hydroxyl and carboxyl groups on graphene sheets. The attachments of these groups posed lattice defects during thermal reduction process leading to the formation of defects on the surface of graphene oxides [42].

#### **5.2. Thermal conductivity analysis**

The effects of temperature and nanoparticle concentrations on thermal conductivity analysis was investigated. The analysis for graphene oxides dispersed in hydrogenated oil-based fluids were carried out at temperature of 30°C, 40°C and 50°C nanoparticle concentrations at 25 ppm, 50 ppm and 100 ppm.

**Figure 6.** Thermal conductivity comparison of graphene oxide-hydrogenated oil nanofluid at 25 ppm, 50 ppm and

**Temperature (°C)**

25 30 0.1394 0.00518

50 30 0.1464 0.00723

100 30 0.1397 0.00437

**Table 3.** Thermal conductivity analysis of graphene oxide-hydrogenated oil based nanofluid with respect to different

**Mean thermal conductivity (W/mK)**

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 0.1408 0.00349 0.1770 0.00914 0.1898 0.01242 0.1958 0.02191

 0.1396 0.00659 0.1753 0.02404 0.1830 0.03290 0.1940 0.03401

 0.1543 0.01409 0.1640 0.01284 0.1736 0.02755 0.2044 0.04361

**Standard deviation** 

**(W/mK)**

100 ppm.

Graphene oxide– hydrogenated oil

**Nanofluid type Concentration** 

particle concentration and temperature.

**(ppm)**

Thermal conductivity of hydrogenated oil-based nanofluids as shown in **Figure 6** and **Table 3** increases linearly with temperature similar to the conclusions of other researchers [21, 43]. The increased in thermal conductivity values were regarded to the effects Brownian motion and micro-convection of nanoparticles induced by at higher temperature [19]. The influences of phonons, molecular diffusion and collision, and free electrons plays a vital role in this scenario [43]. Higher temperature provides better transfer of heat with regards to high phonon vibrations while intense molecular collisions enable better thermal conductivity between nanoparticles suspended.

Furthermore, **Figure 6** highlighted the dependency of thermal conductivity with respect to nanoparticle loadings. This trend is more apparent as high particle concentration contributes to higher collision between nanoparticles which prompted better diffusion and conductance of heat [43]. The same trend can be observed from the findings of other researchers [44]. Although thermal conductivity increment rate is apparent, it does not increase anomalously. The slight fluctuations in were attributed to the clustering of nanoparticles which prompted instability of the nanofluid suspensions. The choice of base fluid with low thermal conductance influences the suspension of nanoparticles into aggregates due to the immense intermolecular attraction force at a nanoscale at increasing concentration [45]. In **Figure 6**, 100 ppm showed lower thermal conductivity as compared to 25 ppm and 50 ppm concentration at 35°C onwards. This phenomenon is regarded to clustering of nanoparticles formed from high volume concentrations resulting in nanoparticle instability in the nanofluid. Agglomerations can provide local heat percolations that improves thermal conductivity [23] at sufficient higher energy input as shown at 50°C, however, the tendency of nanoparticles clustering that leads to settling of aggregates due to lower energy input into the system will result in reduced overall thermal conductivity enhancement. Furthermore, aggregations formed leads to larger variation in particle distribution and poses stability problems at higher temperature [45]. Hence, this could also be the contributing factor to the decrease in thermal conductivity

1730 cm−1 is assigned to C=O stretching of carboxylic and specific carbonyl functional groups [41]. The remaining peaks confirms the presence of carbon–carbon bonds which constitutes

Stacking of graphene oxide sheets were outlined at 31,500× magnification in **Figure 5** that shows folds and bends existing on the surface of graphene oxides. Schniepp et al. [42] explained that functionalized graphene sheets are distinctively different from graphene with the attachments of epoxy, hydroxyl and carboxyl groups on graphene sheets. The attachments of these groups posed lattice defects during thermal reduction process leading to the forma-

The effects of temperature and nanoparticle concentrations on thermal conductivity analysis was investigated. The analysis for graphene oxides dispersed in hydrogenated oil-based fluids were carried out at temperature of 30°C, 40°C and 50°C nanoparticle concentrations at

Thermal conductivity of hydrogenated oil-based nanofluids as shown in **Figure 6** and **Table 3** increases linearly with temperature similar to the conclusions of other researchers [21, 43]. The increased in thermal conductivity values were regarded to the effects Brownian motion and micro-convection of nanoparticles induced by at higher temperature [19]. The influences of phonons, molecular diffusion and collision, and free electrons plays a vital role in this scenario [43]. Higher temperature provides better transfer of heat with regards to high phonon vibrations while intense molecular collisions enable better thermal conductivity between

Furthermore, **Figure 6** highlighted the dependency of thermal conductivity with respect to nanoparticle loadings. This trend is more apparent as high particle concentration contributes to higher collision between nanoparticles which prompted better diffusion and conductance of heat [43]. The same trend can be observed from the findings of other researchers [44]. Although thermal conductivity increment rate is apparent, it does not increase anomalously. The slight fluctuations in were attributed to the clustering of nanoparticles which prompted instability of the nanofluid suspensions. The choice of base fluid with low thermal conductance influences the suspension of nanoparticles into aggregates due to the immense intermolecular attraction force at a nanoscale at increasing concentration [45]. In **Figure 6**, 100 ppm showed lower thermal conductivity as compared to 25 ppm and 50 ppm concentration at 35°C onwards. This phenomenon is regarded to clustering of nanoparticles formed from high volume concentrations resulting in nanoparticle instability in the nanofluid. Agglomerations can provide local heat percolations that improves thermal conductivity [23] at sufficient higher energy input as shown at 50°C, however, the tendency of nanoparticles clustering that leads to settling of aggregates due to lower energy input into the system will result in reduced overall thermal conductivity enhancement. Furthermore, aggregations formed leads to larger variation in particle distribution and poses stability problems at higher temperature [45]. Hence, this could also be the contributing factor to the decrease in thermal conductivity

primarily from graphene sheets.

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**5.2. Thermal conductivity analysis**

25 ppm, 50 ppm and 100 ppm.

nanoparticles suspended.

tion of defects on the surface of graphene oxides [42].

**Figure 6.** Thermal conductivity comparison of graphene oxide-hydrogenated oil nanofluid at 25 ppm, 50 ppm and 100 ppm.


**Table 3.** Thermal conductivity analysis of graphene oxide-hydrogenated oil based nanofluid with respect to different particle concentration and temperature.

performance at higher nanoparticle concentrations due to stability issues. Although presence of oxygen groups provides better stability and paved interlayer interactions [21] for improved thermal conductivity properties, the resulting shear pressure induced by hydrodynamic cavitation process have strained the structure of nanoparticle and in return, affects the stability of graphene oxide in this study.

The findings of this study are compared against our previous experimental work [46, 47] where graphene nanosheet and carbon nanotubes nanoparticles were selected. From **Figure 7**, graphene nanosheets have higher thermal conductivity as compared to carbon nanotube and graphene oxide by a slight margin at 50°C and 100 ppm. The high surface area to volume ratio and intrinsic thermal conductivity values of graphene nanosheets [48] contribute hugely to the increase in thermal conductivity of nanofluids at very low nanoparticle concentrations. Furthermore, larger sheet sizes attract one another and for conducting percolation pathway to conduct heat more efficiently [27], providing better thermal conductivity of graphene nanosheet and graphene oxide nanofluids as compared to carbon nanotube nanofluids.

A comparison between experimental data and the classical thermal conductivity models is shown in **Figure 8**. Similar to graphene nanosheets, graphene oxides are graphene sheets functionalized with oxide groups attached on the surface of the nanosheets. The Maxwell model is able to predict closely at 100 ppm as compared to lower particle concentrations. According to Gupta et al. [49], the contradictions between graphene nanosheets and graphene oxides is possibly influenced by different particle sizes. Gupta et al. [49] had compared the

> sizes in their study and other researchers which lead to the conclusion of the role of particle sizes in the distribution and network formation for heat transfer. There have been various researches to improve Maxwell model with the inclusion of particle sizes [50] which can further improve the predictions of the models. In general, prediction of the models improved with respect to concentration, but consideration of higher nanoparticle concentrations should

> **Figure 8.** Comparison between experimental data and thermal conductivity models at 25, 50 and 100 ppm graphene

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**Figure 9** shows the relationship of viscosity, shear rate and shear stress against the rheological behaviour of hydrogenated oil-based nanofluid. At low shear stress, Bingham fluids behave similar to a solid but flows in liquid manner when adequate stress is applied as shown in **Figure 9** (right) while Newtonian fluid will display constant viscosity regardless of the shear rate applied. From here, we can infer hydrogenated oil-based fluid to follow a non-Newtonian

**Figure 10** showed the comparison of viscosity profiles at 30°C, 40°C and 50°C with respect to increasing logarithmic shear rates and nanoparticle concentrations. The addition of graphene oxide does not influence the viscosity profile of pure hydrogenated oil-based fluid as shown in **Figure 9**. At higher shear rates, the viscosity of the nanofluids are closely similar at all con-

be modelled as well for a more accurate prediction by the models.

behaviour profile with a shear thinning behaviour.

centrations with viscosity values overlapping each other.

**5.3. Rheological analysis**

oxide concentration.

**Figure 7.** Thermal conductivity enhancement comparison between graphene nanosheets, carbon nanotubes and graphene oxide at 50°C and 100 ppm.

Bio-Based Oil Drilling Fluid Improvements through Carbon-Based Nanoparticle Additives http://dx.doi.org/10.5772/intechopen.74674 77

**Figure 8.** Comparison between experimental data and thermal conductivity models at 25, 50 and 100 ppm graphene oxide concentration.

sizes in their study and other researchers which lead to the conclusion of the role of particle sizes in the distribution and network formation for heat transfer. There have been various researches to improve Maxwell model with the inclusion of particle sizes [50] which can further improve the predictions of the models. In general, prediction of the models improved with respect to concentration, but consideration of higher nanoparticle concentrations should be modelled as well for a more accurate prediction by the models.

#### **5.3. Rheological analysis**

**Figure 7.** Thermal conductivity enhancement comparison between graphene nanosheets, carbon nanotubes and

performance at higher nanoparticle concentrations due to stability issues. Although presence of oxygen groups provides better stability and paved interlayer interactions [21] for improved thermal conductivity properties, the resulting shear pressure induced by hydrodynamic cavitation process have strained the structure of nanoparticle and in return, affects the stability of

The findings of this study are compared against our previous experimental work [46, 47] where graphene nanosheet and carbon nanotubes nanoparticles were selected. From **Figure 7**, graphene nanosheets have higher thermal conductivity as compared to carbon nanotube and graphene oxide by a slight margin at 50°C and 100 ppm. The high surface area to volume ratio and intrinsic thermal conductivity values of graphene nanosheets [48] contribute hugely to the increase in thermal conductivity of nanofluids at very low nanoparticle concentrations. Furthermore, larger sheet sizes attract one another and for conducting percolation pathway to conduct heat more efficiently [27], providing better thermal conductivity of graphene nanosheet and graphene oxide nanofluids as compared to carbon nanotube nanofluids.

A comparison between experimental data and the classical thermal conductivity models is shown in **Figure 8**. Similar to graphene nanosheets, graphene oxides are graphene sheets functionalized with oxide groups attached on the surface of the nanosheets. The Maxwell model is able to predict closely at 100 ppm as compared to lower particle concentrations. According to Gupta et al. [49], the contradictions between graphene nanosheets and graphene oxides is possibly influenced by different particle sizes. Gupta et al. [49] had compared the

graphene oxide at 50°C and 100 ppm.

graphene oxide in this study.

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**Figure 9** shows the relationship of viscosity, shear rate and shear stress against the rheological behaviour of hydrogenated oil-based nanofluid. At low shear stress, Bingham fluids behave similar to a solid but flows in liquid manner when adequate stress is applied as shown in **Figure 9** (right) while Newtonian fluid will display constant viscosity regardless of the shear rate applied. From here, we can infer hydrogenated oil-based fluid to follow a non-Newtonian behaviour profile with a shear thinning behaviour.

**Figure 10** showed the comparison of viscosity profiles at 30°C, 40°C and 50°C with respect to increasing logarithmic shear rates and nanoparticle concentrations. The addition of graphene oxide does not influence the viscosity profile of pure hydrogenated oil-based fluid as shown in **Figure 9**. At higher shear rates, the viscosity of the nanofluids are closely similar at all concentrations with viscosity values overlapping each other.

Interparticle frictions increase due to higher concentrations of nanoparticle suspended, thus highlighting the fluid's resistance to flow and subsequently increased the viscosity of nanofluids [43]. Furthermore, temperature parameter plays an important role in viscosity properties. Lower viscosity values are obtained at higher temperature and vice versa for constant nanoparticle concentrations due to the influence of temperature on the intermolecular attractions between nanoparticles and base fluid's particles. Interparticle and intermolecular adhesive forces of particles decreased at higher temperature because of higher energy input into the system [53], leading to the decrease of fluid's viscosity. This phenomenon was also observed by other researchers showing the effects of temperature against

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Two different behaviours of hydrogenated oil-based nanofluids can be deduced from this study, namely shear thinning and shear thickening behaviours. The nanofluids displayed shear thinning behaviour at lower shear rate while slight shear thickening behaviour was observed at higher shear rate instead due to percolation structure effects of nanoparticles suspension in base fluid [27, 54]. At high shearing rates, the formed percolation structure is

The rheological behaviour of hydrogenated oil-based nanofluids over the range of 30–50°C at concentrations 25 ppm, 50 ppm and 100 ppm were compared with non-Newtonian rheological

**Figure 11.** Comparison between experimental data and rheological models at different concentrations at 30°C, 40°C

broken down to form primary particles to form higher shear stress.

viscosity of nanofluids [19].

and 50°C.

**Figure 9.** Graphical illustrations of shear stress (left) and viscosity (right) with respect to shear rate of hydrogenated oil-based fluid at 30°C.

**Figure 10.** Comparison between viscosities at different concentration with respect to shear rate at 30°C, 40°C and 50°C.

At higher shear rate, the viscosity of nanofluids at various concentrations is seen decreasing exponentially towards the viscosity of the base fluid. However, upon closer inspection showed viscosity of hydrogenated oil-based nanofluids are higher compared to its base fluid counterpart. Similar to other studies carried out, the viscosity of nanofluids increases with higher concentrations but reduces when subjected to higher shear rates [51–52]. Interparticle frictions increase due to higher concentrations of nanoparticle suspended, thus highlighting the fluid's resistance to flow and subsequently increased the viscosity of nanofluids [43]. Furthermore, temperature parameter plays an important role in viscosity properties. Lower viscosity values are obtained at higher temperature and vice versa for constant nanoparticle concentrations due to the influence of temperature on the intermolecular attractions between nanoparticles and base fluid's particles. Interparticle and intermolecular adhesive forces of particles decreased at higher temperature because of higher energy input into the system [53], leading to the decrease of fluid's viscosity. This phenomenon was also observed by other researchers showing the effects of temperature against viscosity of nanofluids [19].

Two different behaviours of hydrogenated oil-based nanofluids can be deduced from this study, namely shear thinning and shear thickening behaviours. The nanofluids displayed shear thinning behaviour at lower shear rate while slight shear thickening behaviour was observed at higher shear rate instead due to percolation structure effects of nanoparticles suspension in base fluid [27, 54]. At high shearing rates, the formed percolation structure is broken down to form primary particles to form higher shear stress.

The rheological behaviour of hydrogenated oil-based nanofluids over the range of 30–50°C at concentrations 25 ppm, 50 ppm and 100 ppm were compared with non-Newtonian rheological

**Figure 11.** Comparison between experimental data and rheological models at different concentrations at 30°C, 40°C and 50°C.

**Figure 10.** Comparison between viscosities at different concentration with respect to shear rate at 30°C, 40°C and 50°C.

**Figure 9.** Graphical illustrations of shear stress (left) and viscosity (right) with respect to shear rate of hydrogenated

oil-based fluid at 30°C.

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At higher shear rate, the viscosity of nanofluids at various concentrations is seen decreasing exponentially towards the viscosity of the base fluid. However, upon closer inspection showed viscosity of hydrogenated oil-based nanofluids are higher compared to its base fluid counterpart. Similar to other studies carried out, the viscosity of nanofluids increases with higher concentrations but reduces when subjected to higher shear rates [51–52]. models consisting of Bingham Plastic model and Power Law model as shown in **Figure 11** respectively. In **Figure 11**, it was observed 25 ppm concentration of graphene oxide dispersed yields the lowest shear stress while 50 ppm has the highest shear stress values at higher shear rates. A plausible explanation to this anomaly could be the increased stacking of graphene oxide sheets trapped due to the spindle's rotating movement. Comparisons have shown Bingham model gave better predictions of graphene oxide-hydrogenated oil-based nanofluids compared to Power Law model. The Power Law model over-predicted the shear stress values at higher shear rate due to the flow behaviour index at *n* > 1. Furthermore, hydrogenated oil-based nanofluid has comparatively similar yield stress values at different concentrations at higher temperatures. The rheological behaviour of hydrogenated oil-based nanofluids approaches a limit in which the shear stress values are independent to the concentration of nanoparticles at high temperature [55].

**Author details**

, Suzana Yusup1

\*, Vui Soon Chok<sup>2</sup>

1 Chemical Engineering Department, Biomass Processing Laboratory, Centre for Biofuel and Biochemical Research, Institute for Sustainable Living, Universiti Teknologi PETRONAS,

3 Petroleum Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia

[2] Grand View Research. Drilling Fluids Market Size to Reach \$12.55 Billion By 2024 [Internet]. [Updated: August 2017]. Available from: http://www.grandviewresearch.

[3] Amani M, Al-Jubouri M, Shadravan A. Comparative study of using oil-based mud versus water-based mud in HPHT fields. Advances in Petroleum Exploration and

[4] Udoh FD, Itah JJ, Okon AN. Formulation of synthetic-based drilling fluid using palm oil ester derived ester. Asian Journal of Microbiology, Biotechnology and Environmental

[5] Amorin R, Donsunmu A, Amankwah RK. Enhancing the stability of local vegetable oils (esters) for high geothermal drilling applications. Journal of Petroleum and Gas

[6] Ismail A, Kamis A. Performance of the Mineral Blended Ester Oil-Based Drilling Fluid Systems. 12-14 June; Calgary. Calgary,Alberta: Petroleum Society of Canada; 2001. p. 4.

[7] Al-Yasiri MS, Al-Sallami WT. How the drilling fluids can be made more efficient by using nanomaterials. American Journal of Nano Research and Applications. 2015;**3**(3):41-45.

[8] Xie S, Jiang G, Chen M, Deng H, Liu G, Xu Y, et al. An environment friendly drilling fluid system. Petroleum Exploration and Development. 2011;**38**(3). DOI: 10.1016/S1876-

[9] Growcock FB, Patel AD. The Revolution in Non-Aqueous Drilling Fluids. April 12-14, 2011; Houston. Houston, Texas: American Association of Drilling Engineers; 2011

\*Address all correspondence to: drsuzana\_yusuf@utp.edu.my

[1] BP Statistical Review of World Energy 2016. London, UK. 2016

com/press-release/global-drilling-fluids-market [Accessed: June 2016]

Development. 2012;**4**(2). DOI: 10.3968/j.aped.1925543820120402.987

Engineering. 2015;**6**(8). DOI: 10.5897/JPGE2015.0215

2 KL-Kepong Oleomas Sdn. Bhd, Selangor, Malaysia

and Sonny Irawan<sup>3</sup>

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81

Yee Ho Chai1

Perak, Malaysia

**References**

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3804(11)60040-2

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From the experimental analysis, hydrogenated oil-based fluid exhibits a non-Newtonian behaviour. Although the fluid exhibited zero shear stress at low temperature, the decreased in viscosity of hydrogenated oil-based fluid exhibited shear-thinning properties. However, the flocculation structure of nanoparticles was broken apart to form primary particle which led to slight shear thickening behaviour at higher shear rates. Similar to other findings, higher concentration of nanoparticles exhibits higher viscosity and shear stress properties but variations are insignificant upon comparison. Furthermore, the shear stress values are independent to the concentration of nanoparticles dispersed at higher temperature. The comparison between Bingham model and Power Law model showed Bingham model predicting better results data as compared to Power Law model at all concentrations, nanoparticle types and temperature.
