Study on enhanced oil recovery by co2 microbubbles injection

It evaluated the effects of varying the concentrations of a xanthan gum XG polymer, a surfactant sodium dodecyl sulfate: SDS, and sodium chloride NaCl on both the stability and bubble si

STUDY ON ENHANCED OIL RECOVERY BY CO2 MICROBUBBLES INJECTION

Le Nguyen Hai Nam

September 2022

STUDY ON ENHANCED OIL RECOVERY BY CO2 MICROBUBBLES INJECTION

by

Le Nguyen Hai Nam

September 2022

ABSTRACT

Injecting carbon dioxide (CO2) to enhance oil recovery (EOR) during the tertiary stage is expected to be a reasonable and sustainable method to dismiss greenhouse gas emissions However, in many current CO2-EOR projects, their performance is not always achievable due to several drawbacks, such as density effect, gas channeling, and poor sweep efficiency in heterogeneous porous media All those challenges limit the effectiveness of

CO2-EOR and raise the additional cost Therefore, it is supposed that using CO2

microbubbles would potentially overcome the challenges in the heterogeneous reservoir and promote a practical approach to achieving both oil improvement and CO2 sequestration goals Microbubbles – Colloidal Gas Aphrons (CGAs) have been reported as unique bubbles with micro-size (10-100 m) consisting of a multilayer shell of surfactant molecules and a spherical gaseous core The previous studies reported the significant stability of microbubbles in comparison with conventional foam and their flow restriction ability However, there have been few sufficient studies on the characteristic of CO2 microbubble and their selective plugging performance to improve oil recovery in the heterogeneous reservoir

In this thesis, CO2 microbubbles injection is proposed and examined with designed laboratory experiments Experimental study thoroughly includes CO2 microbubbles system generation, characteristics determination, and flow behavior in porous media from homogeneous sandpack and heterogeneous sandpack models In addition, various features, consisting of CO2 microbubbles fluid characteristics, formation permeability, and operation conditions, were experimentally evaluated for their influences on flow performance in the simulated reservoir Based on the findings from flow experiments in porous media, the oil recovery flooding scheme using CO2 microbubbles has been proposed and successfully

Chapter 1 reviews the fundamental of the oil recovery process from literature The overview of CO2-EOR was discussed with the associated challenges Therein also highlighted the importance of microbubbles used in petroleum engineering The research problem statement and objectives were presented in that regard

Chapter 2 introduces the experimental process with an overview of measurement methods and analysis It evaluated the effects of varying the concentrations of a xanthan gum (XG) polymer, a surfactant (sodium dodecyl sulfate: SDS), and sodium chloride (NaCl) on both the stability and bubble size distribution (BSD) of CO2 microbubbles CO2 microbubble dispersions were prepared using a high-speed homogenizer in conjunction with the diffusion

of gaseous CO2 through aqueous solutions The stability of each dispersion was ascertained using a drainage test, while the BSD was determined by optical microscopy and fitted to either normal, log-normal or Weibull functions The results showed that a Weibull distribution gave the most accurate fit for all experimental data Increases in either the SDS

or XG polymer concentration were found to decrease the microbubble size However, these same changes increased the microbubble stability as a consequence of structural enhancement Stability was also reduced as the NaCl concentration was increased because of the gravitational effect and coalescence

Chapter 3 investigates the plugging performance of CO2 microbubbles in both homogeneous and heterogeneous porous media through a series of sandpack experiments First of all, CO2 microbubble fluids were generated by stirring CO2 gas diffused into polymer (Xanthan gum (XG)) and surfactant (Sodium dodecyl sulfate (SDS)) solution with different gas: liquid ratios Then, CO2 microbubbles fluids were injected into single-core and dual-core sandpack systems The results show that the rheological behaviors of CO2 microbubble fluids in this study followed the Power-law model at room temperature The apparent viscosity of CO2 microbubble fluid increased as the gas: liquid ratio increased CO2

microbubbles could block pore throat due to the “Jamin effect” and increase the resistance in porous media The blocking ability of CO2 microbubbles reached an optimal value at the gas:liquid ratio of 20 % in the homogeneous porous media Moreover, the selective pugging ability of CO2 microbubbles in dual-core sandpack tests was significant CO2 microbubbles

exhibited a good flow control performance in the high permeability region and flexibility to flow over the pore constrictions in the low permeability region, leading to an ultimate fractional flow proportion (50%:50%) in the dual-core sandpack model with a permeability differential of 1.0:2.0 darcy The fractional flow ratio was considerable compared with a polymer injection At the higher heterogeneity of porous media (0.5:2.0 darcy), CO2

microbubble fluid could still establish a good swept performance This makes CO2

microbubble fluid injection a promising candidate for heterogeneous reservoirs where conventional CO2 flooding processes have limited ability

Chapter 4 evaluates the performance of CO2 microbubbles on oil recovery from the single sandpack and parallel sandpack flooding tests All flooding tests were conducted at

45oC The flooding scheme consisted of the injection of brine water (20000 mg/L NaCl concentration) followed by the CO2 microbubble injection In the single sandpack flooding test, about 61.4 % of the original oil in place (OOIP) was recovered after 3 pore volume (PV)

of water flooding Then 0.5 PV of CO2 microbubble was injected, which caused a blockage

in pore spaces The oil recovery was improved by 23.6% by the chase water flooding at the following stage In the heterogeneous sandpack model with the low/high permeability ratio

of 1:4, the CO2 microbubble could adjust to fractional flows in the heterogeneous reservoir and displace the remaining oil in the low permeability region As a result, the injection of

CO2 microbubbles improved the total oil recovery up to 86.9% compared to the injection of base solution with 75.28% in total When the low/high permeability ratio of the parallel sandpack is reduced to 1:2, injecting CO2 microbubbles enhanced the oil recovery to 93.28

% in total The displacement efficiency increases with the decrease of sandpack heterogeneity The results suggest that CO2 microbubble is favorable to enhanced oil recovery in heterogeneous reservoirs

Chapter 5 concludes the present research by highlighting the major findings and further research suggestions

TABLE OF CONTENTS

ABSTRACT I TABLE OF CONTENTS IV LIST OF FIGURES VII LIST OF TABLES XII ACKNOWLEDGEMENTS XIII

CHAPTER 1 INTRODUCTION 1

1.1 Research Overview 1

1.1.1 Enhanced Oil Recovery Using Carbon Dioxide (CO2-EOR) 1

1.1.2 CO2 microbubbles – Colloidal gas aphrons 4

1.2 Research Objectives 10

1.3 Thesis Outline 10

CHAPTER 2 EXPERIMENTAL DESIGN AND CHARACTERIZATION OF CO 2 MICROBUBBLES 12

2.1 Introduction 12

2.2 Materials 13

2.3 Experimental methods 13

2.3.1 Preparation of base solutions 13

2.3.2 Preparation of CO2 microbubble fluids 14

2.3.3 CO2 microbubble stability assessments 16

2.3.4 Determination of CO2 microbubble size 17

2.4 Results and Discussions 21

2.4.1 Visualization of CO2 microbubbles 21

2.4.2 Stability trials 22

2.4.3 CO2 microbubble size distribution 27

2.4.4 Factors affecting the BSD of CO2 microbubbles 31

2.5 Summary 39

CHAPTER 3 FLOW PERFORMANCE OF CO 2 MICROBUBBLES IN POROUS MEDIA 40

3.1 Introduction 40

3.2 Experimental section 40

3.2.1 Materials 40

3.2.2 CO2 microbubble fluids preparation 41

3.2.3 Measurement of rheological property 41

3.2.4 Statistical analysis 43

3.2.5 Preparation of sandpacks 43

3.2.6 CO2 microbubble fluid flow tests 44

3.3 Results and Dicussions 46

3.3.1 Characterization of CO2 microbubbles 46

3.3.2 CO2 Microbubble Fluid Flow in Homogeneous Porous Media 52

3.3.3 CO2 Microbubble Fluid Flow in Heterogeneous Porous Media 59

4.1 Introduction 66

4.2 Experimental section 67

4.2.1 Materials 67

4.2.2 Flooding experiment 68

4.3 Results and Discussion 71

4.3.1 Oil recovery in single sandpack 71

4.3.2 Oil recovery in parallel sandpack 74

4.4 Summary 81

CHAPTER 5 CONCLUSIONS AND RECOMMENDATION 82

5.1 Major findings of the research 82

5.2 Future possibility 86

REFERENCES 87

LIST OF FIGURES

Figure 1.1 Long-term world energy consumption with projection to 2050(adapted from

U.S Energy Information Administration, October 2021) 1

Figure 1.2 Overview of oil recovery stages 2 Figure 2.1 Schematic diagram of the preparation of CO2 microbubbles: (1)

Homogenizer, (2) Polymer and surfactant solution, (3) Porous stone (gas diffuser), (4) Gas flow meter, (5) Pressure regulator, (6) CO2 gas tank 15

Figure 2.2 Apparatus of CO2 microbubbles generation 15

Figure 2.3 Schematic process of drainage test 17 Figure 2.4 Set up for visualization of CO2 microbubbles: (1) Microscope, (2) charge-coupled device (CCD) camera, (3) computer, and (4) glass-slide 18

Figure 2.5 Analyzing procedure of a CO2 microbubbles sample (a) Raw image, (b) 8-bit image enhanced by contrast, (c) Image after thresholding, (d) Bubbles counting and analyzing 19

Figure 2.6 The difference between CO2 microbubbles and conventional foam, introduced

in their structure 22

Figure 2.7 Experimental photograph of CO2 microbubbles drainage with SDS ( 3g/L) and XG (0 g/L) 23

Figure 2.10 Effect of NaCl concentration on the stability of CO2 microbubbles (with 3 g/L SDS and 5 g/L XG) 26

Figure 2.11 Micrographs of CO2 microbubbles: (a) S1 sample, (b) S2 sample, (c) S3 sample, (d) S4 sample, (e) S5 sample, (f) S6 sample, and (g) S7 sample Scale bar:

100 m 27

Figure 2.12 Bubble size distribution (BSD curves predicted by Normal, Log-normal and

Weibull model for (a) S1 sample, (b) S2 sample, (c) S3 sample, (d) S4 sample, (e) S5 sample, (f) S6 sample, and (g) S7 sample 29

Figure 2.13 Q-Q plots for (a) S1 sample, (b) S2 sample, (c) S3 sample, (d) S4 sample, (e)

S5 sample, (f) S6 sample, and (g) S7 sample 30

Figure 2.14 Influence of SDS concentration (1, 2, 3 g/L) upon bubble size (b) BSD at

three SDS concentrations, experimental and fitted results are represented using icons and solid lines, respectively 31

Figure 2.15 Influence of XG concentration (1,3,5 g/L) upon bubble size (b) BSD at three

XG concentrations, experimental and fitted results are represented using icons and solid lines, respectively 33

Figure 2.16 (a) Influence of NaCl concentration (0, 10, 20 g/L) upon bubble size (b)

BSD at three NaCl concentrations, experimental and fitted results are represented using icons and solid lines, respectively 34

Figure 2.17 Microscopic views of CO2 microbubbles samples, 60 minutes after

preparation (a) S1 sample, (b) S2 sample, (c) S3 sample And (d) Bubble size

distribution functions 36

Figure 2.18 Microscopic views of CO2 microbubbles samples, 60 minutes after

preparation (a) S5 sample, (b) S4 sample, (c) S3 sample And (d) Bubble size

distribution functions 37

Figure 2.19 Microscopic views of CO2 microbubbles samples, 60 minutes after

preparation (a) S3 sample, (b) S6 sample, (c) S7 sample And (d) Bubble size

Figure 3.5 Stability of CO2 microbubble fluids at different gas: liquid ratios 49

Figure 3.6 Values of (a) R2 and (b) RMSE of fitting models with CO2 microbubble fluids

at different gas: liquid ratios 50

Figure 3.7 Fitting cures of Power-law model for CO2 microbubble fluids at different gas: liquid ratios 51

Figure 3.8 The plot of viscosity vs shear rate for CO2 microbubble fluids at different gas: liquid ratios 52

Figure 3.10 Schematic representation of blockage mechanism as CO2 microbubble enters

the pore throat 54

Figure 3.11 Pressure drop changes during CO2 microbubble fluid injecting as a function of gas: liquid ratios 55

Figure 3.12 Pressure drop changes during CO2 microbubble fluid injecting into sandpack with different permeabilities 57

Figure 3.13 Pressure drop changes during CO2 microbubble fluid injecting with different flow rates 58

Figure 3.14 Fractional flows of (a) CO2 microbubble fluid with  = 20% and (b) XG polymer solution (5000 mg/L) in dual-core sandpack (permeability ratio of 1.0:2.0 darcy) 60

Figure 3.15 Pressure drops of CO2 microbubble fluid with  = 20% and XG polymer solution (5000 mg/L) in dual-core sandpack (permeability ratio of 1.0:2.0 darcy) 61

Figure 3.16 Schematic illustration of the flow of CO2 microbubble fluid in heterogeneous porous media 62

Figure 3.17 Fractional flows of CO2 microbubble fluid with  = 20% in dual-core sandpacks with different permeability ratios: (a) 0.5:1.0 darcy; (b) 0.5:2.0 darcy 63

Figure 3.18 Pressure drops of CO2 microbubble fluid with  = 20% in dual-core sandpacks with different permeability ratios: 0.5:1.0 darcy and 0.5:2.0 darcy 64

Figure 4.1 General sketch of problem in this chapter 66

Figure 4.2 Size distribution of CO2 microbubbles 67

Figure 4.3 Schematic of sandpack flooding test 69

Figure 4.4 Oil recovery performance from single sandpack flooding test 71 Figure 4.5 Oil displacement in micrometers scale and corresponding effluents at the

flooding stages ( major flow) 73

Figure 4.6 Fractional flow in parallel sandpack with base solution injection (Permeability

LIST OF TABLES

Table 2.1 CO2 microbubbles with various compositions 14

Table 2.2 AD values and corresponding P-values for different bubble size distributions 30 Table 3.1 Rheological model’s parameters of CO2 microbubble fluids 49

Table 4.1 Summary of flooding experiments 70 Table 4.2 Flooding results of the parallel sandpack flooding tests 79 Table 4.3 The average pore size and the ratio of bubble size to pore size for sanpacks 80

ACKNOWLEDGEMENTS

I wish to thank my supervisor, Professor Yuichi Sugai, for his kind support, understanding, encouragement, and guidance throughout this study Thanks are also extended to Professor Kyuro Sasaki and Assistant Professor Ronald Nguele for their kind support, valuable suggestions, and advice for my research I would like to thank Assistant Professor Takehiro Esaki for his kind support

I would like to acknowledge Professor Yamada Yasuhiro and Associate Professor Gen Inoue for their critical comments

I am thankful to Dr Thanh-Hung Vo for his friendship, motivation, and support during the course of this study I cherish every moment I spent with my labmates in the Resources Production and Safety Laboratory for their helpfulness and kindness

I gratefully acknowledge the financial support received from AUN/Seed-Net program (JICA) to pursue my Ph.D study at Kyushu University

I would like to give my sincere gratitude to Ho Chi Minh City University of Technology, Vietnam National University – Ho Chi Minh City for giving me the opportunity to pursue

my study

Last but not least, special thanks to my beloved family: my parents, my wife, and my son for their unconditional love and support

Chapter 1

INTRODUCTION

1.1 Research Overview

1.1.1 Enhanced Oil Recovery Using Carbon Dioxide (CO 2 -EOR)

Global energy consumption is expected to grow continuously with an average annual rate change of 1.3 %, from 601.5 quadrillions Btu in 2020 to 886.3 quadrillion Btu

in 2050 (Nalley et al., 2021) Hydrocarbon resources (including conventional oil, unconventional oil, and natural gas) have played a critical role in addressing the world’s

energy needs until at least the middle of this century (Figure 1.1)

Figure 1.1 Long-term world energy consumption with projection to 2050(adapted from

U.S Energy Information Administration, October 2021)

Figure 1.2 Overview of oil recovery stages

Generally, the production time along the life of conventional petroleum fields has been subdivided into three phases: primary, secondary, and tertiary recovery During the primary stage, hydrocarbon fluids are produced by natural mechanisms, including dissolved-gas drive, gas-cap drive, aquifer drive, gravity drainage, and combined drive (Willhite, 2018) Besides, artificial techniques could be applied to lift the oil in the production tubing once the reservoir pressure reduces Typically, only up to 30% of the original oil in place (OOIP) is produced after this stage In the secondary stage, waterflooding is commonly utilized to force the remaining oil out of the pore structure and maintain the reservoir pressure This approach could recover about 30-50% of OOIP and

With the increasing impact of global warming over the last decades, more carbon dioxide (CO2) needs to be diminished to a sustainable level as a long-term consequence Hence, carbon mitigation techniques have gained global attention as a potential method to reduce CO2 emissions At present, most research focuses on the topics of carbon capture and storage (CCS) and carbon capture, utilization and storage (CCUS) CCS comprises capturing CO2 generated by burning fossil fuels for energy and sequestering it underground permanently (Dejam et al., 2018a, 2018b; Vo Thanh et al., 2019, 2021; Vo Thanh, Sugai, Nguele, et al., 2020)

While in the CCUS, captured CO2 is effectively utilized in industrial processes (Han et al., 2019) One of the primary techniques of CCUS is the utilization of CO2 for enhancing oil recovery (CO2-EOR)(Mohagheghian et al., 2019; Vo Thanh, Sugai, & Sasaki, 2020) Additional oil could be extracted by utilizing large amounts of CO2 Moreover, depleted oil and gas reservoirs can be potential formations for CO2 storage (Bachu, 2016) From an industrial ecology perspective (Meylan et al., 2015), CO2-EOR is reasonable and sustainable to control global CO2 emissions

The advantages of employing CO2 as a displacement agent during EOR have received significant attention in the petroleum industry An additional benefit is that this method would provide an economical approach to the geological storage of CO2 to reduce atmospheric CO2 concentrations (Azzolina et al., 2015; Mohagheghian et al., 2019; Vo Thanh et al., 2019; Vo Thanh, Sugai, & Sasaki, 2020; Vo Thanh, Sugai, Nguele, et al., 2020) However, CO2 flooding can result in low recovery efficiencies because of the high mobility of CO2 in flooding areas Specifically, the injected CO2 tends to flow through

highly permeable layers or fractures, which leads to poor sweeping efficiency in the low permeability zones (Lake, 1989) For this, CO2 foam is often considered to improve mobility control during CO2-EOR operation by increasing gas viscosity and redirecting fluid to low permeable areas (Du et al., 2018, 2020; J Yang et al., 2019) However, the foam becomes unstable under harsh environments Therefore, the stability of foam is a challenge for this application in EOR (Razavi et al., 2020)

1.1.2 CO 2 microbubbles – Colloidal gas aphrons

Recently, microbubbles (defined as having sizes of 10 to 100 μm) have become of interest as a means of removing contaminants from aqueous solutions (Hashim et al., 2012; Molaei et al., 2015), as components of oil well-drilling fluids (Alizadeh et al., 2019; Pasdar

et al., 2019; Tabzar et al., 2015a; Zhu et al., 2020) and also with regard to EOR (Natawijaya

et al., 2020; Shenglong Shi, Yefei Wang, Shixun Bai, Mingchen Ding, 2017) The use of microbubble-based fluids is growing rapidly in the oil and gas industry One advantage of microbubbles is that they have a unique structure differing from conventional foams that maintain their stability for longer periods under severe conditions

The microbubbles were first reported as colloidal gas aphrons (CGAs) by (Sebba, 1987) CO2 microbubbles comprise a spherical core made of gaseous CO2 with a multilayer covering comprising surfactant molecules and a viscous liquid This multilayer structure, made of an inner layer (between the gaseous core and the liquid layer) and an

the bubbles comprise a spherical gas core with a surfactant layer (Telmadarreie et al., 2016)

Wang et al., (2001) applied CGAs to separate the heavy metal elements from the aqueous solution in mineral processing They found that CGAs could have an outstanding performance in CuO flotation at specific operating conditions Waters et al., (2008) evaluated the efficiency of the CGAs flotation system in separating CuO from SiO2 The results revealed that CGAs utilization increased CuO recovery significantly compared with previous methods

Hashim et al., (1998) recommend using CGAs in recovering cellulosic pulp from contaminated effluent Nancy Bjorndalen et al., (2009) pointed out that CGAs fluid can successfully block the micromodel The oil-based CGAs drilling fluid was examined by (Shivhare et al., 2014) They reported that the aphrons exhibit a good plugging performance

in porous media and restrict formation damage due to fluid invasion H N Bjorndalen et al., (2014)conducted flow tests using CGAs fluid prepared by polymer and surfactant

It was inferred that CGAs fluid could effectively block the water-wet porous media Pasdar et al., (2019) studied the fluid invasion control ability of CGAs-based fluid using a micromodel system They found that fluid invasion through fracture can be decreased significantly by injecting CGAs fluid Several studies have indicated that microbubbles can seal highly permeable layers in heterogeneous porous media during the EOR process and

so improve sweeping efficiency and oil recovery One group applied a microbubble foam

to shallow reservoirs and concluded that these microbubbles blocked porous media via the Jamin effect (E Yang et al., 2020) As a microbubble flows through a pore, it will

experience a capillary force if its diameter is larger than the pore throat (Wright, 1933) (Shi et al., 2016)conducted double sandpack experiments and determined that microbubbles blocked the high permeability sandpack while increasing the swept volume

in the sandpack with lower permeability (Shenglong Shi, Yefei Wang, Shixun Bai, Mingchen Ding, 2017) attempted a micromodel test of plugging performance and showed that microbubbles were capable of temporarily plugging the highly permeable regions such that subsequent flow was forced into the low permeability areas

The CGA flow properties in porous media were also investigated using a modeling approach Alizadeh et al., (2015) developed a mathematical model to predict the stability

of microbubbles in drilling fluid in operational conditions of a gas well Alizadeh and Alizadeh et al., (2017) also presented a mathematical model to analyze the transportation

of microbubbles in porous media They thought that the invasion of microbubble fluid in porous media is influenced by the ratio of bubble diameter to grain size

Several studies found remarkable stability of the microbubbles compared with conventional foams(N Bjorndalen et al., 2008; Growcock et al., 2004; Pasdar et al., 2018a, 2018c) Ivan et al., (2001) examined the effect of elevated pressure on CGAs and found that these foams remained stable up to a pressure of 10.3 MPa, while Fred Growcock, (2004) demonstrated that CGAs could survive for a significant time span under pressurization as high as 27.6 MPa N Bjorndalen et al., (2008) visually assessed the

and reported that these materials were stable up to 13.7 MPa, while (N Bjorndalen et al., 2008) showed that CGAs became unstable at temperatures ranging from 50 to 75 °C The blocking performance of microbubbles is greatly affected by their stability and size distribution (Longe, 1989) and (Jauregi et al., 1997) evaluated the effects of the amount of surfactant on the stability of CGAs, and both concluded that increasing the surfactant concentration improved the CGA stability (Pasdar et al., 2020) showed that increased viscosity also enhanced the stability of CGAs (Tabzar et al., 2015b) performed static drainage tests and observed that the amount of a xanthan gum (XG) polymer in the CGA dispersion played an essential role in conferring stability Overall, the stability of microbubbles appears to be greatly affected by the concentrations of both polymers and surfactants in the foam

Both static liquid drainage (Yan et al., 2005) and bubble size distribution (Pasdar

et al., 2018c) can be used to assess the stability of microbubbles The static liquid drainage methods measure the liquid phase volume drained from the microbubble system as a function of time, and several researchers have used this technique to study the stability of CGAs (Yan et al., 2005) proposed an empirical model to characterize the liquid drainage from CGA dispersions, while (Sadeghialiabadi et al., 2015) investigated the effects of geometric and operating variables on CGA stability using the drainage curve method (Tabzar et al., 2020) also studied the stability of nano-enhanced CGAs by monitoring drainage rates In contrast, the bubble size distribution technique evaluates increases in bubble size over time as a measure of stability Several methods have been developed to

ascertain bubble size distribution, including visual, electro-resistivity and acoustic techniques (Chen et al., 2017)

Visual methods (including microscopy, photography, and video microscopy) are most frequently used to measure particle and bubble size distributions (Maaref et al., 2018; Moradi et al., 2011) Optical microscopy in particular has been widely employed to ascertain the size and stability of CGAs As an example, Zhu et al (Zhu et al., 2020) determined the bubble size distribution and examined the effect of attapulgite on CGA drilling fluid stability using optical microscopy in conjunction with a Gaussian statistical distribution Parmar et al (Rajeev Parmar, 2015) generated a microbubble suspension by transferring a mixture of gas and liquid to a pressure chamber and found a Weibull distribution of bubble sizes based on image analysis It should be noted that neither of the above two studies employed a goodness of fit test to determine which mathematical distribution function best represented the experimental data Raquibul (Alam et al., 2017) proposed that the bubbles produced in a laboratory-scale electroflotation cell had a log-normal diameter distribution based on high goodness of fit Nevertheless, few reports to date have examined the size distributions of CO2 microbubbles intended for EOR

In addition, there is still disagreement concerning the effects of the surfactant on the microbubble diameter distribution Xu et al (Xu et al., 2009a) reported that increases

in the surfactant concentration decreased the bubble diameter, in contrast to the statement

concentrations lower than the critical micelle concentration (CMC), increments in the amount of surfactant decreased the CGA bubble size (N Bjorndalen et al., 2008)

(Telmadarreie et al., 2016) focused on the effectiveness of employing CO2

microbubbles as an injection agent to improve heavy oil recovery based on flooding tests

in heterogeneous porous media The results showed that injecting CO2 microbubbles significantly increased the sweeping efficiency relative to the performance of the base fluid (Natawijaya et al., 2020) performed EOR flooding tests in parallel sandpacks using

CO2 microbubbles They observed that these microbubbles blocked pores in the high permeability sandpack, therefore improving the displacement efficiency in the low permeability sandpack and increasing the cumulative oil production

The contradiction in these results shows the necessity of performing additional work to study the effects of surfactant concentration on microbubble size There is also a need for an efficient means of reducing experimental uncertainty when investigating the size distributions of CO2 microbubbles Meanwhile, CO2 microbubbles can be considered

to have more potential than conventional methods due to being flexible and eco-friendly for CO2-EOR and storage projects Several experimental studies have demonstrated that microbubbles can be employed as a temporary blocking agent However, few scholars have systematically investigated factors that affect the plugging ability of CO2 microbubbles in homogeneous and heterogeneous porous media

1.2 Research Objectives

This study primarily aims to investigate CO2 microbubble generation considering the stability, bubble size distribution, rheological property, and EOR efficiency of microbubble solution by conducting a series of laboratory experiments The detail of each objective is presented as follows:

• To investigate the stability and bubble size distribution with the impact of chemical components

• To understand the plugging mechanism of microbubbles in porous media through sandpack flooding experiments

• To evaluate the EOR ability of microbubbles in both homogeneous and heterogeneous formation

1.3 Thesis Outline

This dissertation consists of five chapters

Chapter 1 starts with an introduction to the importance of CO2-EOR and emphasizes the advantages of CGAs-microbubbles in the petroleum industry This chapter also discusses the CO2 microbubbles and their characteristics based on previous studies

bubble size distribution was determined by optical microscopy and fitted to theoretical distribution functions

Chapter 3 focuses on the plugging ability of CO2 microbubbles in porous media Firstly, the measurements and analyses have been carried out to examine the characteristics of CO2

microbubbles fluid Then, it describes the flooding experiments under various conditions The effect of gas: liquid ratio, formation permeability, injection flow rate and heterogeneity have been evaluated

Chapter 4 discusses the performance of CO2 microbubbles in enhanced oil recovery The effectiveness of the proposed approach was evaluated by a series of sandpack flooding experiments The results from homogeneous and heterogeneous sandpacks demonstrate its advantages over in oil production improvement

Chapter 5: summarizes the findings from the present research Finally, recommendations are made for further studies

This chapter examined the effects of the polymer, surfactant and salt concentrations

on the stability of CO2 microbubbles using drainage tests This work also employed microscopic imaging together with statistical interpretation to determine the effects of the above parameters on the microbubble size distribution A further objective of this chapter

2.2 Materials

The chemicals used in this study include biopolymer Xanthan Gum (XG) and anionic surfactant sodium dodecyl sulfate (SDS, purity ≥ 99.8) CO2 was supplied by a domestic company with a purity of 99.9% Deionized water was used to make the base solutions with specific chemicals concentrations These components are successfully applied in previous studies and provide good generating microbubbles performance (Arabloo et al., 2014; Tabzar et al., 2015a) Sodium chloride (NaCl) was added to examine the effect of salinity on the CO2 microbubble fluids and to prepare the synthetic formation brine All chemicals were supplied by Junsei Chemical (Japan) and deionized (DI) water was used to prepare all aqueous solutions

2.3 Experimental methods

2.3.1 Preparation of base solutions

A series of saline solutions was prepared by dissolving specific amounts of NaCl

in 300 mL DI water (in Table 2.1) The base solutions were then obtained by adding

varying amounts of the SDS and XG polymer to these saline solutions, followed by stirring for 2 h using a magnetic stirrer (MS-H280-Pro Model, Dlab Scientific Inc.) at 1000 rpm to achieve complete dissolution

Table 2.1 CO2 microbubbles with various compositions Sample

XG concentration (g/L)

SDS concentration (g/L)

2.3.2 Preparation of CO 2 microbubble fluids

Figure 2.1 presents a diagram showing the apparatus used to generate CO2

microbubbles In this process, 200 mL of a base solution was transferred into a 300 mL container, after which CO2 gas (99.9% pure) was injected from the bottom of the container through a diffuser at a certain flow rate using a flow controller The dispersion was subsequently homogenized by stirring at a rate of 8000 rpm for 4 min using an overhead mixer (HG-200 Hsiangtai Model, As One Corporation) The gaseous CO2 diffused into the base solution eventually broke down into microbubbles with micron-scale diameters

(Figure 2.2) All experiments were performed at ambient temperature and pressure

Figure 2.1 Schematic diagram of the preparation of CO2 microbubbles: (1)

Homogenizer, (2) Polymer and surfactant solution, (3) Porous stone (gas diffuser), (4)

Gas flow meter, (5) Pressure regulator, (6) CO2 gas tank

Figure 2.2 Apparatus of CO2 microbubbles generation

2.3.3 CO 2 microbubble stability assessments

In preparation for stability tests, a quantity of each CO2 microbubble dispersion was transferred into a 300-mL graduated cylinder and allowed to stand As time passed, the aqueous solution drained from the microbubbles and the volume of this solution was recorded over time The maximum volume of drained liquid (200 mL) was obtained at the point at which the CO2 microbubbles had entirely collapsed (in Figure 2.3) A kinetic

model was used to quantify the base solution drainage from each CO2 microbubble dispersion over time This model was previously proposed by Yan et al (Yan et al., 2005)

and is based on Equation 2.1:

𝑉𝑡= 𝑉𝐹 𝑡𝑛

where V t (mL) and V F (mL) are the volume of drained solution at time t (min) and

the final volume of drained solution, respectively, T 1/2 (min) is the half-life (the time

required for the drained liquid to equal 50% of V F ), and n is an exponent that defines the

sigmoid character of the model curve When assessing the stability of CO2 microbubbles,

a specific drainage rate constant (K) can be obtained by differentiating Equation 2.2 as

(Yan et al., 2005):

Figure 2.3 Schematic process of drainage test 2.3.4 Determination of CO2 microbubble size

The CO2 microbubbles were visualized and the bubble size distributions were evaluated by taking small aliquots of each dispersion from the test containers immediately after preparation of the dispersion and 60 min after preparation Each sample was transferred to a glass microscope slide A transmitted-light microscope with a charge-couple device camera connecting to a desktop computer was used to capture digital images

of the CO2 microbubbles

Figure 2.4 Set up for visualization of CO2 microbubbles: (1) Microscope, (2)

charge-coupled device (CCD) camera, (3) computer, and (4) glass-slide

Several images were acquired from each specimen for statistical analysis and the average diameters of the CO2 microbubbles in these images as well as the D10, D50 and D90

values were determined Here, D10, D50 and D90 represent the diameters for which 10%,

50% and 90%, respectively, of the microbubbles were smaller in size Figure 2.4 presents

a diagram of the microscopy imaging system used to evaluate the CO2 microbubbles

The captured images were processed using the ImageJ software package after being

bubbles from each sample to ensure a representative size distribution Figure 2.5

summarizes the enhancement procedure for a typical image

Figure 2.5 Analyzing procedure of a CO2 microbubbles sample (a) Raw image, (b) 8-bit image enhanced by contrast, (c) Image after thresholding, (d) Bubbles counting and

analyzing

Bubble size distribution

The output data from the ImageJ software were analyzed in the MATLAB program

to obtain each bubble size distribution (BSD) and were also subjected to additional statistical analysis It was essential to determine the exact distributions and so the optimal probability distribution function (PDF) was applied to the experimentally measured size data

Around 300 microbubbles were examined in each sample to estimate the average diameter

(D avg), given by Equation 2.3:

𝐷𝑎𝑣𝑔 =∑𝑁𝑖=1𝐷𝑖

where D i (m) is the diameter of the observed microbubbles and N is the total

number of microbubbles in each sample

Three pdfs were applied to the distributions: normal, log-normal and Weibull A normal PDF is one that conforms to the equation (Pinho et al., 2018):

𝑓(𝑥) =𝑒

−(𝑥−𝜇)2

2𝜎2

where  is the mean, σ is the standard deviation, and x represents the diameter of a

bubble The log-normal PDF is given by (Moradi et al., 2011):

𝑓(𝑥) = 1

𝑥𝜎√2𝜋𝑒

−(ln(𝑥)−𝜇)2

where σ is the theoretical standard deviation, µ is the theoretical mean of ln(x), and

x is the diameter of a bubble The Weibull PDF can be written as (Moradi et al., 2011):

where x is the diameter of a bubble and a and b are shape and scale parameters,

respectively

given dataset Higher P-values also indicate better agreement between the data and the theoretical distribution, while a P-value less than a significance level of 5% demonstrates that the experimental data do not conform to a particular theoretical distribution (Alam et al., 2017)

2.4 Results and Discussions

2.4.1 Visualization of CO 2 microbubbles

Figure 2.6 presents a diagram of an aphron microbubble based on the structure

proposed by Sebba along with an optical microscopy image of the present CO2

microbubbles As noted, CO2 microbubbles can provide a CGA in which the microbubbles have a gaseous CO2 core surrounding by a thin aqueous film This thin film is made of surfactant molecules and has three layers (Sebba, 1987) The addition of XG polymer increases the viscosity of the outer film and so strengthens the aphron structure such that the foam can endure harsh conditions such as high pressure and temperature (Pasdar et al., 2018b) Compared with microbubbles, a typical bubble is made by one single covering of surfactant molecules

Figure 2.6 The difference between CO2 microbubbles and conventional foam, introduced

in their structure

2.4.2 Stability trials

Figure 2.7 shows the experimental CO2 microbubble drainage process and

demonstrates that the dispersion separated into two phases over time Figure 2.8 plots the

drainage data for solutions having varying SDS concentrations without the XG polymer as functions of time It can be seen that all the microbubbles collapsed entirely within approximately 20 min in each case Each plot is quite similar, which confirms that (in the absence of the XG polymer) the SDS concentration had only a minimal effect on the stability of the CO2 microbubbles Figure 2.8 also plots K as a function of the SDS

more stable dispersions, and the enhanced stability observed at higher SDS concentrations can be attributed to the presence of a greater number of surfactant molecules at the bubble surfaces, which in turn strengthened the microbubble shells and provided good surface elasticity (Yan et al., 2005) These effects delay the liquid drainage and reduced bubble coalescence as a result of greater electrostatic repulsion between the microbubbles (Jauregi

et al., 1997)

Figure 2.7 Experimental photograph of CO2 microbubbles drainage with SDS ( 3g/L)

and XG (0 g/L)

Figure 2.8 Effect of SDS concentration on the stability of CO2 microbubbles (with 0 g/L

XG)

Figure 2.9 plots the drainage data over time for dispersions prepared using various

XG polymer concentrations with an SDS concentration of 3 g/L It is evident from these plots that the XG polymer concentration had a significant effect and that higher XG polymer concentrations improved stability, which means that the drainage rate constant was inversely proportional to the XG polymer concentration Specifically, the K value

dispersions The polymer would be expected to raise the viscosity of the base solution while inhibiting gas diffusion from the core to the bulk liquid, which would consequently stabilize the microbubbles (Hosseini-Kaldozakh et al., 2019) These results are consistent with earlier studies (Pasdar et al., 2018c; Tabzar et al., 2020) examining the effects of polymers on CGA stability, which demonstrated improvements in stability at higher polymer concentrations

Figure 2.9 Effect of XG concentration on the stability of CO2 microbubbles (with 3 g/L

SDS)