OTC-26376-MS

Novel Lightweight Biopolymer Drilling Fluid for Underbalanced Drilling Lim Symm Nee, University of Malaya; Munawar Khalil, University of Malaya; Badrul Mohamed Jan; University of Malaya; Brahim Si Ali; University of Malaya

Copyright 2016, Offshore Technology Conference This paper was prepared for presentation at the Offshore Technology Conference Asia held in Kuala Lumpur, Malaysia, 22–25 March 2016. This paper was selected for presentation by an OTC 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 Offshore Technology Conference and are subject to correction by the author(s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference 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 OTC copyright.

Abstract This research centres on optimizing the formulation of a water-based lightweight biopolymer drilling fluid using Design Expert for underbalanced drilling (UBD). Response Surface Methodology (RSM) was selected as a viable means to obtain the optimized drilling fluid formulation. Concentrations of four main raw materials (glass bubbles, clay, xanthan gum and starch) were varied in a suitable range to obtain the formulation with the desirable density, plastic viscosity (PV) and yield point (YP) for UBD application. Based on the results, the optimized drilling fluid can be formulated with 24.46% w/v of glass bubbles, 0.63% w/v of clay, 0.21% w/v of xanthan gum and 2.41% w/v of starch. The desirability factor, d for this optimum condition selected is 0.628. The mathematical models generated by RSM were able to predict the three response parameters well, as the experimental values were found to be in good agreement with the predicted values. The error is less than 1.0, standard deviation less than 0.5 and the accuracy is more than 98.5%. All mathematical models were quadratic in nature. The model used to predict the PV has an inverse square root transform. This drilling fluid is considered to be less harmful to the environment as it is water-based and, at the same time, composed of natural polymers (xanthan gum and starch) which are biodegradable. With this novel formulation, we could expect to drill more wells in underbalanced conditions to improve production of oil.

Introduction The existence of numerous types and classes of drilling fluids was sparked by the introduction of different drilling methods in the exploration for oil and gas. These drilling methods are grouped into three categories, which are: overbalanced (OBD), underbalanced (UBD) and managed pressure drilling (MPD) (Malloy and Shayegi, 2010; Ostroot et al., 2007). The decision to pursue either of these methods depends on choosing the most suitable technology for a particular wellbore based on certain criteria (Malloy and Shayegi, 2010). The choice of applying UBD or MPD was made due to the inability to use conventional OBD for a particular well. UBD or MPD techniques could overcome drilling problems such as to minimize or eliminate formation damage, small formation pressure or fracture gradient window, minimize well cost, increase rate of penetration (ROP), extend bit life, increase safety in drilling operations, etc. (Ostroot et al., 2007). Among all three methods, UBD has an edge over others in terms of the ability to eliminate formation damage in a wellbore. For MPD, the residual formation damage can be as high as that of conventionally drilled OBD well. Furthermore, UBD and MPD

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.

2 OTC-OTC-26376-MS

techniques have the potential to reduce drilling fluid costs significantly by using more economical and light fluid systems with the elimination or high reduction of mud losses (Ostroot et al., 2007). Since the introduction of horizontal drilling, UBD has been widely used in a global scale (Medley et al., 1995). This drilling technique has generated profitable returns in Canada, USA, Europe and also China particularly in Tarim, Dagang and Sichuan oilfields (Luo et al., 2000). The IADC Underbalanced Operations and Managed Pressure Drilling Committee defined UBD as a drilling activity employing appropriate equipment and controls where the pressure exerted in the wellbore is intentionally less than the pore pressure in any part of the exposed formations with the intention of bringing formation fluids to the surface (Malloy and Shayegi, 2010). It is well-known that UBD techniques possessed numerous advantages such as the ability to prevent formation damage, higher ROP, reduce perforation damage due to drilling, prolonged bit life, a rapid indication of productive reservoir zone, potential for dynamic flow testing while drilling, etc. (Khalil and Badrul, 2012b). There are many types of drilling fluids utilized in UBD ranging from purely gas (compressible) to purely liquid (incompressible) (Medley et al., 1995). Lightweight compressible fluids such as air or natural gas or reduced oxygen content air are usually used in UBD depending on the reservoir conditions encountered (Khalil and Badrul, 2012b). However, drilling using these fluids can be very challenging and posed many problems (Khalil and Badrul, 2012b; Medley et al., 1995). Common issues encountered includes increased costs when using compressors and nitrogen, drill string corrosion and vibration, cause downhole fires and explosions, complex hydraulic calculations, increase friction factor, difficulty to lift drill cuttings, etc. (Lim et al., 2015; Medley et al., 1995). Due to these difficulties, incompressible drilling fluids are often times preferred. Incompressible drilling fluids incorporating glass bubbles as a density reducing agent could minimized the adverse effects of compressible drilling fluids in UBD (Khalil and Badrul, 2012b). This is an alternative way to produce low density fluids that carries the same benefits as air based drilling fluids. The base fluid could be made up of fresh water, brine, diesel, etc. (Arco et al., 2000). Oil, crude or synthetic oil base drilling fluids are considered harmful to the environment (Khalil and Badrul, 2012a; Khalil and Badrul, 2012b). Therefore, water based drilling fluids are usually used when drilling. In this research, glass bubbles were added into a water based biopolymer drilling fluid to make it lighter. Our work is a continuation from previous works by Khalil and Badrul (2012a & 2012b). The biopolymers used to formulate this drilling fluid are xanthan gum and starch. Xanthan gum solutions may thicken exhibiting a pseudoplastic nature which is very stable in a wide range of pH, pK (ionic concentration) and temperature (Casas et al., 2000). Another natural polymer, starch, is a polymer composed of glucose molecules, amylose and amylopectin linked together (Tester et al., 2004). In drilling fluids, these biopolymers function as rheological control agents in aqueous systems and also as fluid loss control agents (Khalil and Badrul, 2012a; Lim et al., 2015). Other than these ingredients, clay also plays an important role in this drilling fluid. Bentonite or sodium montmorillonite is used in a large scale globally as a drilling fluid additive (Bol, 1986). It functions as a viscosifier and at the same time reduces fluid loss into the formation by forming a layer of filter cake on the walls of the wellbore (Bol, 1986).

Materials The main ingredient comprises of glass bubbles or hollow-glass spheres (HGS) with a rating of 4000 psi. HGS acts as a density reducing agent decreasing the density of the drilling fluid making it lightweight. Environmentally friendly biopolymers, xanthan gum and soluble starch were also added

OTC-26376-MS 3

into the fluid formulation. Clay (Wyoming, USA) was included into the mixture to improve fluid rheological properties and prevent fluid loss. Sodium chloride, NaCl (R&M Chemicals) was used as an additive in the drilling fluid. A bactericide was added to protect the biopolymers from parasitic attacks.

Methodology

Lightweight Biopolymer Drilling Fluid Formulation

The lightweight biopolymer drilling fluid was formulated by mixing all the ingredients together: glass bubbles, biopolymers (xanthan gum and starch), clay, sodium chloride and bactericide in distilled water. Mixing was done using the IKA RW20 digital mixer at a speed of 500 rpm. The amount of sodium chloride and bactericide were fixed constant when running tests for optimizing the drilling fluid formulation.

Density and Rheological Measurements

Density measurements were made using a 25ml pycnometer at ambient temperature and pressure. Plastic viscosity (PV) and yield point (YP) measurements were obtained from calculations based on the shear stress and applied shear rates values from a rotational viscometer (Haake Viscotester Model VT550) with repeatability and accuracy: ± 1% , comparability: ± 2%. This viscometer is equipped with MV2P spindle. The applied shear rates were set from a range of 2.639 to 528 s-1 for all the fluid samples except for samples with 10% w/v of clay and 1% w/v of xanthan gum. The applied shear rate could only go up to 264 s-1. The Bingham-plastic Model was used for the PV and YP calculations.

Experimental Design and Data Analysis Statistical design of experiments and data analysis were performed using the Design Expert software (Version 8.0, Stat-Ease Inc., Minneapolis, USA). From Design Expert, the Central Composite Design (CCD) was employed to generate matahematical models that were used to describe the relationship between the variables (glass bubbles, clay, xanthan gum and starch concentrations) and responses (density, PV and YP). The range and centre point values of the four independent variables are shown in Table 1.

Table 1. Summary of experimental domain of central composite design (CCD)

Variables Factor Levels

Code Name -2 -1 0 1 2 A Glass bubbles concentration (% w/v) 10 15 20 25 30 B Clay concentration (% w/v) 0.5 1.75 3.0 4.25 5.5 C Xanthan gum concentration (% w/v) 0.1 0.2 0.3 0.4 0.5 D Starch concentration (% w/v) 0.5 1 1.5 2 2.5

The experimental design comprises of eight factorial points, eight axial points at a distance of ± 2 from the centre and five replicates of the centre point. In the statistical diagnostic checking test, the Analysis of Variance (ANOVA) was utilized to evaluate the adequacy of the models generated. The R2 value was determined to check the quality of the generated polynomial models. Its statistical significance was obtained by the Fisher’s F-test. The probability of error (or P-value) with 95% confidence level was also used to evaluate the model terms. The optimum condition was determined using three dimensional plots and their respective contour plots.

4 OTC-OTC-26376-MS

Experimental Validation Laboratory based test experiments were carried out in actual conditions based on the generated optimal setting from RSM to validate the mathematical models generated by the software. The error and accuracy between the predicted and experimental values were obtained to determine the closeness of these values.

Results and Discussions

Statistical (ANOVA) and Process Analysis

The CCD design from RSM predicted and generated mathematical models for each of the responses (density, PV and YP). The results to determine the relationship between the independent variables and responses are portrayed in Table 2.

Table 2. CCD results of the independent variables and responses parameters

Run Factors Responses A

(% w/v) B

(% w/v) C

(% w/v) D

(% w/v) Density (lb/gal)

PV (cP)

YP (Pa)

1 15.00 1.75 0.40 1.00 7.0534 58.8 9.8265 2 20.00 3.00 0.30 1.50 6.7876 70.7 10.2148 3 20.00 3.00 0.30 1.50 6.7839 70.8 10.3247 4 20.00 3.00 0.10 1.50 6.7798 62.2 2.8124 5 25.00 1.75 0.20 2.00 6.4618 75.8 6.3041 6 20.00 3.00 0.30 1.50 6.7876 70.7 10.5728 7 15.00 4.25 0.40 2.00 7.2075 65.4 12.4862 8 25.00 4.25 0.40 1.00 6.5587 95.9 24.1190 9 25.00 1.75 0.40 2.00 6.4743 110.6 18.3668 10 20.00 3.00 0.50 1.50 6.7965 95.1 22.2386 11 20.00 3.00 0.30 1.50 6.7869 70.5 10.0034 12 25.00 4.25 0.20 1.00 6.5338 80.9 10.4125 13 20.00 3.00 0.30 2.50 6.8088 81.1 10.4246 14 20.00 0.50 0.30 1.50 6.7123 68.4 7.1171 15 30.00 3.00 0.30 1.50 6.2658 120.0 18.3536 16 20.00 3.00 0.30 1.50 6.7866 70.7 10.3036 17 20.00 5.50 0.30 1.50 6.8746 73.1 14.2923 18 15.00 4.25 0.20 2.00 7.1816 53.7 5.1994 19 10.00 3.00 0.30 1.50 7.5402 38.5 5.3068 20 20.00 3.00 0.30 0.50 6.7787 69.8 9.5908 21 15.00 1.75 0.20 1.00 7.0726 53.1 2.7580

OTC-26376-MS 5

Density

Using ANOVA, the experimental results were fitted into the equation for the first response, density. The best equation that fits the results was quadratic. The “goodness of fit” of the model obtained was analyzed by ANOVA. The best model fitting for density (Y1) in terms of coded factors are represented by Eq. (1):

Y1 = 6.79 – 0.32A + 0.041B + 4.844 x 10-3C + 7.525 x 10-3D – 5.813 x 10-3AB + 3.837 x 10-3AC – 0.012AD + 7.187 x 10-3BC – 7.787 x 10-3BD + 4.088 x 10-3CD + 0.029A2 + 1.529 x 10-3B2 + 2.044 x 10-4C2 + 1.604 x 10-3D2 (1) where A represents glass bubbles concentration; B represents clay concentration; C represents xanthan gum concentration and D represents starch concentration. The ANOVA results for the response surface quadratic model are presented in Table 3. Based on Table 3, the Model F-value implied that the model is significant and has only a 0.01% chance to occur due to noise. The data presented in the table depicts that the mathematical model is significant at the 5% confidence level since the P-value is less than 0.05. A high R2 value that is closed to 1 is desirable and a reasonable agreement with the adjusted R2 value is necessary (Noordin et al., 2004). From Table 3, the high calculated R2 value of 1.0000 defined that the quadratic model is able to fit perfectly the variability of the responses obtained from the experimental data. The adjusted R2 value is also very close to the R2 value. The Lack of Fit (F-value) indicates that it is not significant relative to the pure error. This means the model fits which is desired. A Lack of Fit value this large requires 11.93% chance to occur due to noise.

Table 3. Analysis of variance (ANOVA) results for first response (density)

Response F-value P R2 Adj. R2 AP SD PRESS

Density (Y1)

26055.62

< 0.0001

1.0000

0.9999

709.958

2.124 x 10-3

1.646 x 10-3

(P: probability of error; AP: adequate precision; SD: standard deviation; PRESS: predicted residual error sum of squares) The ANOVA results is possible to measure the signal to noise ratio of the experimental data by studying the adequate precision (AP) value. A ratio that is larger than 4 is preferred and indicates model discrimination. This means the model can be used to navigate the design space (Beg et al., 2003). In this study, the high AP value is greater than 4 which is desired. A diagnostic plot between actual and predicted values can be utilized to justify the model satisfactoriness. Fig. 1 shows the diagnostic plot of the predicted versus actual values for density (first response tested).

6 OTC-OTC-26376-MS

Fig. 1. Predicted versus actual values plot from Design Expert for the drilling fluid density

From the plot, we can deduce that the actual density values were in good agreement with the ones obtained from the model (predicted values). The points in Fig. 1 mostly lie along the diagonal line which indicates that the model gives a very good estimation of the predicted values. This means the predicted densities are very close to the actual experimental values. The 3D response surface plot in Fig. 2 shows the relationship between density as a function of glass bubbles, clay, xanthan gum and starch concentrations. The 3D plots in Fig. 2 shows that the density of the drilling fluid decreases with the addition of glass bubbles. This is due to the low density of glass bubbles which have properties that makes it a good density reducer. However, it tends to increase with the addition of other components (clay, xanthan gum and starch). These components are deemed to be heavier. Based on the 3D plots, the effect of glass bubbles concentration is seen to be more profound due to more significant changes in the density.

OTC-26376-MS 7

Figure 2. 3D response surface plots for density versus (a) A & B, (b) A & C, (c) A & D, (d) B & C (e) B

& D, (f) C & D Plastic Viscosity

Plastic viscosity is the second response studied. Experimental results were used to fit the model equation. ANOVA can be used to obtain the model that best fits the results. The mathematical model generated is quadratic in nature with a recommended transform of inversed square root. In terms of coded factors, Eq. (2) portrays the mathematical model that represents the relationship between the variables and the response (YP). 1/sqrt (Y2) = 0.12 – 0.017A – 9.879 x 10-4B – 6.061 x 10-3C – 2.163 x 10-3D – 8.038 x 10-4AB – 1.151 x 10-3AC – 4.660 x 10-4AD + 5.901 x 10-4BC – 4.405 x 10-3BD – 2.087 x 10-3CD + 1.829 x 10-3A2 + 6.918 x 10-6B2 – 1.060 x 10-3C2 – 8.852 x 10-4D2 (2)

8 OTC-OTC-26376-MS

where A is glass bubbles concentration; B is clay concentration; C is xanthan gum concentration and D is starch concentration. ANOVA analysis results for this response were given in Table 4. The model F-value implies that the model is significant. The probability (Prob > F) value is seen to be lower than 0.05. This means that the model terms, namely A, B, C, D, AB, AC, AD, BC, BD, CD, A2, C2 and D2 are significant. Remaining model terms were considered insignificant because the P values are more than 0.1000. The mathematical model fits the experimental results very well as indicated by similar high R2 and adjusted R2 values of 1.0000. The Lack of Fit (F-value) is not significant relative to the pure error. It requires a chance of 36.30% for a lack of fit value this large to occur due to noise.

Table 4. Analysis of variance (ANOVA) results for the second response (plastic viscosity)

Response F-value P R2 Adj. R2 AP SD PRESS

PV (Y2) 35441.19 < 0.0001 1.0000 1.0000 851.961 9.705 x 10-5 2.948 x 10-6

(P: probability of error; AP: adequate precision; SD: standard deviation; PRESS: predicted residual error sum of squares)

The AP ratio obtained was greater than 4 which is desirable and indicates an adequate signal. Hence, the model could be used to navigate the design space. A diagnostic plot of the actual and predicted values was used to justify the closeness of fit of the model. Fig. 3 illustrates the diagnostic plot for PV between predicted and actual values. In this plot, all the experimental design points fall on the diagonal line. The predicted values are in adequate agreement with the actual values. The 3D contour plots are shown in Fig. 4. Based on Fig. 4, the plots showed that PV increase with increasing concentrations of the four raw materials. The solid content in the mixture increased with addition of more raw materials into the fluid. This will make it thicker and harder to flow which will eventually increase the fluid PV. Addition of glass bubbles has the most effect on the fluid because the percentage of glass bubbles incorporated relative to other raw materials is the highest. The drilling fluid becomes more viscous as more glass bubbles are added.

Fig. 3. Predicted versus actual values plot from Design Expert for the drilling fluid plastic viscosity

OTC-26376-MS 9

Yield Point

The equation for the third response, YP is also quadratic in nature. Eq. (3) represents the quadratic model in coded factors with the best fit for YP (Y3). Y3 = 10.28 + 3.26A + 1.79B + 4.94C + 0.21D + 0.80AB + 1.43AC – 0.076AD + 0.23BC – 0.35BD – 0.18CD + 0.38A2 + 0.095B2 + 0.55C2 – 0.079D2 (3) where A is glass bubbles concentration; B is clay concentration; C is xanthan gum concentration and D is starch concentration

Table 5. Analysis of variance (ANOVA) results for the third response (yield point)

Response F-value P R2 Adj. R2 AP SD PRESS

YP (Y3) 956.72 < 0.0001 0.9996 0.9985 114.053 0.22 7.58

(P: probability of error; AP: adequate precision; SD: standard deviation; PRESS: predicted residual error sum of squares)

The ANOVA analysis results are presented in Table 5. The model F-value is found to be significant. The model terms A, B, C, D, AB, AC, BC, BD, A2 and C2 are found to be significant since the P value is less than 0.0001. The calculated R2 value and adjusted R2 are closed to 1 and in good agreement to each other. The Lack of Fit F-value is 1.52 which implies that it is not significant relative to the pure error. A non-significant lack of fit is desired as it indicates that the model fits. The high AP value also represents an adequate signal. Fig. 5 illustrates the diagnostic plots of the predicted versus actual values for YP. The points sit on the solid line which indicates that the predicted values are in adequate agreement with the actual values suggesting the model proposed could be used to predict the experimental outcome for various optimization conditions.

10 OTC-OTC-26376-MS

Fig. 4. 3D response surface plot for plastic viscosity versus (a) A & B, (b) A & C, (c) A & D, (d) B & C,

(e) B & D, (f) C & D

The 3D relationships and interactions between the raw materials concentration and the drilling fluid YP are portrayed in Fig. 6. Similar trend is also observed in Fig. 6 which indicates that increment of all the ingredients will cause the YP to increase as well. The presence of higher solid content increases flow resistance which may hinder molecular movements (Lim et al., 2015).

OTC-26376-MS 11

Fig. 5. Predicted versus actual values plot from Design Expert for the drilling fluid yield point

Drilling Fluid Formulation Optimization

In this study, the main objective is to obtain the optimum concentrations of raw materials to formulate a lightweight biopolymer drilling fluid for UBD application. The mathematical model equations from Eq. 1 to 3 were used to predict the desired responses: density, PV and YP of the lightweight drilling fluid. During the optimization process, the desired drilling fluid is required to have the lowest possible density and acceptable PV and YP to ensure good drilling fluid properties suitable for UBD. A desirability function (d) for multiple responses was used to simultaneously optimize all the four main raw materials concentration (factors) related to the three responses studied (Montgomery, 2001). Eq. (4) gives the desirability function (d):

d = mi

ri

i=1

N

∏⎡

⎣⎢

⎤

⎦⎥

1/ ri∑

(4) where N represents the number of responses, ri refers to the importance of a particular response and mi is the partial desirability function for specific responses. Generally, the optimum condition obtained from the optimization process is represented by the highest value of d (Montgomery, 2001). Table 6 represents the results of the process optimization along with its calculated desirability (d) values which were generated by the Design Expert software. The density of the drilling fluid was assigned a higher importance compared to PV and YP during the optimization. Based on the results in Table 6, the highest d value was 0.628. This value corresponds to the formulation condition where the value of factor A (glass bubbles concentration) is 24.46 %w/v, factor B (clay concentration) is 0.63 %w/v, factor C (xanthan gum concentration) is 0.21 %w/v and factor D (starch concentration) is 2.41 %w/v. The corresponding responses, density (Y1) is 6.4884 lb/gal, PV (Y2) is 63.6 cP and YP (Y3) is 5.9118 Pa. A good range of yield stress for drilling fluids should be less than 15 Pa (Khalil and Badrul, 2012a). Although the PV is relatively high, it is still within acceptable limits. The optimum fluid density can be used to instill underbalanced conditions. This is because the density is lesser than the density of aerated drilling fluids having densities around 6.9 lb/gal.

12 OTC-OTC-26376-MS

Fig. 6. 3D response surface plot for yield point versus (a) A & B, (b) A & C, (c) A & D, (d) B & C, (e)

B & D, (f) C & D

Experimental Validation

A real laboratory experiment was conducted at the selected optimum conditions to validate the optimum process conditions obtained from response surface methodology (RSM). The tests were performed based on the conditions given with the highest d value. The corresponding density, PV and YP were measured and compared with the predicted response values calculated using the models generated by RSM. Table 7 presents the confirmation data of the fluid rheological properties between optimum process condition calculated from the mathematical design by RSM and the experimental study. Based on the results, it can be concluded that the three response parameters obtained from the experiment and as estimated by the models were in good agreement. This is due to the high value of calculated accuracy, and low value of error and standard deviation (σ) between both values.

OTC-26376-MS 13

Table 6. Numerical optimization results for RSM

No. Factors Responses

d A (% w/v)

B (% w/v)

C (% w/v)

D (% w/v)

Y1 (lb/gal)

Y2 (cP)

Y3 (Pa)

1 24.46 0.63 0.21 2.41 6.4884 63.6 5.9118 0.628* 2 23.36 0.78 0.20 2.02 6.5420 64.9 4.9498 0.603 3 25.57 0.77 0.17 2.00 6.4213 68.5 3.9941 0.528 4 25.06 0.53 0.22 2.07 6.4466 69.1 5.9125 0.512 5 23.63 2.80 0.12 2.34 6.5457 72.2 4.0901 0.433 6 24.81 1.93 0.15 1.68 6.4739 74.6 4.0578 0.359 7 23.84 3.23 0.12 2.27 6.5373 76.9 4.6044 0.271 8 27.76 1.49 0.17 2.17 6.3168 77.1 5.3730 0.266 9 24.64 3.25 0.12 1.31 6.5104 78.4 4.3676 0.195

10 25.04 4.35 0.11 0.91 6.5296 78.6 5.8388 0.181 *selected (A: Glass bubbles concentration, B: Clay concentration, C: Xanthan gum concentration, D: Starch concentration, Y1: Density, Y2: Plastic viscosity, Y3: Yield point)

Table 7. Verification experimental results at optimum conditions

Factors Responses A

(% w/v) B

(% w/v) C

(% w/v) D

(% w/v) Y1

(lb/gal) Y2

(cP) Y3

(Pa) Optimized formulation condition calculated by RSM (predicted value)

24.46 0.63 0.21 2.41 6.4884 63.6 5.9118 Confirmation study of optimized formulation condition selected from Table 5 (experimental value)

24.46 0.63 0.21 2.41 6.4946 62.7 5.8997 Error

-0.0062

0.9

0.0121

Standard deviation (σ)

0.003 0.450 0.006

Accuracy (%) 99.90 98.58 99.80

Conclusion

RSM from Design Expert has successfully aided in the formulation of the lightweight biopolymer drilling fluid for underbalanced drilling. Based on the 3D response surface plots, the fluid density decreases with increasing glass bubbles concentration. However, the fluid density increases with increasing clay, xanthan gum and starch concentrations. PV and YP of the drilling fluid also increases with increasing raw materials concentration. Experimental results for all the responses fit the quadratic mathematical model predictions. The optimum formulation could be formulated under these conditions: Factor A (Glass bubbles concentration) is 24.46 %w/v, factor B (clay concentration) is 0.63 %w/v, factor C (xanthan gum concentration) is 0.21 %w/v and factor D (starch concentration) is 2.41 %w/v. The formulated fluid has a density of 6.49 lb/gal, PV of 63.6 cP and YP of 5.91 Pa. The validation experiment showed that the predicted results are in good agreement with the experimental outcome.

14 OTC-OTC-26376-MS

Acknowledgements

The authors deeply appreciate the contribution from UMRG Sub-Program 6, Project number: RP016-2012F and the Center for Energy Science, Cluster of Advanced Engineering Technology (AET) at University of Malaya, the support of University of Malaya IPPP grant, Project number: PG130-2012B, Center for Energy Science, University of Malaya and the Department of Chemical Engineering, Faculty of Engineering, University of Malaya.

References

Arco, M.J., Blanco, J.G., Marquez, R.L., Garavito, S.M., Tovar, J.G., Farias, A.F., Capo, J.A., 2000. Field application of glass bubbles as a density-reducing agent. Presented at the SPE Annual Technical Conference and Exhibition (SPE 62899), Dallas, Texas, 1-4 October. Beg, Q.K., Sahai, V., Gupta, R., 2003. Statistical media optimization and alkaline protease production from Bacillus mojavensis in a bioreactor. Process Biochem. 39, 203-209. Bol, G.M., 1986. Bentonite quality and quality-evaluation methods. Presented at the 1986 IADC/SPE Drilling Conference (SPE 13454), New Orleans, 6-8 March. Casas, J.A., Santos, V.E., Garcia-Ochoa, F., 2000. Xanthan gum production under several operational conditions: molecular structure and rheological properties. Enzyme Microb. Technol. 26, 282-291. Khalil, M., Badrul, M.J., 2012a. Herschel-Bulkley rheological parameters of a novel environmentally friendly lightweight biopolymer drilling fluid from xanthan gum and starch. J. Appl. Polym. Sci. 124, 595-606. Khalil, M., Badrul, M.J., 2012b. Viscoplastic modeling of a novel lightweight biopolymer drilling fluid for underbalanced drilling. Ind. Eng. Chem. Res. 51, 4056-4068. Lim, S.N., Khalil, M., Badrul, M.J., Brahim, S.A., 2015. Lightweight biopolymer drilling fluid for underbalanced drilling: An optimization study. J. Petrol. Sci. Eng. 129, 178-188.

Luo, S., Meng, Y., Tang, H., Zhou, Y., 2000. A new drill-in fluid used for successful underbalanced drilling. Presented at the SPE International Symposium on Formation Damage (SPE 58800), Lafayette, Louisiana, 23-24 February. Malloy, K.P., Shayegi, S. 2010. UBD or MPD: An engineering choice based on intent. Presented at the SPE/IADC Managed Pressure Drilling and Underbalanced Operations Conference and Exhibition (SPE/IADC 130563), Kuala Lumpur, Malaysia, 24-25 February.

Medley, Jr., G.H., Maurer, W.C., Garkasi, A.Y., 1995. Use of hollow glass spheres for underbalanced drilling fluid. Presented at the SPE Annual Technical Conference and Exhibition (SPE 30500), Dallas, Texas, 22-25 October. Montgomery, D.C., 2001. Design and Analysis of Experiments, Fifth Ed., John Wiley & Sons Inc., New York.

OTC-26376-MS 15

Noordin, M.Y., Venkatesh, V.C., Sharif, S., Elting, S., Abdullah, A., 2004. Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. J. Mater. Process. Technol. 145, 46-58.

Ostroot, K., Shayegi, S., Lewis, D., Lovorn, R., 2007. Comparison and advantages of underbalanced and managed-pressure drilling techniques: When should each be applied? Presented at the Offshore Technology Conference (OTC 18561), Houston, Texas, 30 April-3 May.

Tester, R.F., Karkalas, J., Qi, X., 2004. Starch-composition, fine structure and architecture. J. Cereal Sci. 39, 151-165.