SPE MS. 1. Introduction

Transcription

1 All final manuscripts will be sent through an XML markup process that will alter the LAYOUT. This will NOT alter the content in any way. SPE MS Density and Viscosity Prediction of Super Lightweight Completion Fluid (SLWCF) at Reservoir Conditions Zulhelmi Amir, Badrul Mohamed Jan, Ahmad Khairi Abdul Wahab, Munawar Khalil, Brahim Si Ali, Chong Wen Tong, Center for Energy Science and Faculty of Engineering, University of Malaya; Mohd Kamal Sareh, Mohd Rashidi Shafi i, 3Phase Reservoir Technology Sdn Bhd Copyright 2016, IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition This paper was prepared for presentation at the IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition held in Singapore, August This paper was selected for presentation by an IADC/SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the International Association of Drilling Contractors or the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the International Association of Drilling Contractors or the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the International Association of Drilling Contractors or the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of IADC/SPE copyright. Abstract This paper presents the effect of reservoir conditions, specifically temperature and pressure, on the rheological behavior and density of super lightweight completion fluid (SLWCF) for underbalanced perforation. In this study, fluid s density was measured at various temperatures and pressure ranging from K to K, and 0.1 MPa to 25 MPa, respectively. Meanwhile, fluid s viscosity was measured at temperature between K to K, and pressure range of 0.1 MPa and 4.48 MPa. In order to understand the effect of reservoir conditions to the density and viscosity of the fluid, experimental data were fitted to several density-/viscosity-temperature-pressure models and then the generated results were statistically evaluated. Based on the results, it is observed that the Tait-like equation was able to satisfactorily express the relationship between the density, pressure and temperature. The predicted density values based on the Tait-like equation are also in good agreement with the regressed model results. For the case of fluid s viscosity, it is found that both modified Mehrotra and Svrcek s and Ghaderi s equation were the best equation for viscosity prediction. Using these equations, it is statistically possible to predict the variation of fluid s density and viscosity over the wide range of pressure and temperature. Furthermore, it is also found that the predicted density and viscosity values are very close to the experimental data with very low deviation. This confirmed the reliability and accuracy of the prediction. This paper provides a novel data prediction of rheology and density of Saraline-based SLWCF at reservoir conditions for the purpose of underbalanced perforation. This result is essential as a tool for field engineers to roughly estimate the density and viscoplasticity of completion fluids as they subjected to reservoir conditions. 1. Introduction After drilling and cementing, the final stage of well completion is to perforate the wellbore that allow the hydrocarbon to be pumped out for production. Perforation in upstream oil and gas industry is

2 2 SPE MS referred to as a process of punching hole in the cemented casing of an oil well in order to establish a connection between wellbores and reservoirs (Gatlin, 1960; King, et al., 1986; Mhaskar et al., 2012). In general, the process in perforating oil well involves deploying down the perforation gun to the desired depth, filling up the casing with the suitable fluid at the appropriate pressure, and then firing the shapedcharge perforation bullets that have high energy to penetrate into the casing, cement and formation rock (Bellarby, 2009). The main objective of perforation is to create optimal production path to the formation. Perforation is considered as the vital element in determining the productivity of a well (Mustafa et al., 2009). Hence, creating a clean and undamaged perforation tunnels is essential to ensure the high production rate. However, due to its high speed and high impact energy upon detonation, the perforation shaped-charges shatter formation rock, jet metal and cement, then plug rock pores and create a compacted low permeability region along the perforation tunnel (Bell, 1982; King et al., 1986; Halleck, 1997). This rock property damage is referred to as perforation-induced formation damage (Walton, 2000). In this case, perforating does not achieve expected clean and undamaged perforation tunnel that are responsible for the reduction of reservoir permeability and oil production (Halleck, 1997). Some techniques have been applied to mitigate this perforation induced formation damage, such as acid stimulation, skin fracturing, extreme overbalance and propellant assisted perforating (Mustafa et. al, 2009; Cuthill, 2001). However, such techniques are still hindered by high cost and also the additional works and surface facilities that have to be taken into account. For instance, in acidizing job, the compatibility of acid with rock mineral, reservoir fluid, mixing water composition have to be considered (Ruksanor, 2012). One of the best alternatives that have been accepted as standard practice in the field to control perforation-induced formation damage is through underbalanced perforation. It refers to perforation job conducted in condition where the wellbore pressure is maintained lower than reservoir pressure (King et al., 1986; Behrmann, 1996; Badrul et al., 2009). During and after detonation of perforation gun, the surge flow of reservoir fluids created due to dynamic forces, differential pressure and drag erodes out shattered rock grains and perforation debris out of perforation tunnel (Bolchover and Walton, 2006). In terms of efficiency, handling, and economics, this method can be considered as the highest potential method to optimize well performance. Underbalanced perforation is also considered as preventive measures since it mitigates the formation damage even prior to detonation. Controlling the wellbore pressure to provide underbalanced condition always come with some difficulties. Millhone (1983) observed that low- to very low-density fluid is required in some of practical consideration to achieve underbalanced condition. Fluid systems with very low density reduce the volume of fluid invasion into a producing reservoir, thus minimizing damage on perforation tunnels (Alsobrook and Jones, 2007). Underbalanced condition prior and during gun detonation may be achieved by employing traditional water-based fluid, e.g. clear brines, polymer fluids, biodegradable polymers, or water-soluble, and gas-based fluids, e.g. air, gas, mist, or natural gas. Unfortunately, the application of the afore-mentioned completion fluids has some drawbacks since they usually require additional works, times, special equipment and cost. In addition, wellbore may also be eroded if formation is loosely consolidated and there is also possibility of downhole fire if hydrocarbons are encountered. Therefore, extra safety precautions are necessary when dealing with high pressure gas or air as completion fluids because of its potential hazard. Badrul et al. (2009) has introduced an attractive and interesting low density completion fluid referred to as Super Lightweight Completion Fluid (SLWCF). Glass bubbles were added into the existing oil-based completion fluid to achieve underbalanced condition by manipulating the fluid density. This provides broader application of conventional completion fluid especially in depleted reservoirs where pressure is typically low. With density value of approximately 0.60 g/cm 3 (5.0 lbm/gal), SLWCF has been successfully formulated and tested at Bunga Raya field (a join field between Malaysia and Vietnam). SLWCF has been able to achieve underbalance pressure of about 0.84 MPa (122 psi) at the time of perforating. From the production result, a well perforated using SLWCF

3 SPE MS 3 produced an additional 1000 barrels of oil perday, i.e. almost four times higher production rate than a similar well perforated using conventional completion fluid. With such promising potential application, this article presents further studies on Saraline-based SLWCF physical properties such as density and viscosity at reservoir conditions, which may change the fluid behavior. Previous work reported that Saraline-based SLWCF flow behavior can sufficiently be described using two types of models, namely Sisko and Mizrahi-Berk models (Amir et al., 2015). However, these descriptions are only limited at considerably low temperature and ambient pressure. It is important in upstream oil and gas industry to understand the fluid density and viscosity during perforation process. Such information on fluid behavior not only ensures that the fluid meets operational requirement, but also determine the correct handling and operational practice such as the required horsepower for the pump. Hence, the importance of a comprehensive study on the density and viscosity of Saraline-based SLWCF at at reservoir conditions, i.e. elevated pressure and temperature is highly required. 2. Materials and methodology 2.1 Materials and formulation In this study, Shell Saraline 185 V synthetic oil was used as the continuous phase to formulate Saraline-based SLWCF. Saraline oil is derived from natural gas, thus it does not contain any aromatic hydrocarbons, sulfur compounds, or amines. The density of Saraline oil is g/cm 3 [6.49 lbm/gal] (Shell MDS, 2014). Meanwhile, to reduce the density of Saraline oil in formulation, 3M glass bubbles (HGS4000) were used as a density-reducing agent. Finally, to improve fluid stability, bentonite clay and hydrocarbon based emulsifier were used as homogenizing and stabilizing agent, respectively. In this study, Saraline-based SLWCF was prepared based on our previous work s formulation (Muhammad et al., 2011). The fluid was prepared by mixing 60 %wt of Saraline and 40 %wt of glass bubbles. To improve the stability of the fluid, 3 %wt of clay and 9 %wt of emulsifier were added. The mixture was then agitated by using IKA T25 digital ultra-turrax disperser at 6000 rpm for an hour. The readily prepared fluids were then placed in a sealed-cap container for further tests. 2.2 Density measurement and analysis The Saraline-based SLWCF density was measured using a Anton Paar DMA model highpressure vibrating tube density meter equipped with DMA 4500 as the evaluation unit. A high-pressure volumetric hand-pump (model HPT1, rating: 10,000 psi) equipped with a GE Druck DPI 104 manometer was used to pressurize and introduce the sample into the density meter. A peristaltic pump Masterflex Model was used to inject the sample into the system. Finally, a digital pressure transducer (Swagelok S Model transducer, rating: 0-8,000 psi) was used to monitor the pressure during the measurement. The uncertainty of the pressure was estimated at about 0.5% of the real pressure. The experiment was conducted at various temperatures and pressures ranging from to K and 0.1 to 25 MPa, respectively. In this study, the experimental densities of Saraline-based SLWCF were fitted to the Tait-like equation (Eq. 1). Data analysis and model fitting analysis were carried out using commercial statistical software, the SAS Enterprise Guide Software version 5.1. The calculation of the Tait-like equation

4 4 SPE MS parameters and the absolute average deviation (AAD), the maximum deviation (DMAX), the average deviation (bias), and the standard deviation values were also calculated. where ( T ) ( ) + P ( ) ρ0 ρ ( T, P) = (1) B T 1 C ln B T ρ 0 ( T ) is the temperature dependence of the density at 0.1 MPa expressed as 2 3 ( T ) = A + AT + A T + A ρ (2) T Parameter function C is assumed to be temperature independent, and is given by the following polynomial 2 ( T) B + B T B B = + (3) 0 1 2T In addition, regression analysis was also performed to verify the accuracy of the Tait-like equation results. Here, a mathematical model (Eq. 4) proposed by Chhetri & Watts (2012) was used on the regression analysis. ( ) = c a T + b P Density ρ (4) where c is a constant, a is the temperature coefficient, T is the temperature in K, b is the pressure coefficient and P is the pressure in MPa. Using this regression model, the experimental density data of Saraline-based SLWCF, over the temperature and pressure range of interest, were fitted to the model using a commercial statistical toolbox on Matlab Version R2015A. A linear relationship was plotted to calculate the coefficients and statistical parameters. Then, the deviations of each value from the regressed model were compared to Tait-like equation. Then the accuracy of the predicted values using Tait-like equation was determined. 2.3 Viscosity measurement and analysis Saraline-based SLWCF viscosity at High Pressure High Temperature (HPHT) conditions was measured using a high pressure and high-temperature NI Rheometer FANN 75. After the equipment has been set up, approximately 100 ml of the sample were injected through the sample port to bring the level of the sample up in the coupling. The experiment was carried out at temperature and pressure ranging from K to K and 0.1 to 4.48 MPa (14.5 to 650 psi) respectively. Test sample pressures and temperatures were varied by fluid pressurization and electric heater, respectively. Measurements were carried out by measuring the angles at two different speeds, specifically 600 rpm and 300 rpm for each pressure and temperature. Measurements were conducted at least 3 times for each speed, before an average angle was obtained. To obtain the viscosity, the reading angle at 600 rpm was subtracted from the reading angle at 300 rpm and the result was divided to 1000 for unit conversion from cp to Pa s.

5 SPE MS 5 The measured viscosity data of Saraline-based SLWCF were fitted to four different viscositytemperature-pressure models. The four models are; the Mehrotra and Svrcek s equation, modified Mehrotra and Svrcek s equation, Ghaderi s equation and Gold et al. s modulus equation and they are listed in Table 1. Table 1 Viscosity-temperature-pressure models and equations Models Equations ln µ = exp 1 + A2 lnt + A Modified Mehrotra and 2 ln µ = A + BlnT + A 3 1 Mehrotra and Svrcek ( ) ( ) P Svrcek ( ) ( ) CP B2P B3 B4 3 Ghaderi µ = exp B ln P T T P 4 Gold et al. s Modulus ( ) µ = µ 0 exp a1 + a2t + b1 + b2t P µ is 0.14 Pa s. Data analysis and model fitting analysis were carried out using commercial statistical software, Matlab Version R2015a. Based on the data fitting, each of the calculated equation s parameters is generated and its ability to describe the relationship of viscosity function of pressure and temperature was statistically evaluated with Matlab. The statistical parameters such as Sum of Square Error (SSE), and Root Mean Square Error (RMSE), R-squared and adjusted R-squared are calculated for model optimization. To validate the prediction of SLWCF viscosity, the fitting results were evaluated by measuring the Average Absolute Percentage Deviations (AAPD), standard error and deviation between the experimental values measured in the laboratory and the predicted values calculated using the equations. The formulas for these parameters are expressed as follows: 0 µ exp 100 AAPD( %) = 1 (5) n µ calc Standard Error (%) = 100 ( µ exp µ calc ) n range 2 (6) where µ exp is the experimental viscosity value gathered by the author and µ calc is the calculated value from the derived equation, n is the number of data points, and the range is the maximum value of minus the minimum value. The deviations between the experimental and predicted values evaluated based on equation 7. µ exp

6 6 SPE MS D ρ exp erimental (7) ρ = predicted 3. Results and discussion 3.1 Densities of Saraline-based SLWCF The measurement of density values of Saraline-based SLWCF was performed at elevated temperature and pressures ranging from K to K and 0.1 MPa to 25 MPa, respectively, to simulate the reservoir condition during perforation activity. The result of the variation Saraline-based SLWCF densities is shown in Table 2 and Figure 1. Table 2 Experimental density (g/cm 3 ) values for Saraline-based SLWCF as a function of temperature and pressure. Pressure (MPa) Temperature (K)

7 SPE MS 7 Figure 1 3D contour plot of Saraline-based SLWCF densities as a function of temperature and pressure. From the result, it is clear that the density is inversely proportional to temperature and directly proportional to pressure, in a linear fashion. This is due to the fluid volume increment with temperature through the weakened intermolecular force in the fluid. Thus, thermal energy eventually decreases the density of the fluid under isobaric conditions when mass is kept constant. Moreover, Saraline-based SLWCF seems to be more compressible at higher temperature as it exhibits a small increment of density at room temperature when the pressure is increased. Meanwhile, density change as a function of pressure at various temperatures shows the density is increasing with pressure under isothermal condition. The increment of pressure under isothermal conditions seems to decrease the volume of the fluid due to the compression of intermolecular space in the fluid when mass is kept constant. As the result, the density is increased. However, the effect of pressure on the fluid density seems negligable because of the high content of glass bubbles which may cause the fluid to be less compressible and more resistance to pressure. 3.2 Density Model Fitting In the present work, two fitting methods were used to correlate the density of Saraline-based SLWCF as a function of temperature and pressure. First, the measured density values were fitted to Taitlike equations (Eq. 1). Tait-like equation has commonly and successfully been used to correlate the relationship of density with a wide range of pressures and temperatures for simple and complex fluids (Dymond and Malhotra, 1988; Fandiño et al., 2005; Milhet et al., 2005; Miyake et al., 2008). The main advantage of this equation is its capability to simultaneously evaluate several fluids thermodynamic properties, such as isobaric thermal expansivity (αp) and isothermal compressibility (кt) (Milhet et al, 2005). According to Reynolds and Couchman (1977), this equation also adequately represents the

8 8 SPE MS equation of state for solid containing material over a wide range of pressures, typically less than 100 kbar (145,000 psi). In this study, experimental data obtained from density measurements (Table 2) were fitted to the Tait-like equation and its parameters were generated. To determine the accuracy of the fitting process, several statistical parameters such as absolute average deviation (AAD), the maximum deviation (DMAX), the average deviation (bias), and the standard deviation σ, were calculated using the following equations: 100 AAD = N N i= 1 ρ exp calc i ρi exp ρi (8) DMAX ρi = max 100 exp calc ρi exp ρi (9) 100 bias = N N i= 1 ρ exp calc i ρi exp ρi (10) N exp calc ( ρi ρi ) 2 i=1 σ = (11) N m where, N is the number of experimental data points and m is the number of calculated parameters. The eight calculated Tait-like equation parameters and statistical fitting parameters, i.e. AAD, DMAX, bias, and standard deviation (σ) are given in Table 3. From the statistical calculation and the value of statistical parameters, the Tait-like equation provides an excellent ability to describe the correlation between fluid s density as a function of temperature and pressure. Results show that the standard deviation (σ) and absolute average deviation (AAD) are very small (0.002 g/cm 3 and %), suggesting that the predicted value is very close to the real value. This is also supported by the low value of the measurement uncertainty (bias: %). Therefore, the Tait-like equation could be used adequately in predicting the effects of temperature and pressure on Saraline-based SLWCF density.

9 SPE MS 9 Table 3 Tait-like equation calculated parameters and deviations of density correlation for Saraline-based SLWCF. Parameters Calculated value A 0, g cm A 1, g cm 3 K A 2, g cm 3 K A 3, g cm 3 K C B 0, MPa B 1, MPa K B 2, MPa K AAD, % DMAX, % Bias, % Standard Deviation (σ), g cm Figure 2 presents a comparison of the Saraline-based SLWCF experimental density values and the calculated density values using the Tait-like equation at K. From the results, it is found that all of the points are very near to the straight line. This shows that the Tait-like equation is appropriate for the prediction of the density of Saraline-based SLWCF. Furthermore, the deviations between the experimental and predicted values were also considered. The deviation between the measured fluid densities and calculated from the Tait-like equation is presented in Figure 3. The data suggests that the Tait-like equation is able to give a reliable performance. It seems to perform satisfactorily to predict the experimental data. This is because of the low deviation range, which is in the range of ± 0.6%. The value was confirmed by the maximum deviation (DMAX) in Table 3, which is %. Figure 2 Comparison of the Saraline-based SLWCF experimental density values and calculated density values using Tait-like equation at K.

10 10 SPE MS Figure 3 Deviation between the measured and calculated Saraline based SLWCF densities. To verify the reliability and accuracy of the density prediction using the Tait-like equation, a generalized regression equation from Chhetri & Watts (2012) was used. The generalized regression model is presented as Eq. 4. To assess the suitability of the generalized regression model, the experimental data fitting outcome using this model was analyzed. The results of the regression constant, together with the corresponding calculated model and statistical parameters, such as R 2, the sum of square error (SSE), and the root mean square error (RMSE) are summarized in Table 4. Based on the results, the regressed plot gives a very high R 2 and adjusted R 2 value ( and ) and very low SSE and RMSE values ( and ). High R 2 and adjusted R 2 values, and low SSE and RMSE values indicate accurate prediction. Because of the good fitting performance, this regression model is applicable as comparative model for Tait-like equation. Table 4 Regression constant and statistical parameter values for measured densities of Saraline-based SLWCF. Parameters Calculated value a b c R Adjusted R RMSE SSE Figure 4 presents the comparison plot of the prediction results using the Tait-like equation with the regressed model. It can be seen that all of the points from both equations are lying almost at the same spot and close to the straight line. Based on the results, the accuracy between predicted value using the Tait-like equation and regressed model value is almost 99%. This indicates that the regressed model is in

11 SPE MS 11 good agreement and comparable with the results produced by the Tait-like equation. Hence, the reliability of the Tait-like equation model to predict the density of Saraline-based SLWCF over a wide range temperatures and pressures can be verified. Figure 4 Comparison of the predicted results using the Tait-like equation with the regressed model. 3.3 Viscosities of Saraline-based SLWCF Viscosity measurements of Saraline-based SLWCF viscosities were conducted at temperatures and pressures between K and K and 0.1 MPa and 4.48 MPa, respectively. Table 5 shows the measured experimental viscosity data and Figure 5 illustrates 3D plot shows the relationship of Saraline-based SLWCF viscosities as a function of temperatures and pressures. Table 5 Experimental values of viscosity (Pa s) for Saraline-based SLWCF as a function of temperatures and pressure. Pressure (MPa) Temperature (K)

12 12 SPE MS Figure 5 3D plot of Saraline-based SLWCF viscosities as a function of temperature and pressure. Based on the result, it can be seen that the reduction of viscosity as a function of temperature could be divided into two regions. First, significant viscosity reduction at low temperature ( K to K) and second, fairly slower viscosity reduction at high temperature (above K). 3D plot in Figure 5 clearly shows the slope of the first region is greater than the second region. For example, the viscosity value at 0.1 MPa decreased almost double from about Pa s at K to 0.66 Pa s at K. Similarly, the viscosity at 4.48 MPa decreased from Pa s at K to 0.06 Pa s at K. This phenomenon is mainly due to the effect of temperature on the intermolecular interaction. It is known that thermal energy increases the molecular distance and weakens the intermolecular forces (Hassan and Hobani, 1998). In other words, thermal energy inclines to rearrange the particles in parallel directions and breaks into smaller particles when temperature is increased. The particle can move easily as reduction of intermolecular forces and particles interaction. In contrast, the viscosity change is negligable with further temperature increament. This is because of flocculation phenomenon, which is a process where small dispersing particles such as clays, glass bubbles or polymers tends to agglomerate and precipitate to form a fragile structure called floc (Marsh, 1931; Craft and Exner, 1933; Annis, 1997; Rastegari and Svrcek, 2004). It is a result of the attractions between negative and positive charges of solid particles during mechanical agitation. The flocculation tends to occur at high temperature. In addition the high concentration of solid in slurries i.e. glass bubbles accelerates the flocculation phenomenon in Saraline-based SLWCF to the higher degree. Therefore, flocculation effect compensates the viscosity reduction at higher temperature. Furthermore, it can also be seen that the viscosity of SLWCF is fairly affected by the changes of pressure. Although the trend in Figure 5 shows that viscosity is negatively correlated with pressure, the changes seem to be insignificant at all tested pressure ranges. This might be due to the high concentration of glass bubbles increases the fluid resistance to pressure and causes the fluid to become

13 SPE MS 13 less compressible. Even at high pressure, i.e MPa (650 psi), it is difficult to compress the fluid and reduce its intermolecular distance. Hence, only minor and trivial variation of viscosity changes is observed in Saraline-based SLWCF with pressure. 3.4 Viscosity Model fitting In order to model the Saraline-based SLWCF viscosity at various temperatures and pressures, the data were fitted to four viscosity-temperature-pressure models as mentioned previously. The results of the data fitting along with the calculated model and statistical parameters, such as Sum of Square Error (SSE), and Root Mean Square Error (RMSE), R-squared and adjusted R-squared are summarized in Table 5. Table 5 Viscosity-temperature-pressure models of Saraline-based SLWCF with their calculated model's coefficients and statistical parameters. Viscosity models Parameters R-squared Mehrotra and Svrcek Gold et al. s modulus Modified Mehrotra and Svrcek Ghaderi A 1 = A = A = a 1 = a 2 = b 1 = b 2 = A = B = C = B = B = B = B = Adjusted R-squared SSE RMSE The Mehrotra and Svreck s equation was originally developed for an investigation of the effects of pressure and temperature on the viscosity of compressed Cold Lake bitumen. Their correlation scheme is able to satisfactorily correlate the viscosity of specific non-newtonian fluid at different pressure and temperature. Basically, this correlation seems compatible with Saraline-based SLWCF since this fluid has pseudo-plastic behavior, which is represented by Sisko and Mizrahi-Berk models (Amir et al., 2015). However, based on the results, the original Mehrotra and Svrcek along with Gold et al. s modulus equation gave a poor prediction for Saraline-based fluids. These two models gave negative R- squared and adjusted R-squared (-8.25, , and , respectively). Moreover, the fitting of these two models also seems to give greater observed errors (SSE and RMSE). These show that the

14 14 SPE MS original Mehrotra and Svrcek and Gold et al. s modulus equation may not be suitable to represent and predict the viscosity-temperature-pressure relationship for Saraline-based SLWCF. This phenomenon might be due to the nature of SLWCF viscosity. Mehrotra and Svrcek s equation is based on its ability to accommodate a dramatic reduction in viscosity with temperature using a natural logarithmic factor. In a study of new correlations to predict the viscosity of Canadian bitumen, Puttagunta et al. (1993) observed that when temperature is increased from K to K, the viscosity of Canadian bitumen decreased as much as 99.15%. The exponential factor in the equation indicates a big and rapid change of fluid viscosity. However, with regards to SLWCF, the reduction of the fluid viscosity was only around 45%, which is not as high as the Canadian Bitumen. In addition, Gold et al. s modulus equation was originally used to model synthetic and mineral lubricants. The model is accommodating lubricant characteristics such as compressibility and rapid viscosity change under high pressure, which are not observed in SLWCF. The Mehrotra and Svrcek s equation has been modified by eliminating the exponential factor of the temperature to accommodate for the smaller viscosity change. The calculated modified Mehrotra and Svrcek s equation parameters and statistical parameters, such as R-squared, SSE and RMSE are presented in Table 5. Viscosity prediction using modified Mehrotra and Svrcek s equation gives a significantly high R 2 and adjusted R 2 values, and , respectively, which indicates that the fitting is satisfactory. In addition, the calculated SSE and RMSE are very low ( and , respectively) representing high quality fitting and the equation is deemed reliable. Moreover, good prediction can also be obtained using Ghaderi s model. Based on calculation, the model gave a considerably high value of R 2 and adjusted R 2 ( and , respectively) and low value of SSE and RMSE. It can be understood that Ghaderi s model was previously used as predictive tools to estimate the viscosity of diesel oil based muds for drilling application (Ghaderi, 2012). Therefore, since SLWCF has a similar viscoplastic characteristic with a typical oil-based mud, Ghaderi s model can be used to represent the viscosity behavior of Saraline-based SLWCF. Average Absolute Percentage Deviations (AAPD) and Standard Error for both models are presented in Table 6. Based on the calculation, the two statistical error parameters for both Modified Mehrotra and Svrcek s and Ghaderi s equation were very low. This shows that the proposed models could predict about 95% of viscosity data with AAPD less than 6%. Furthermore, Figure 6 exhibits the comparison between the experimental viscosity data of the Saraline-based SLWCF and calculated values obtained from the equations. All data seem to fall within the straight line, indicating an approximately 95% fit certainty. Table 6 Statistical parameters of viscosity-temperature-pressure models and equations. Models AAPD (%) Standard Error (%) Modified Mehrotra and Svrcek Ghaderi

15 SPE MS 15 Figure 6 Comparison of the Saraline-based SLWCF experimental and calculated viscosity values using modified Mehrotra and Svrcek s and Ghadri s equations. Figure 7 and Figure 8 show the deviation between the experimental and calculated viscosity values. From the result, it is clear that the data fitting using both equations is considered good and acceptable since almost all prediction points fall at low deviation range, which mostly is in the range of ±10%. Figure 7 Deviation between the measured and calculated Saraline-based SLWCF viscosities using modified Mehrotra and Svrcek s equations.

16 16 SPE MS Figure 8 Deviation between the measured and calculated Saraline-based SLWCF viscosities using Ghaderi s equation. 4. Conclusions The density and viscosity of Saraline-based SLWCF have been measured and evaluated at a wide range of elevated pressure and temperature. Based on the results, the following conclusions are reached; 1. The density of Saraline-based SLWCF is inversely correlated with temperature at isobaric condition but positively correlated with pressure at isothermal condition, both in the linear fashion. 2. Statistical evaluations suggest that Tait-like equation was able to accurately predict the density values of Saraline-based SLWCF at wide range of pressure and temperature. 3. The goodness of Tait-like equation is comparable with the goodness of regressed model. The comparative accuracy value between two equations was found to be up to 99%. 4. It was found that the viscosity of Saraline-based SLWCF is more sensitive to temperature than pressure. 5. From the modeling evaluation, the modified Mehrotra and Svrcek s and Ghaderi s equations were able to describe viscosity behavior of Saraline-based SLWCF over a wide range temperature and pressure. 6. The predicted and calculated viscosity values using both equations showed a low deviation and good agreement with the experimental data. 7. In term of field applications, the study in this paper is beneficial and useful for operators and contractors to determine operational parameters such as the required pump horsepower to ensure the completion fluid could be pumped safely as planned. 5. Acknowledgements The authors would like thank to the Shell MDS (Malaysia) Sdn. Bhd., 3M Asia Pacific, Pte. Ltd., Scomi Oil and Gas (Malaysia) Sdn. Bhd. for their contribution in providing the materials for this work. The authors also appreciate the financial support from University of Malaya Research Grant (UMRG) RP F, UM Post Graduate Grant PG A, University of Malaya Research Grant (UMRG) RP031B-15AFR, and High Impact Research (HIR) Grant HIR-D and HIR-D

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