Graphene oxide-zinc oxide nanocomposites as multifunctional materials for thermally stable and high-performance biodegradable water-based drilling muds

Abstract

Effective drilling operations are significantly influenced by the rheological, filtration, and lubricity properties of drilling muds, which directly impact mud flow, rate of penetration, and cuttings transport. However, conventional water-based muds(WBMs) often exhibit poor thermal stability and performance under high-temperature conditions. This study introduces graphene oxide-zinc oxide nanocomposites(GO-ZnO NCs) as multifunctional additives to improve and optimize WBMs performance. Initially, GO-ZnO NCs were synthesized via a solvothermal method and characterized by X-ray diffraction(XRD), field emission scanning electron microscopy(FESEM-EDX), Fourier electron infrared analysis(FTIR), and thermogravimetric analysis. The synthesized nanocomposites were systematically evaluated for their effectiveness in improving the rheological, lubricity, and filtration properties of water-based muds across varying concentrations(0.1-1 wt%) and temperatures (85 − 175). Finally, WBM properties were optimized by using Response Surface Methodology (RSM).Morphology and structural analysis indicate the successful synthesis of GO-ZnONCs. Both the experimental results and RSM analysis showed a clear enhancement in drilling mud performance when GO-ZnO NCs were added. Plastic viscosity, yield point, and gel strength (10-sec and 10-min) increased by 25%,19.8%, 20% and 14.8% respectively. Additionally, a 20% reduction in filtration volume and a 7.1% improvement in lubricity were achieved. Significantly, with increasing temperatures the modified WBMs exhibited minor variation in the measured properties compared with conventional WBMs. The optimum formulation was achieved at 0.87wt% nanocomposite and 137. These findings indicate that GO-ZnO NCs. are a promising, thermally stable additive for high performance WBMs. Future research should evaluate their cost-effectiveness and long-term stability to enable large-scale field application.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-35573-8.

Introduction

The effectiveness of any drilling operations heavily depends on the performance and characteristics of the utilized drilling fluids. They perform several tasks during drilling operations like transporting and suspending the rock cuttings from downhole to the surface (hole cleaning), cooling the drilling bit, controlling the borehole pressure and maintaining the wellbore integrity. These functions purely dependent on the rheological characteristics such as (plastic viscosity, apparent viscosity, yield point and gel strength) and filtration properties of the drilling muds. Consequently, designing proper drilling fluids is crucial for achieving safe and efficient drilling operations^1,2. Water-based drilling mud (WBM) is the most prevalent and recommended type of drilling mud due to inherent lower operation costs, simple preparation, and environmental friendliness. However, improper designs of this mud will cause several borehole problems such as stuck pipe, borehole collapse, formation damage and fluid loss. Therefore, it is necessary to optimize the rheological and filtration characteristics of WBM to enhance its performance.With the current development in nanotechnology, several research studies have examined the possibility of applying nanomaterials such as MWCNT^3–5, Fe₃O4, Fe₂O₃^6,7, SiO₂^8,9, ZnO¹⁰, GO¹¹, CuO¹², Al₂O₃⁷, ZnO¹³,ZnTiO₃¹⁴ as an additives to enhance the rheological and filtration characteristics and mitigate the problems associated with WBM systems. These experimental studies demonstrated that the presence of nanoparticles(NPs) in WBM had a positive impact on its rheological and filtration properties. They can also improve the electrical and thermal characteristics of WBM.

Recent advancement in materials science and engineering has led to significant progress in developing nanocomposites (NCs) material with enhanced physiochemical, thermal, and mechanical properties. Several research studies have been applied on using nanocomposites as WBM additives. The results of these studies revealed a homogenous dispersion of NCs in the WBM, making them perform multiple functions such as controlling fluid loss, maintaining thermal stability, enhancing rheological performance and mitigating shale swelling. In a research study conducted by Mao, et al¹⁵.,the surface of silica nanoparticles was coated by polymer to form polymer-silica nanocomposite which showed outstanding thermal stability, and improved rheological and filtration characteristics¹⁵.Saboori his co-researchers functionalized the surface of copper oxide (CuO) nanoparticles with polyacrylamide (PAM) via solution polymerization method and they used CuO/PAM NCSs as WBM additive to enhance the rheological characteristics and thermal conductivity. The findings of this study showed that with increasing the concentration of CuO/PAM nanocomposites, the viscosity of WBM increased. In addition, the volume of mud loss significantly decreased as a result of excellent pore sealing by CuO/PAM nanocomposites. The thermal conductivity of WBM was also improved¹⁶.

Furthermore, Abdo et al. studied the rheological stability of WBM at HPHT by incorporating ZnO-clay nanocomposites. The experiments exhibit that the viscosity and yield point of WBM with nanocomposites remain relatively stable with increasing pressure and temperature. In contrast, the mud viscosity and yield point decreased significantly without ZnO-clay nanocomposites. This confirms that the ZnO-clay nanocomposites improve the stability of clay dispersion in WBM, by preventing clay particles from flocculation¹⁷. In a research study accomplished by Ahmad, et al¹⁸. the CNT-polymer nanocomposites was synthesized via in-situ emulsion polymerization method to examine its impact on the rheological and borehole stability of WBM at HPHT conditions. The rheological characteristics (PV, YP and Gs) were stable up to 148 with (2 Wt/Vol %) percentage of CNT-Polymer nanocomposites. Additionally, the fluid loss of WBM nanocomposite (modified) was reduced significantly compared to conventional one (unmodified). The results also showed that using 2 wt/vol% of nanocomposite reduced the shale swelling by 46% and the shale dispersion minimized up to 90%. Additional type of nanomaterials that have demonstrated significant impact on the rheological and filtration properties of WBMs are known as graphene oxide (oxygenated version of graphene).

The 2D graphene oxide nanosheets (GONSs) are distinguished through their exceptional physical and chemical properties including large specific surface area and high tensile strength. The surface layers of GONSs are functionalized with epoxide and hydroxyl, while their edges contain carboxyl and carbonyl groups. These functional groups enables the GONSs to be functionalized with various chemical materials, including polymers, organic compounds and nanomaterials, to form graphene oxide nanocomposites (GONCSs)^19–22. GONCs have been investigated in fields such as biomedicine, electronics, and materials science. However, their potential within the oil and gas sector has received comparatively little attention. Existing studies suggest that GO-based nanoparticles could deliver strong performance in various upstream and downstream applications, including enhancing oil recovery^23–25 and drilling operation²⁶. Lalaji and his co-researcher were able to synthesize GO-TiO₂ nanocomposites that improve the rheological properties and to be effective mud loss control agent in WBM systems. Additionally, it showed lower shale swelling than KCl and PAC additives²⁷. The GO nanosheets have proved their effectiveness as mud loss control when modified its surface with polymer²⁸. showed that PAAN-GO nanocomposite capable of providing stable fluid loss at high temperature and high salty conditions. Further experimental investigations were carried out by²⁹ demonstrated that poly-acrylamide-co-acrylic acid GO reduced filtration loss and formed a thin mud cake compared to reference mud. It also improved the lubrication performance of the WBM. Several researchers have explored the role of GONCSs in WBM, highlighting their significant impact on drilling mud performance in Table 1.

Table 1.

Summary of various GONCSs in rheological and filtration characteristics of WBM.

GO Nanocomposites	Experimental condition	Outcomes PV = Plastic Viscosity AV = Apparent Viscosity FL = Fluid loss MC= mud cake	Ref.
GO-Octadecylamine	• Filtration test pressures at 80 psi,120psi and 150 psi. • WBM, WBM + GO, WBM + GO-ODA	• MC thickenss reduced from 1.2 to 1.02 mm and 0.832 mm by adding GO and GO-ODA respectively. • At 150 psi, FL reduced by 25%.	30
Boron Nitride (BN), Titanium Nitride (TN) -GO	• LPLT conditions • Concnetraion (0.5 wt% per each set)	• 40%−60% BN and TiN NCs reduced PV up to 31% and increased YP values up to 61% • MC thickness reduced nearly to 30%. • FL reduced by 35%.	31
Poly(acrylamide-co-acrylic acid)-GO	• LPLT condition • Concentration 0.5 wt%	• PV and AV decreased, YP increased. • FL of base mud by 4% with adding 0.5wt% of nano Poly-AM-GO. • MC of base mud reduced from 1.33 mm to 0.88 mm. • Coefficient of friction (Cof) reduced from 0.46 to 0.43.	29
Glycine-GO	• Concentrations(2.66 wt %,5.32 wt %,10.64 wt %)	• Thermal stability enhanced up to 350 • PV reduced by 33%, YP increased from 10 to 25 Ib/100ft2, FL reduced by 45%, mud cake thickness minimized from 1 ml to 0.4 ml.	11
PAAN -GO	• Aging at 25,150 and 180 • Concentrations: (1wt% PAAN,1wt%PAANG0.2GO, 1wt%PAAN0.5GO)	• PAAN0.2GO, PAAN0.5GO decreased the rheological parameters. • Before aging API FL of PAAN0.2GO 1.4 ml less than PAAN.After aging API FL differences increased to 4.4 and 7 ml.	28
Acrylamide, Acrylic Acid (AC) GO	• LPLT HPHT conditions • Aging 16 h. • Concentration(0.1–0.2.1.2-0.5–0.8.5.8-1) wt%. • GO+ (0–16 wt% NaCl) • GO+(0–1.2.2 wt% CaCl2)	• Optimum concnetration 0.8%. • Before aging: PV and AV incresed with incresing AM/AA/GO. • After aging : PV and AV decresed with increseing temp. • Mud properties decomposed at 140. • As salt concentrations incresed, the mud viscosity decreased. • MF decreased by 60% with increasing AM/AA/GO concentration.	32

From the literature, it can be concluded that limited attention has been given to metal oxide nanocomposites as an additive for enhancing the WBMs properties. In this work, GO-ZnO NCs nanocomposite were synthesized by a solvothermal method. The structure and chemical morphology of prepared GO-ZnO NCs were characterized by X-ray diffraction (XRD), Fourier transforms infrared (FTIR), Field Emission Scanning Electron Microscopy (FESEM-EDX), Thermogravimetric analysis (TGA). A series of laboratory experiments was then performed to evaluate the synergistic effects of GO-ZnO NCs concentration and temperature on the rheological and filtration properties of WBMs. Subsequently, flow characteristics such as plastic viscosity, apparent viscosity, yield point and gel strength were optimized by central composite design (CCD) withing the framework of Response Surface Methodology(RSM) technique.

Experimental design and RSM optimization

Chemicals and materials

The details of materials used for formulating water-based drilling muds and synthesizing GO, ZnO, and GO-ZnO nanocomposites are presented in Table 2. All the chemicals were used as received, without further purification.

Table 2.

Materials used for synthesizing GO-ZnONCs and formulating WBMs.

Materials	CAS number	Supplier(s)	Purpose
Graphite Powder	7782-42-5	HMBG Chemicals	Precursor
Sulfuric acid (H2SO4) (98%)	7664-83-2	BENDSON	Oxidizing agent
Potassium permanganate (KMnO4)	7722-64-7	SIGMA-ALDRICH	Oxidizing agent
Hydrogen peroxide (H2O2)	7722-84-1	SIGMA-ALDRICH	Terminate reaction
Zinc acetate dihydrate (Zn(CH3CO2)2.2H2O)	5970-45-6	SIGMA -ALDRICH	Precusor
triethanolamine N(CH2CH2OH)3	10–71-6	SIGMA-ALDRICH	Stablizer
Hydrochloric acid (37%)	7647-01-0	SIGMA-ALDRICH	pH modifier
Bentonite	1302-78-9	MACKLIN	Viscofier
Barite	7727-43-7	SIGMA-ALDRICH	Weighting agent
Soda ash	497-19-8	MERCK	pH controller
Xanthan gum (XG)	11138-662	MACKLIN	Solid suspension
Poly anionic cellulose (PAC-UL)	244-66-2	MACKLIN	Filtration control agent

Design of experiment and analysis

The experimental design and responses analysis were performed using Design Exprt software (Version 13, Stat-Ease, Inc.), a statistical tool that enables the design and control of experimental variables such as (concentration and temperature)and the assessment of their effects on the selected responses (PV, YP, GS, FL and CoF). In this study, the response surface methodology (RSM) was employed to model the relationship between the input and output factors, thereby aiding in the prediction and optimization of the response variables. RSM is an integral part of the design of experiment and recognized as an effective mathematical tool for optimizing various properties of WBMs³³. Central Composite Design (CCD) model within RSM family was used to establish the mathematical relationship between the variables and responses, as well as to describe the interactions between the variables. One of the primary advantages of RSM-CCD lies in its ability to evaluate and optimize several parameters with fewer experimental runs compared to other methods, thus reducing experiment cost. Moreover, a CCD/RSM serves two main purposes: (i) it efficiently approximate both 1 st and 2nd order terms, and (ii) to models a response variable with curvature by incorporating center and axial point into a factorial design. By applying RSM alongside CCD, researchers can develop a comprehensive mathematical model that clearly demonstrates how variables impact on responses and clarifies the complex interaction among these variables^34,35.

Based on CCD method, a total of 13 experiments were conducted-5 center points per blocks, and 8 axial points, with an alpha of 1.41 as shown in Fig. 1. The lowest temperature was set at 85 to represent surface operating conditions, while the maximum temperature 175 was selected to simulate the downhole conditions. The concentration of GO-ZnO NCs were set at two levels: a minimum of 0.1wt% (Low) and a maximum of 1 wt% (High). This concentration range was selected based on previous studies on graphene oxide nanosheets^37–39. GO-ZnO NCs concentration lower than 0.1wt% may not effectively enhance the properties of WBM, while concentration exceeding 1wt% tends to increase the cost of mud. Table 3 present the levels of each parameter, including low, mid and high content.

Fig. 1.

Central composite design representation.

Table 3.

Experimental design levels for GO-ZnONCs concentration and temperatures presence in CCD.

Parameters	GO-ZnONCs	Temperature
Level	0.1 wt% (Low)	85 (Low)
	1 wt% (High)	175 (High)
Center Point	0.75 wt%	130
Axial Point	0.1wt% (-	85 (-
	1wt% (+	175(+

A fundamental statical method, Analysis of Variance was employed to identify the significant interaction between process variables and responses. The significance of ANOVA lies in its capacity to identify patterns and relationships within data, allowing researchers and decision makers to draw meaningful conclusions and make informed decisions. ANOVA aids to determine whether the observed differences are truly influenced by the independent variables or if they arise from random variations by examining the variance within and between groups. This capability is invaluable for researchers aiming to evaluate the effectiveness of specific factors^24,33,40. The coefficient of determination (R²) and the adjusted (R²) were applied to examine the predictive accuracy of the polynomial model. Where (R²) value close to unity indicates the significance of the model. Additionally, the model is considered to exhibit acceptable agreement when the difference between the predicted (R²) and adjusted (R²) values is less than (0.2). Additionally, the model’s significance was further validated by the probability value (p-value less than 0.05). A lower P-value indicates stronger evidence of meaningful differences between group means, thereby enhancing the reliability of the analysis. In contrast, the F-statistic measures the degree of variation between groups, indicating how strongly their means diverge. Additionally, 3D surface plots and contour plots were generated to provide a visual interpretation on the effects on the independent variables on the responses. The desirability approach was utilized for optimization within Response Surface Methodology (RSM). This method involves transforming predicted responses into a dimensionless desirability value (d). A (d) values range between (0 and 1). A desirability value of (d = 0) indicated unacceptable, while (d = 1) gives optimal or highly desirable outcome. Finally, the model’s validity was then confirmed through a confirmation test, which estimates the error between the experimental values and the predicted response at the optimal settings. The complete experimental design, form the synthesis GO-ZnONCs to the optimization of rheological, filtration, and filtration properties of WBM, is presented in Fig. 2.

Fig. 2.

Experimental procedure flow chart.

Water-based mud formulation

The water-based drilling mud was formulated according to recommended American Petroleum Institute API 13 B-1⁴¹. Table 4 displays the main compositions of the conventional and nano- WBM systems. The reference WBM (R) mud was formulated by adding 0.25 g of soda ash to 320 mL fresh water. After 2 min of mixing with a Hamilton Beah mixer, 1.5 g of high-molecular weight of xanthan gum was added and stirred for 5 min to improve the rheological properties of WBM. Then, 2.25 g of poly-anionic cellulose grade and 15 g of bentonite were added to the solution. Finally, 35 g of barite as a weighting agent was added to the mixture. Followed by 30 min of stirring to get better stability. After that, GO-ZnO nanocomposites were added to the solution in varying concentrations and temperatures based on RSM suggestion. The WBM prepared to obtain mud density of 9.5 ppg.

Table 4.

Drilling mud formulation.

Mud system	Reference Mud	NCs-WBM
Fresh Water (ml)	320	320
Soda ash (g)	0.25	0.25
Xanthan gum (g)	1.5	1.5
Polyanionic cellulose (g)	2.25	2.25
Bentonite(g)	15	15
Barite (g)	35	35
GO-ZnO NCs (wt%)	0	0.1–1.1

Rheological and filtration measurements

Rheological and filtration properties of the reference and nanocomposites-muds were examined at different temperatures and concentrations as suggested by RSM software. The plastic viscosity (PV), apparent viscosity (AV), yield point (YP)and (10s–10 min) gel strength of the formulated muds, were measured by API FANN Viscometer Model-35 A. The process starts with filling the viscometer cup with mud sample. After activating gear switch, the viscometer rotor is operated at various speeds (3,6,300,600 rpm), Their corresponding dial reading were recorded and used to measure the rheological properties after applying the following equations:

1

2

3

Where : PV, AV in cP = 1mPa.s Yp, GS (10s–10 min) in Ib/10ft² = 0.48 Pa.

The relationship between shear rate and AV was measured by Brookfield RST Rheometer. The measurement begins with filling the test cup with 68.5 ml of drilling mud. The device operates after adjusting the software input data such as measuring system (CCT-40), measuring block and analysis, temperature controller (Lauda-ECO), time (300 s), shear rate range (0–1022 s^− 1). In addition, API filter press series 300 was used to measure the filtration (fluid loss) and analyze the potential of mud samples to invade the borehole formations. The mud samples were assessed at 100 psi differential pressure at a standard time of 30 min. All the tests were conducted using API Recommended Practice 13B-1.

Lubricity measurements

The mud lubricity was evaluated at various concentrations, using the OFITE lubricity tester. Before testing, the rotating cup first cleaned with deionized water for 15 min to eliminate any residual impurities from previous trials test. Once cleaned, the mud samples were poured into the metal rotating cup, and 150 inch-pounds of torque was then applied to determine the coefficient of friction (CoF) after applying the following standard equations:

4

5

Where : CF = Correction Factor, CoF = Coefficient of friction.

Synthesizing methods

Synthesis of GONSs

Multiple layers graphene oxide nanosheets were synthesized according to a modified Hummer’s method⁴². Briefly, 5 g of graphite powder (less than 20 size)was dispersed in a mixture of 120 ml of sulphuric acid (H₂SO₄)and 1.5 g Sodium Nitrate (NaNO₃). This solution was immersed in an ice bath to keep the temperature between (0–10) with continuously stirring for 4 h. Then, 15 g of potassium permanganate (KMnO₄) was gradually added to the above solution with stirring and the temperature was maintained lower than 20 to prevent overheating. After 30 min, the solution was heated to 35 for 45 min. A block -green thick paste was formed. After that, a total of 150 ml of deionized water was added to the solution and slowly heated to 98 and stirred continuously for 45 min.

Afterward, 30 ml (30 Wt% of aqueous solution) of Hydrogen peroxide (H₂O₂) was added to the prepared solution and stirred for 45 min. The color of the mixture was changed from dark green to bright yellow, due to the reduction of Mn₂⁺ to Mn₃⁺. The resulting mixture was subjected to centrifugation at 4000 rpm for 25 min and the precipitate was then dispersed in 5wt% HCl to remove residual metal ions. The dispersion was subsequently centrifuged again and rinsed with DI water at least 2 times to remove the acid. Finally, the product was filtered with Whatman filter paper to obtain clean GO. the filter cake of GO was dried under 100 over 24 h and ground into fine powder. Figure 3shows the synthesis processes of GO nanosheets using modified Hummer’s method.

Fig. 3.

Synthesizing procedure of GONSs.

Synthesis of ZnO NPs

Zinc oxide nanoparticles were synthesized by Sol-Gel preparation method^43,44. Zinc acetate dihydrate (Zn(CH₃CO₂)₂.2H₂O) was used as precursors, triethanolamine (TEA) as the stabilizer and absolute ethanol as the solvent. Begin with preparing solution (1) by dissolving 2 g of zinc acetate dihydrate in 15 ml ethanol and stirring about 1 h. to obtain a transparent solution. Then, a separate solution (2) was prepared for increasing the hydroxyl group of zinc acetate dihydrate by adding 8 gm of sodium hydroxide (NaOH) in 10 ml of DI water and stirring for about 15 min. Consequently, the second solution was added to the first solution with continuous stirring. The chemical reaction is shown to be:

After the chemical reaction, the resulting precipitate was centrifuged and washed with DI water. The final product was dried at 80 for 2 h. to obtain ZnO nano powder. Figure 4 illustrates the schematic diagram for the synthesizes of ZnO nanoparticles.

Fig. 4.

Preparation of ZnO Nanoparticles.

Synthesis of GO-ZnO nanocomposites

The GO-ZnO nanocomposites were synthesized by solvothermal method^43,45,46. In this method, 150 mg obtained GONSs powder was dispersed into 15 ml of ethanol and the solution was mixed homogeneously by ultrasonic bath for 60 min at 25. Separately, 150 mg of ZnO powder was dispersed in 15 ml of ethanol. Then, both solutions were mixed using ultrasonic bath for 30 min at 25.The final mixture was transferred to Teflon-lined autoclave and heated at 170 over night. The final products were centrifuged and washed with ethanol and deionized water 3 times and dried at 60 for 12 h. Finally, 1Wt% of GO-ZnO nanocomposites were obtained as shown in Fig. 5. The synthesized procedure of GO-ZnO NCs is shown in Fig. 6.

Fig. 5.

Chemical structure of synthesized GO-ZnO NC.

Fig. 6.

Procedure of Synthesizing GOZnONCs.

Structural characterization techniques

The structure, size and morphology of the synthesized GO, ZnO, and GO-ZnO nanocomposites were investigated using several analytical techniques including X-ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM), Fourier transform infrared spectroscopy (FTIR), Thermogravimetric (TGA) and Zeta potential.

X-Ray diffraction (XRD) analysis

Rigaku Smart Lab, Advance XRD with scan range (2) from 0 to 90 and scan speed 2 min^{− 1} was performed to determine the mineral composition or crystalline structure for synthesized materials. The sample powders were placed in a sample holder and compacted by a plexigalss. The average crystalline diameter (size) was calculated using the following Debye-Scherrer formula⁴⁷:

6

Where: D = Average crystalline size⁴⁸, K = Dimensionless Scherrer factor (0.98), = (1.5046 A^o)X-Ray wavelength, = Full width of half-maximum(FWHM), = Bragg diffraction angle.

The following equation is used to measure the degree of crystallinity of the synthesized materials.

7

Field emission scanning electron microscopy

Surface morphology of synthesized GO, ZnO and GO-ZnO NCs were carried out using Field Emission Scanning Electron Microscopy (FESEM), Hitachi (SU8020) and Zeiss Crossbeam 340, following standard procedures. The surface of samples was coated with platinum for 15 min, then placed on aluminum pins using carbon tape for stability. In addition to FESEM, Energy-Dispersive X-ray (EDX) analysis was conducted for elemental analysis synthesized materials.

Fourier transform infrared analysis

The chemical structure and surface functional groups of the GO, ZnO and ZnO-GO NCs were confirmed by Fourier -transform infrared spectroscopy (FTIR) model (PERKIN ELMER − 2000). This analysis measures the absorbed electromagnetic infrared radiation (IR) by the substance and plotted against wavelength. (0.02 g) sample powder was mixed with (0.02 mg) of potassium bromide (KBr) and pelletized. Finally, 100 psi pressure was applied by hydraulic press. The spectrometer operated with wavelengths range from 4000 to 400 cm^− 1.

Thermogravimetric analysis

The thermal stability and degradation behavior of GO, ZnO, and GO-ZnO NCs were evaluated Perkin Elmer 4000 thermo-gravimetric analysis (TGA). In this analysis, the change in sample weight was continuously recorded as the materials were exposed to controlling heating program. Around 1–2 mg of each dried material was placed in a sealed aluminum crucible attached to a high precision microbalance. The sample heated up to 480 at a uniform heating rate 10/min under nitrogen atmosphere.

Zeta Potential () analysis

The colloidal properties such as surface charge of synthesized GO, ZnO and GO-ZnO NCs were estimated by zeta potential, Malven Zetasizer (ZSP).

Results and discussion

GO-ZnO nanocomposites structure and morphology

XRD analysis of GO-ZnO NCs

The X-ray diffraction patterns of GO, ZnO, and GO-ZnO nanocomposites are shown in Fig. 7. The XRD pattern of pure GO showed a shifted curve with high peak (2-theta) at 9.74 and increasing in the interlayer distance (d) to 0.73 which is align with results reported in the synthesize GONs⁴⁹. This proves the successful synthesis of GO, aligning with the results from existing research literature^49–51 and International Center for Diffraction Data (ICDD), 03–065-1528 DB card number. In addition, the degree of crystalline is10.5%. Increasing the interlayer distance (d-spacing) is primarily due to the existence of oxygen functional groups in the GONSs and intercalation of (H₂O) molecules within carbon layered structure of carbon material. In contrast, the XRD analysis of synthesized ZnO shows that all diffraction peaks are indexed within standard wurtzite structure of ZnO which is also consistent with ICDD, DB card number 01–078-3341⁵². The major characterized reflections peaks observed in the XRD of ZnO are located at 31.7, 34.6, 36.2, 47.48, 56.65, 62.8, and 67.9which correspond to crystallographic plane of (100), (002),(101),(102),(110),(112),and(201)respectively. The mean crystalline size of ZnO was calculated from the most intense peak using Debye-Scherrer [D-S] equation. The average particle size of ZnO was in nanomaterials scale with 39 nm. The calculated crystalline percentage of ZnO NPs is 65.07%.

Fig. 7.

XRD of synthesized GONSs., ZnO NPs and GO-ZnONCs.

The X-ray diffraction patterns of GO-ZnO NCs show the presence of all ZnO peaks without any observable shifting. While two weak diffraction peaks located at 2-theta values of 9.26 and 21.2 generated form the corresponding planes of (001)and (002) respectively confirm that the ZnO NPs were successfully functionalized onto the surface of GO. According to Scherrer formula the average particle size of GO-ZnO NCs was estimated to be 30.8 nm. All the obtained results are in good agreement with the previously reported data^45,53. The crystallinity percentage of the GO-ZnO nanocomposites is 36.2%.

FTIR analysis of GO-ZnO NCs

The surface functional group of synthesized GO, ZnO, and GO-ZnO NCs were examined using FTIR spectra method, as shown in Fig. 8. The FTIR analysis of GO confirmed the existence of hydroxyl, carboxyl, and carbonyl functional groups on the basis of GO. The main peak that appeared at 3487 cm^{− 1} is attributed to the stretching vibrations of the hydroxyl (O-H) atoms. The carbocylic acid (C = O) stretching vibration exhibiting a maximum value at 1718 cm^{− 1} suggests the presence of carbonyl and carboxyl groups at the edges of the GO. The peak observed at 1610 cm^{− 1} is characteristic of aromatic (C = C) bonds. Additionally, the peaks at 1160 cm^{− 1} were assigned to carboxy (C–OH) stretching vibrations. The results of ZnO FT-IR spectra exhibited a broad absorption at 3400 cm^{− 1}, indicating the (O-H) stretching. The peak around 1600 cm^{− 1} is assigned to (H-O-H) bending. A strong and sharp peak below 500 cm^{− 1} verify the characteristic of Zn-O stretching vibrations. Similar results have been reported in previous studies^52,54.

Fig. 8.

FTIR of GONs, ZnO NPs and GO-ZnO NCs.

The FTIR spectra of the GO-ZnO NCs shows a wide absorption band that confirms the presence of GONSs and ZnO NPs within the composite. A wide absorption band at 3308 cm^{− 1} corresponds to the (O-H) stretching vibration due to the moisture or carboxylic acid functionalized on the GO surface. A sharp peak at 1612 cm^{− 1} is attributed to (C = O) stretching from carbonyl groups. The (O-H) peak is shown at 1388 cm^{− 1} further confirming the presence of functionalities on the GO surface. Strong absorption bands can be noticed at 963 cm^{− 1}, 881 cm^{− 1} and 775 cm^{− 1} and 712 cm^{− 1} due to the Zn-O stretching vibrations. Both ZnO NPs and GO-ZnONCs showed an intense peak below 500 cm^{− 1}, which corresponds with a 2nd order vibration (E₂) mode associated with hexagonal crystal structure of the ZnO structure. Shifting of stretching bands arises from strong chemical interactions between oxygen containing functional groups of GO and Zn^{+ 2} ions on the ZnO surface. Additionally, the shift is often associated with the particle reduction of GO, in which oxygenated groups are removed or transformed during composite formation^43,55.

Surface morphology analysis of GO-ZnO NCs

The surface morphology and structure of GO, ZnO and GO-ZnO NCs were characterized by FESEM, as shown in Fig. 9⁵⁶. The FESEM images reveal that GO has a 2D film with multiple-sheets structure. The sheets appear thin transparent and closely stacked with each other. In addition, several nano wavy wrinkles regions can be also observed on GO surface. The flexible nature of the GO is also evident, and the GO nanosheets appear overlapped with each other, as shown in Fig. 9(a). According to Fig. 9(b), ZnO NPs show a semispherical to slightly irregular surface. This smoothness indicated to well-crystallized material. It can be observed that ZnO nanocrystals tend to cluster or agglomerate in the suspension due to high surface energy. The FESEM showed that average thickness of the ZnO was in the range of 50–80 nm, while the XRD (Debye-Scherrer) calculation gave a smaller average thickness of 39 nm. This differences because XRD measures the size of a single crystallite inside the particle, whereas FESEM measures the whole particle or agglomerated structure. A single FESEM particle contains several smaller crystallites which make the particle size appear larger. In addition, the Scherrer equation often underestimates size because it does not account instrumental broadening or lattice strain effects. As shown in Fig. 9 (c), ZnO NPs are successfully decorated and adhered onto the surface layer of GONSs, creating a sandwich-like composite structure. This structure arrangement will significantly prevent the agglomeration of GO sheets⁵³. It can be observed that the nanocrystalline structure of ZnO evolved by GONSs, which exhibited a typical wrinkled texture It can be also observed that the ZnO nanoparticles tend to disrupt the original structure of the GO sheets, leading to its modification or even partial destruction.

Fig. 9.

FESEM images of (A) GONSs. (B) ZnO Nps (C) GOZnONCs.

EDX analysis was performed on the synthesized GO, ZnO and GO-ZnO NCs to confirm their elemental composition, with the corresponding results presented in Fig. 10. The EDX spectrum of GO shown in Fig. 10 (a) depicted that GO has 65.7 wt% (C), 27.5 wt% (O) and 6.8 wt% (S) as impurities. However, in Fig. 10 (b) we can see pure ZnO contained only (Zn) and (O) elements. The measured elemental composition showed that rate of Zn to O is about 1:0.92 The spectrum confirmed that the atomic composition of GO-ZnO NCs is composed of 44.15% (C), 33.8% (Zn), and 22.05% (O) as shown in Fig. 10 (c). In addition, the EDX does not detect any elemental impurities in the GO-ZnO NCs, which confirms that the synthesizing method is successfully good quality GO-ZnO NCs.

Fig. 10.

EDX Spectra of (a) GO (b) ZnO and (c) GO-ZnONCs.

Zeta potential

Zeta()potential as a key indicator of colloidal stability and provides insight into particle surface charge. The zeta-potential values of GO, ZnO and the GO-ZnO NCs are demonstrated in Fig. 11. It is obvious that zeta potential of all synthesized materials exhibits negative charge. At a neutral pH (7) value, the zeta potential for GO, ZnO and GO-ZnO are (−17.38) mV, (−42.3)mV and (−50.2)mV, respectively. The high zeta potential value of GO-ZnO NCs indicates strong electrostatic repulsion and good dispersion and suspension stability.

Fig. 11.

Zeta potential analysis of GO, ZnO and GO-ZnO NCs.

Thermogravimetric analysis

A detailed assessment of the volatile composition of the synthesized materials is illustrated in Fig. 12. The TGA profile of the ZnO nanoparticles demonstrated negligible weight loss (0.5%) when the sample heated up from room temp to 175. The total weight loss was at 480 was only (1.5%). This weight loss is due to the removal of adsorbed water on the surface of nanoparticles⁵⁷. The initial weight loss for GO up to 175 (43%) primarily attributed to evaporation of absorbed water. The total weight loss of about (62.1%) due to breaking down oxygenated functional groups (hydroxyl, epoxy.etc)⁵⁶. In contrast, GO-ZnO NCs displayed high thermal stability till 175 with (5.8%) mass loss and more gradual decomposition pattern, retaining more mass at elevated temperatures (51.7%). This improvement indicated that ZnO incorporation stabilized the Go structure and limits volatile released through strong interfacial interactions.

Fig. 12.

TG thermogram of GO, ZnO and GO-ZnO NCs.

Mathematical modelling

Response surface methodology was conducted to design the experiment and statically analysis the outcomes. The following regression model Eqs. (8–13) gives the best fit of the experimental results :

8

9

10

11

12

13

Where : A = NCs concentration B = Temp.

Using the mentioned equations, the predicted values of the response can be determined. Equations from 8 to 13 with positive sign indicating a direct or positive effect on the responses, while coefficient with negative sign suggest an inverse or negative effect.

Model validation and uncertainty analysis

To statically validate the lab’s outcomes, ANOVA was utilized to evaluate the relationships between process input variables and output responses. The model adequacy and compatibility were also evaluated by ANOVA, ensuring the models effectively represent the interactions within the experimental data. These data were analyzed using multiple regression models—including linear, interaction, 2FI, quadratic, and cubic forms, to identify the most accurate and representative predictive equations. Both the coefficient of determination (R²) and the P-Value were considered to gain insight into the significance of the relationships between model variables. A model is considered significant if the P-Value is less than [0.05] and R² is close to unity⁵⁸. The model exhibits a high degree of statical significance, as p-values for all the responses lower than 0.001. Since the p-value less that 0.05 is widely recognized in engineering analysis³⁴. The reproducibility of the model according to coefficient of variance (CV) should be less than 10%⁴⁰. The CV for PV, YP, GS, CoF and fluid loss were equal to 2.7, 1.42, 4.06,1.68,1.61 and 0.55 respectively. The detailed ANOVA analysis results are presented in Table 5.

Table 5.

ANOVA evaluation of the model.

Properties	Model Source	F-Value	P-Value	R 2	C.V.%
PV	Quadratic	57.4	0.0001	0.97	2.7
YP	Quadratic	102.5	0.0001	0.98	1.42
GS-10 s	Quadratic	15.09	0.0012	0.915	4.06
GS-10 min	Quadratic	93	0.0001	0.985	1.68
CoF	Quadratic	13.65	0.0017	0.90	1.61
Filtration	Quadratic	129	0.0001	0.99	0.55

Model evaluation and diagnostics analysis

To evaluate the adequacy of the models, diagnostic plots illustrating the correlation between predicted and experimental values were employed. The experimental response data was compared with the predicted values produced by the model. An appropriate distribution of data points roughly aligned with a straight line showed the model’s capability to precisely predict the responses values⁴⁰. Figure 13 demonstrate the adequacy of the model as the predicted data shown an acceptable level of distribution with the actual experimental data.

Fig. 13.

Predicted values versus actual experimental values for (a) Pv (b) YP (c) Fluid loss (d) Lubricity.

Synergic effect of process variables on the responses

The effect of two independent process variables such as GO-ZnONCs concentrations and temperatures on the PV, YP, GS10sec-10 min, lubricity and filtration volume were examined. Thirteen experiments have been suggested by RSM, including 3 different ranges of temperatures and NCs concentrations. The experimental results of the responses are shown in Table 6.

Table 6.

Rheological, filtration and lubricity properties developed by RSM.

Run	NCs Conc.	Temp.	PV	YP	GS 10-sec.	Gs 10-min	Lub. CoF	Filtration Volume
	Wt%		cP	Ib/100ft 2	Ib/100ft 2	Ib/100ft 2	0.26	Cc.
1	1	85	15.5	26	4.5	15.5	0.28	7.8
2	0.55	85	14	24.5	4.3	15	0.283	8
3	0.55	130	13	21.9	3.5	13	0.29	9
4	0.55	175	10	18.9	3.5	12	0.27	11.2
5	1	175	12.5	21	3.7	13	0.283	10.9
6	0.55	130	13	21.9	3.8	13	0.265	9
7	1	130	14.5	24	4.2	14	0.3	8.6
8	0.1	85	12	22	4	14	0.308	8.5
9	0.1	130	11	20.8	3.5	12	0.283	9.5
10	0.55	130	13	21.9	3.8	13.5	0.283	9
11	0.55	130	13	21.9	3.8	13.5	0.283	9
12	0.55	130	13	21.9	3.5	13.5	0.268	9
13	0.1	175	9.5	18.5	3	10	0.26	11.6

Effect of input variables on the rheological properties

The effect of different GO-ZnO NCs concentration, shear rate and temperature on the apparent viscosity of the WBM is illustrated in Fig. 14. As shear rate increases, the apparent viscosity decreases for both WBM and GO-ZnO NCs muds, confirming the typical shear-thinning behavior of drilling muds. This reduction occurs because, at higher shear rates, the long-chain polymer additives and dispersed nanocomposites become more aligned in the flow direction, minimizing internal resistance and allowing the mud to flow more easily. Such behavior helps to hold and remove the rock cuttings in both static and dynamic conditions⁵⁹. The drilling mud exhibited a slight increase in apparent viscosity with increasing the GO-ZnO NCs concentration. The maximum improvement was observed at 0.5wt% which may suggest that at this concentration, the nanocomposites interact optimally with the polymer additives, enhancing structural stability. In contrast, as temperature increased from 85 to 175, a minor reduction in apparent viscosity is observed for all nanocomposite mud systems. This reduction can be attributed with weakened interparticle forces and increased space between drilling mud additives and nanocomposites at elevated temperature, which finally reduces the overall structural integrity of the mud.

Fig. 14.

Effect of GO-ZnO NCs concentrations and temperatures on mud apparent viscosity.

The resistance generated by friction between solid particles and fluid layers within the mud system is known as plastic viscosity (PV). Generally, muds with high PV are more challenging to pump due to increased internal resistance, whereas muds with low PV may be insufficient for effectively transporting drilled cuttings³⁴. According to API, the recommended value of PV for any mud system should be in range of 8–35 cP. Based on the experiment results, PV values of the mud samples are in range of API -recommended as shown in Fig. 15 (a).It is observed that the PV of the reference mud increased by 25% with increasing NCs concentrations. This improvement of PV is attributed to the good dispersion of NCs within the fluid, which contributes to improving the mud internal resistance. However, with increasing temperature from 85 to 175, the PV values for both reference mud and nanocomposite mud decreased. This behavior can be attributed to the thermal thinning occurs due to weakening of intermolecular forces and polymer-nanocomposites interaction, resulting in decreased internal resistance to flow. However, the temperature effect on the nanocomposite mud is comparatively less than the reference mud. For instance, at 175 the PV of reference mud is minimized by 29%,while at a nanocomposite concentration of 1wt%, the reduction was limited to 19%. These results indicate that nanocomposite mud has better temperature resistance or stability compared to reference mud in terms of PV.

Fig. 15.

Effect of GO-ZnO NCs concentration and temperature on the PV (a) Experiment data (b) 3D surface generated by RSM.

The curvature of the 3D surface plot generated by RSM Fig. 15(b) indicates a clear interactive effect between nanocomposite concentration and temperature on PV. The 3D surface shows a positive gradient along nanocomposite axis and a negative gradient along the temperature axis, confirming that PV increases with GO-ZnO concentration and decreases slightly with increasing temperature.

The initial resistance of mud to flow is known as yield point (YP). This resistance is caused by electrochemical forces acting between solid particles. The ability of the mud to transport rock cuttings from the borehole to the surface largely depends on its YP. A mud with higher YP improves the mud lifting capacity and provides effective borehole cleaning⁵⁹. The results of conventional WBMs with various NCs concentrations and temperatures are shown in Fig. 16(a). It is observed that that with increasing NCs concentrations the YP improved by 19.8%. This improvement may be attributed to the adhesion on NCs within polymeric structure of the drilling mud. It also reduces the differential pipe sticking and provides better hole cleaning²⁷. Conversely, as the temperature increased, the YP decreased, likely due to thermal weakening of the interaction between the NCs and mud additives. However, the rate of reduction in YP for the nanocomposite WBM was lower than base mud. The smooth, upward surface of 3D map in Fig. 16(b) suggests good model fitting, indicating that GO-ZnO NCs concentration and temperature significantly affects the YP.

Fig. 16.

Yield point of WBMs at different NPs concentrations and Temp. (a) Experiment (b) 3D map generated by RSM.

Gel strength measures the capability of drilling mud to suspend rock cuttings. A sufficient GS is required to effectively suspend rock debris in the borehole under static conditions. However, if the gel strength exceeds certain limit, the mud pumps will be unable to recirculate the mud⁶⁰. It can be observed from the Fig. 17(a, b) that, with increasing NCs concentrations up to 1wt%, the 10-sec. and 10-min. gel strength enhanced by 20% and 14.8%,respectively. Moreover, increasing temperature results in a reduction in both10-sec and 10-min GS values; however, the rate of reduction in nanocomposite mud is less than reference mud. For example, drilling mud formulated with 1wt% GO-ZnO NCs showed a 17% and 16% reduction in the 10-sec. and 10-min gel strength, respectively at 175. while the reduction rate for the reference mud without GO-ZnONCs. was 25% and 18.8% under same conditions. These findings indicate that GO-ZnONCs. mud at elevated temperatures can effectively mitigate flocculation and thereby minimize the decrease in GS.

Fig. 17.

Effect of Temp. and NCs. concentration on gel strength (a) 10-sec. (b) 10-min.

Effect of input variables on filtration volume

Mud loss occurs due to the pressure difference between the drilled formation and the wellbore. Drilling muds with higher filtration volumes are generally considered undesirable, as they can lead to the invasion of solid particles into the formation, thus inducing wellbore instability, formation damage and pipe sticking. The filtration behavior of WBMs is directly dependent on the concentration and physical properties of the colloidal materials that are present in the WBM^14,61. API filtration results obtained from mud samples are presented in Fig. 18(a). It is obvious that the filtration volume decreased gradually with increasing GO-ZnO NCs Fluid loss of the reference WBM is about 9.4 cc after 30 min. At the same time the maximum 20% (7.2 cc) reduction in API filtration is found at 0.75wt% of the GO-ZnONCs. concentration. In addition, the WBM samples with 0.1 wt%, 0.55wt% and 1 wt% GO-ZnO NCs. reduced the filtration volume to 8.5,8 and 7.8 cc respectively (9.5,14.8% and 17% less compared to base mud).This result implies that GO-ZnO NCs can form an efficient and effective bridge among solid particles. Figure 18(b) illustrates a clear negative correlation between GO-ZnO NCs concentration and filtration volume, while temperature shows a positive correlation. The curvature of the surface indicates a strong interactive effect higher NCs. loading mitigates the adverse influence of temp. on API filtration performance.

Fig. 18.

(a, b) Filtration volume of WBMs at different GO-ZnONCs and temperatures.

Effect of input variables on mud lubricity

Mud lubricity plays a significant role in controlling and minimizing the continuous friction between drilling strings and borehole. One of the notable challenges associated with WBMs during drilling directional or extended horizontal well is relatively high friction (drag and torque). Therefore, managing friction is one of the key challenges in drilling operation. Figure 19(a) shows the effect of different NCs concentrations on mud lubricity at different temperatures. It is obvious that increasing NCs concentrations reduced the mud lubricity and resulted in a reduction of mud friction. Adding 1wt% of NCs with WBM reduced the CoF by 7.1%. This improvement can be attributed to the role of NCs acting as nano-scale ball bearing between the two contact surfaces, the metal block and machine interface²⁹. On the other hand, a slight increase in CoF was observed at high temperature for both reference and nanocomposite muds. This is because the PAC polymer in WBMs will face thermal degradation and lubrication efficiency between two contact surfaces become unstable. At 175, the CoF of reference WBM is increased by 14.9%,while at 1wt% of GO-ZnO NCs. WBM, the CoF is increased only by 6.9%., because the polymer degradation is less due to thermal conductivity of GO-ZnO NCs. Figure 19(b) generated by RSM highlights inverse correlation between GO-ZnO concentration and CoF, confirming that positive influence of GO-ZnO NCs effectively offsets the adverse influence of elevated temperature on the lubricity of drilling mud.

Fig. 19.

(a, b) WBMs lubricity at various GO-ZnONCs concentrations and temperatures.

Optimization process of WBM properties

During WBM formulation it is essential to improve the rate of penetration by enhancing mud viscosity. At the same time, the yield point should be sufficiently high to ensure the efficient transport of rock cuttings to the surface and reduce the possibility of cutting accumulation. Furthermore, the filtration rate should be also minimized to avoid invasion of solid particles to the formation which can lead to wellbore stability. In this study, a desirability function approach with range between [d 0 and d1] was utilized to identify the optimal values of response parameters, with the goal of optimizing the rheological, filtration and lubricity properties of WBM and the goal for GO-ZnO NCs and temperature were assigned as minimum and maximum values respectively. A confirmation test was performed to evaluate the percentage of absolute error (POAE), which quantifies the ratio of the difference between the actual and predicted values to the actual value. This assessment was conducted for the response parameters under the identified optimal conditions. Figure 20 illustrate the ramp graph of the input and optimized output responses. It can be noticed from the ramp graph that at 0.87 Wt% GO-ZnONCs and 137, the optimum values of PV, YP, GS10sec-10 min, filtration volume and CoF were obtained.

Fig. 20.

Optimization results developed by RSM.

Experimental work was performed to confirm the accuracy of the optimum values developed by RSM. Then absolute error (POAE) equation was applied to measure the differences between actual experimental and RSM predicted values. According to⁶²,if the POAE is less than 10%, the equations are considered reliable..

14

The optimization values suggested by RSM model and a comparison between predicted and actual experimental values are presented in Table 7. It can be noticed that POAE for all the mud properties are below 10%. Statistically, these minimum error values in the responses under optimal conditions demonstrate the precision and consistency of the experimental findings. Overall, the POAE values indicate a strong agreement between the predicted and actual experimental outcomes.

Table 7.

Rheological, filtration and lubricity properties of WBM in optimal conditions.

Responses	Optimal Output
Mud properties	RSM Predicted Value	Experimental Actual Value	POAE (%)
PV (cP)	13.8	14.5	4.8%
YP (Ib/100ft2)	22.8	21.4	6.5%
GS10sec (Ib/100ft2)	3.9	4	2.5%
GS10min(Ib/100ft2)	13.7	13	5.3%
Filtration volume (cc)	9.0	8.5	5.8%
Lubricity (CoF)	0.27	0.28	3.8%

Qualitative analysis of cost factor and toxicity risks

The selection of drilling-mud additives for a specific well requires a strategic balance among economic feasibility, technical performance and environmental compatibility. A drilling mud may be technically viable, but it might not be applied in drilling operations if it is not economically feasibility or fails to meet environmental and regulatory requirements. In water-sensitive shale formation and HPHT formation, the use of oil-based muds substantially increases overall operations expenditure due to higher materials costs. Therefore, the incorporation of GO-ZnO NCs into water-based muds can offer a cost-effective alternative to oil-based muds, and they also reduce the environmental footprint. The synergistic structure of GO-ZnO NCs, characterized by a high -surface area to-volume ratio, provides multiple operational and economic advantages. Notably, the required concentration of GO-ZnO NCs to enhance the rheological and filtration properties of WBMs is substantially lower than that of traditional additives, thereby improving overall cost efficiency. Furthermore, the ability of GO-ZnO NCs to enhance thermal conductivity contributes to better heat dissipation around the drill bit, potentially extending bit service life, reducing replacement frequency, and minimizing drilling operation downtime. In addition, any excessive increase in filtrate loss directly contributes to additional expenditure, so uncontrolled filtrate invasion into the formation can reduce productivity by inducing formation damage. Based on the results, GO-ZnO NCs reduced the mud filtration by forming compacted filter paper, thus preventing water invasion into formation.

Although the application of GO-ZnO NCs in WBMs remains under investigation, their potential health and environmental impact cannot be neglected⁶³. examined the cytotoxic and neurotoxic behavior of GO-ZnO on the fruit fly (Drosophila melanogaster).The finding revealed that the impact of GO-ZnO on cytotoxic was intermediate. Conversely, the ZnO affected the neuromuscular, while the effect of GO was less. In another study conducted by⁶⁴, toxicity of GO-ZnO NCs were evaluated on Drosophila melanogaster (hsp70-lacZ)Bg⁹for 24 h., and 48 h. exposure periods. The finding showed that at a concentration of 3.996 mg/ml was toxic, while 0.033 mg/ml produced no observed toxicity. Beyond biological toxicity⁶⁵, reported that the high surface area to volume ratio of GO decorated with ZnO NPs showed strong adsorption efficiency for removing heavy-metal pollutant from acid mine drainage and shows good potential for wastewater remediation applications⁶⁶. examined the impacts of GO and ZnO on Scenedesmus obliquus, daphnia magna and freshwater fish (Danio rerio) aquatic organisms. The findings showed mixture toxicity depends on the trophic level of the organism tested. Previous studies have examined the photocatalytic degradation of GO-ZnO NCs against several dye pollutants. For example, photocatalytic degradation of GO-ZnO against methylene blue (MB) was 88% within 4 h⁶⁷., methyl orange (MO) 99% within 1.5 h⁶⁸. In addition, MB, MO and Rh-B (Rhodamine B) degraded up to 96%,58% and 89%, respectively after exposing to GO-ZnO NCs under visible light^69,70. Recently⁷¹, conducted experiment study on Congo red (CR) dye pollution which pose serious threat on environment. The GO-ZnO NCs showed enhanced photocatalytic degradation of CR (84.7%).

Overall, current studies support GO-ZnO NCs as a highly effective, eco-friendly photocatalysts with substantial potential for addressing dye pollution, enhanced sunlight-driven photocatalysts. The studies indicated that the GO-ZnO NCs have potential for large-scale removal of both organic and inorganic contaminants from polluted water systems. GO-ZnO NCs are highly effective candidate for use in environmental cleanup applications.

Conclusion

In this work, GO-ZnO NCs were synthesized via a solvothermal method and characterized using XRD, FTIR, FESEM-EDX, TGA, and Zeta potential analyses. The effects of different GO-ZnO NCs concentrations and temperatures on the rheological, filtration, and lubricity properties of WBMs were examined through laboratory experiments. Overall, the outcomes demonstrate that GO-ZnO NCs function as an efficient, thermally stable material capable of improving WBM performance in field drilling operations. The key findings of this study can be summarized as follows :

1. Characterization analyses confirmed the successful synthesis of GO-ZnO NCs, showing a multiple sheet-like GO structure decorated with spherical ZnO nanoparticles.

2. Incorporating GO-ZnO NCs into WBM significantly enhanced PV and YP performance by 25% and 19.8% respectively, due to the uniform dispersion and strong adhesion of nanocomposites within polymeric structure of the mud. This improvement increased internal resistance, reduced differential pipe sticking. In addition, the GS improved by 20%(10-sec) and 14.8%(10-min) improving cutting suspension and hole cleaning efficiency.

3. Fluid loss reduced by 20% at 0.75wt%, indicating effective formation of sealing and bridging network withing the filter cake. Mud lubricity improved by 7.1%, attributed to the role of Go-ZnO NCs acting as nano-scale-ball bearing at metal-surface interface.

4. GO-ZnO NCs demonstrated better thermal stability, with only slight property variation at elevated temperatures.

5. RSM analysis exhibited strong agreement between predicted and experimental values, with POAE less than 6.5%, and identified the optimal formulation at 0.87wt% NCs and a temperature of 137 F.

6. Despite the promising outcomes, this study has several constraints that need to be considered. First, the long-term stability and reusability of GO-ZnO NCs in WBMs were not examined. Second, the study focused mainly on rheological, filtration and lubricity properties, while other aspects, such as shale swelling and dispersion and density effects were beyond its scope. Finally, an evaluation of cost-effectiveness is necessary to support the feasibility of large-scale field applications.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ANOVA

Analysis of variance

API

American petroleum institute

AV

Apparent viscosity

CCD

Central composite design

CoF

Coefficient of friction

FESEM

Field emission scanning electron microscopy

FTIR

Fourier transform infrared spectroscopy

FL

Fluid loss

GO-ZnO NCs

Graphene oxide-zinc oxide nanocomposites

GONSs

Graphene oxide nanosheets

Gs

Gel-strength

HPHT

High pressure high temperature

LPLT

Low pressure low temperature

NCs.

Nanocomposites

NPs

Nanoparticles

OBM

Oil-based mud

PAC-UL

Polyanionic cellulose -ultra low

POAE

Percentage of absolute error

PV

Plastic viscosity

ROP

Rate of penetration

RSM

Response surface methodology

TEA

Triethanolamine

WBM

Water based mud

XG

Xanthan gum

XRD

X-Ray diffraction

YP

Yield point

ZnO

Zinc oxide

2D

Two-dimensional

Author contributions

**Ahmed R.AlBajalan**; Conceptualization, Formal analysis, Methodology, Resources, Writing-Original draft, Writing-review and editing. **A.A.A.Rasol**; Supervision, Formal analysis, Resources, Funding acquisition, writing -original draft, writing review and editing. **M.N.A.M.Norddin**; Supervision, Formal analysis, Writing-review and editing.

Funding

This research was supported by UTM Fundamental Research Grant (UTMFR) (Q.J130000.3846.23H10) and Collaborative Research Grant (CRG) (Q.J130000.3046.10G31).

Data availability

The data will be available from the corresponding author based on request.

Declarations

Competing interests

The authors declare no competing interests.