i TECHNICAL AND ECONOMIC EVALUATION OF NANOFLUID- ALTERNATING-BRINE FLOODING FOR ENHANCED OIL RECOVERY IN NIGER DELTA RESERVOIRS Yetunde Aderonke OMOTOSHO UNIV ERSIT Y O F IB ADAN L IB RARY i TECHNICAL AND ECONOMIC EVALUATION OF NANOFLUID- ALTERNATING-BRINE FLOODING FOR ENHANCED OIL RECOVERY IN NIGER DELTA RESERVOIRS BY Yetunde Aderonke OMOTOSHO B.Sc. Petroleum Engineering, M.Sc. Petroleum Engineering (Ibadan) MATRIC NO: 117542 A Thesis in the Department of Petroleum Engineering, Submitted to the Faculty of Technology in partial of fulfilment of the requirement for the Degree of DOCTOR OF PHILOSOPHY of the UNIVERSITY OF IBADAN FEBRUARY, 2022 UNIV ERSIT Y O F IB ADAN L IB RARY ii CERTIFICATION I certify that this work was carried out by Mrs Yetunde Aderonke OMOTOSHO in the Department of Petroleum Engineering, Faculty of Technology, University of Ibadan. ……………………………………………. (Supervisor) O. A. FALODE B.Sc. (Jos), M. Sc., Ph. D. (Ibadan) Professor of Petroleum Engineering, Petroleum Engineering Department, University of Ibadan, Nigeria UNIV ERSIT Y O F IB ADAN L IB RARY iii DEDICATION This thesis is dedicated to all female researchers who juggle work, career and family life with unwavering determination to fulfil their dreams. UNIV ERSIT Y O F IB ADAN L IB RARY iv ACKNOWLEDGEMENTS I am eternally indebted to the Almighty God for keeping me through the thick and thin of this research work and for giving me great and wonderful by-fruits in the course of the programme. I am in awe of Him for the success of it all. I would like to acknowledge my supervisor, Prof. O.A. Falode, who has been fathering and mentoring me right from my undergraduate days since 2003. Thank you for believing in me. I want to unreservedly express my gratitude for your constant support and guidance in the course of this study. Ab initio, your hands were full but you still made room to supervise me. Your impact in my life remains indelible and undebatable. I want to appreciate Dr Lateef Akanji of the University of Aberdeen, who warmly received me during my visit to the University of Aberdeen to carry out the analyses pertaining to this work. Thank you for always provoking me to excellence. I am especially thankful to a father and mentor, Dr Lateef Akinpelu, whom I had been understudying since my undergraduate days. I never knew I would slide into your shoes after your retirement. It was not easy though, but by divine arrangement, it became possible. Thank you for constantly showing me the way of kindness, empathy and dedication. My immense gratitude goes to Dr Olufemi Omoniyi, for his financial support. I am greatly appreciative. I would like to acknowledge and appreciate the academic staff members of Petroleum Engineering Department, UI. I would like to thank Prof. Isehunwa for his fatherly counsel and guidance in the course of the programme; Dr Oluwatoyin Akinsete, a dedicated postgraduate coordinator, for his unrelenting effort and exceptional administrative skill in making the thesis flow frictionlessly from one level to the other; Dr Julius Alawode, for your advisory tips while putting the work together; Dr Princess Nwankwo, for making me to realise that it is possible to juggle all together and still come out shining; Dr Sarah Akintola and Engr Seyi Orisamika, for their moral support. I am also grateful to the technical staff members of the department viz., Mr Adebola Adebanjo for providing UNIV ERSIT Y O F IB ADAN L IB RARY v guidance during the preliminary stage of the work; Mr Adeniyi Adenuga and Mr Ope Oni for sacrificing their time and effort and giving helpful tips during part of my bench work. My profound gratitude goes to the entire staff members of the Department of Petroleum Engineering, Covenant, University, Otta, for accepting and supporting me during part of my bench work at their reservoir engineering laboratory. I am specifically grateful to the head of department, Prof. O.D. Orodu; the head of the laboratory, Mr Daramola as well as Mr Temilola Ojo, who had to do several sleepless nights in the laboratory to ensure the experiments went smoothly. My gratitude goes to members of staff of Centre for Petroleum, Energy Economics and Law, now Department of Mineral, Petroleum, Energy Economics and Law, for their support while working as a member of staff of the Centre. I am grateful to my spiritual and academic friends and sisters, Nkechi Oranye, Dr Rita Onolemhemhen, Dr Olamide Mustapha, Dr Nike Oluleye, Mrs Adeola Oremule, Mrs Yewande Ayimode, Mrs Olutola Olatunji, Temilade Shodipe, Taiwo and Kehinde Shodipe, Tomiwa Shodipe, Mrs Bukola Olanisebe and the host of others whom time and space will not permit me to list. I will never forget the impact of my academic brother, Dr Akinsanoye, who appeared on the scene at the tail of this research and gave me ideas on how to improve on my write-up. I want to appreciate my Pastors, Pastor (Dr) Ayo and Ibidun Olude for their moral and spiritual support in the course of this journey. Thank you for your constant word of encouragement, parental counsel and prayers. They are way beyond what words can convey. Your reward remains untainted. I also want to appreciate my parents, Mr and Mrs Agbolade Oshinibosi and Mr and Mrs James Omotosho for their parental, moral and spiritual support during this research voyage. Thank you very much for your parental advice and prayers. May you witness greater celebrations of your children in life. UNIV ERSIT Y O F IB ADAN L IB RARY vi I want to unreservedly appreciate my brother, Ajibola Oshinibosi, for always provoking me to excellence and for sponsoring part of the research work; my sisters, Motunrayo Akinboye and Odunayo Oshinibosi for being a great source of encouragement I greatly indebted to my Pastor and husband, Dr Oladipo Olufemi Omotosho, who has been a great pillar of financial, moral and physical support in the course of this research. I acknowledge the constant help you enthusiastically give when the need arises, especially with the children. No doubt the children are more used to you. Thank you for all the prayers, words of encouragement and for seeing that glorious future in our union ahead of the nuptial knot. To my children, Ifeoluwa and Inioluwa Omotosho, both of you are fruits of this Ph.D. journey. It has been so exciting mothering you both. You babies are full of inspiration. UNIV ERSIT Y O F IB ADAN L IB RARY vii ABSTRACT Nanofluid flooding in the petroleum industry has generated growing interest because of its potential to greatly improve oil recovery. However, studies have reported that injection of nanofluid could lead to impaired permeability due to adsorption of nanoparticles on reservoir rocks thereby incurring high costs. The use of single Nanofluid Flooding (NF) has not appreciably reduced permeability impairment. This study was therefore, designed to investigate the technical and economic viability of Nanofluid-Alternating-Brine Flooding (NABF) for enhanced oil recovery in Niger Delta reservoirs. Eight sandstone core samples obtained from Niger Delta, were characterised for porosity and permeability using Helium-Porosimeter and Permeameter, respectively. Densities and viscosities of crude oil samples and brine (Salinity: 32.2g/L) were determined using pycnometer and viscometer, respectively. Core samples were initially saturated with brine and drained with crude oil, to determine the initial Water Saturation (SWi). Silica nanoparticles of size: 20-70 nm, were dispersed in brine at concentrations ranging from 0.01 to 3.00 wt%. Interfacial Tensions (IFT) between oil and nanofluids were measured. Brine Flooding (BF) of core samples was conducted at 2.00 cm3/min. The Optimum Concentration (OC) and Optimum Injection Rate (OIR) during NF were determined by injecting each nanofluid concentration at 0.50, 1.00, 2.00 and 3.00 cm3/min. The NABF was carried out at OC and OIR. The Oil Recovery Factors (ORF) for all experiments were computed using material balance. The images of pre-flooded and post-flooded core samples were obtained using Scanning Electron Microscope. Nanoskin factors (Sn) were determined for NF and NABF and compared with the analytical model developed from Darcy’s equation. The ORF obtained were upscaled for field application and evaluated for Threshold Oil Price (TOP). Risk analysis with varying ORF, Capital Expenditure (CAPEX) and Operating Expenses (OPEX) was carried out using a commercial software. Data were analysed using ANOVA at 𝛼0.05. Porosity and liquid permeability for the samples were 17.0-30.0% and 1.1x10-8 -1.6x10-8 cm2 (1104.9-1584.0 md), respectively. The densities of crude oil and brine were 0.88 and 1.02 g/cm3, while their viscosities were 3.0x10-4 kgms-2 (3.0 cp) and 1.0x10-4 kgms-2 (1.0 UNIV ERSIT Y O F IB ADAN L IB RARY viii cp), respectively. The SWi were 11.0-18.4%. The IFT were 1.9x10-2 -2.3x10-2 N/m (18.5- 23.0 dynes/cm) while the OC and OIR for NF were 2.00 wt % and 2.00 cm3/min, respectively. The ORF for BF, NF and NABF were 68.9-73.1, 63.8-66.2 and 83.8-86.2%, respectively. The pre-flooded cores had evenly distributed grain matrices void of external particles while permeability impairment was observed for NF. Permeability impairment reversal was observed during NABF. The predictive model for Sn agreed with the experimental result. Economic analysis revealed that for unit CAPEX (N13,985.56/bbl; $34.00/bbl) and OPEX (N1,867.48/bbl; $4.54/bbl), at discount rate of 10.0%, TOP was N20,196.79/bbl ($49.10/bbl). Risk analysis on profitability showed that TOP for proved, probable and possible ORF were 33,400.81, 19,197.24 and N12,545.87/bbl (81.20, 46.67 and $30.50/bbl), respectively. The order of impact of the economic variables on profitability was ORF>CAPEX>OPEX. Improved oil recovery in Niger Delta reservoirs was achieved using nano-alternating-brine flooding with minimal permeability impairment. The method is also profitable within the stipulated oil price regime. Keywords: Nano-enhanced oil recovery, Nanofluid flooding, Nanofluid, Nanoskin, permeability, Threshold oil price Word count: 498 UNIV ERSIT Y O F IB ADAN L IB RARY ix TABLE OF CONTENTS CONTENT PAGES TITLE i CERTIFICATION ii DEDICATION iii ACKNOWLEDGEMENTS iiv ABSTRACT vii TABLE OF CONTENTS iix LIST OF FIGURES xiiiii LIST OF TABLES xv LIST OF ABBREVIATIONS xvi CHAPTER ONE 1 INTRODUCTION 1 1.1 Background to the Study 1 1.2 Statement of the Research Problem 8 1.3 Significance of the Study 11 1.4 Research Aim and Objectives 11 1.5 Scope of the Study 13 1.6 The Study Area 13 CHAPTER TWO 19 LITERATURE REVIEW 19 2.1 Nanotechnology as a Possible EOR Technique 19 2.2 Principles of Nano-enhanced Oil Recovery 20 2.3 Displacement Mechanisms of Nanofluid Flooding 21 2.3.1 Structural Disjoining Pressure 21 2.3.2 Displacement by Interfacial Tension Reduction and Wettability 23 Alteration 2.4 Synergy of EOR Methods for Oil Recovery Optimisation 27 2.5 Transport of Fluids in Porous Media 28 2.5.1 Transport of Nanofluids in Porous Media 28 2.6 Preparation of Nanofluids 30 2.7 Retention of Nanoparticles in Porous Media 31 UNIV ERSIT Y O F IB ADAN L IB RARY x 2.8 Economic Evaluation of EOR Processes 31 CHAPTER THREE 35 METHODOLOGY 35 3.1 Materials 35 3.2 Preliminary Experimental Set Up 35 3.2.1 Preparation of Core Samples 35 3.2.2 Characterisation of Core Samples 38 3.2.3 Formulation and Characterisation of Injection Brine 40 3.2.4 Characterisation of Crude Oil 40 3.2.5 Formulation and Characterisation of Different Nanofluid Concentrations 40 3.2.6 Measurement of Interfacial Tension between Nanofluid and Crude Oil 41 3.2.7 Vacuum Saturation with Brine 43 3.3 Coreflood Set Up 45 3.3.1 Primary Drainage Process (Oil Flooding) 48 3.3.2 Secondary Brine Flooding (BF) 48 3.3.3 Nanofluid Flooding (NF) with Changing Concentration and Injection Rate 49 3.3.4 Nanofluid Flooding (NF) with Optimal Concentration and Injection Rate 50 3.3.5 Nanofluid-Alternating-Brine Flooding (NABF) 50 3.4 Scanning Electron Microscopy 50 3.5 Model Development for Nanoskin Factor 51 3.5.1 Physical Description 51 3.5.2 Simplifying Assumptions 51 3.5.3 Mathematical Model 52 3.5.4 Governing Equations 52 3.5.5 Model Development 54 3.5.6 Solution Method 56 3.5.7 Application 56 3.6 Technical Evaluation of Nanofluid-Alternating-Brine Flooding for a Case in Niger Niger Delta 57 3.6.1 Upscaling of Experimental Results 57 3.7 Economic Evaluation of Nanofluid-Alternating-Brine Flooding for a Case Re servoi Reservoir in Niger Delta 59 UNIV ERSIT Y O F IB ADAN L IB RARY xi 3.7.1 Deterministic Approach 59 3.7.2 Probabilistic Approach 60 CHAPTER FOUR 62 RESULTS AND DISCUSSION 62 4.1 Characterisation of Core Samples 62 4.1.1 Weight, Volume and Density Measurements 62 4.1.2 Porosity 64 4.1.3 Permeability 69 4.1.4 Summary of Sample Characterisation 71 4.2 Formulation and Characterisation of Injection Brine 73 4.3 Characterisation of Crude Oil Sample 73 4.4 Formulation and Characterisation of Different Nanofluid Concentrations 73 4.5 Interfacial Tension 75 4.6 Vacuum Saturation Results for Initial Water Saturation 78 4.7 Coreflood Experiments 80 4.7.1 Primary Drainage Process (Oil Flooding) 80 4.7.2 Secondary Brine Flooding 80 4.7.3 Nanofluid Flooding with Changing Concentration and Injection Rate 80 4.7.4 Oil Recovery for Brine Flooding (Control) 88 4.8 Nanofluid Flooding with Optimum Concentration and Injection Rate 90 4.9 Nanofluid-Alternating-Brine Flooding (NABF) 90 4.10 Scanning Electron Microscopy Results for BF, NF and NABF Experiments 94 4.10.1 Brine-Flooded Core 94 4.10.2 Nanofluid Flooded Core 96 4.10.3 NAB Flooded Core 98 4.11 Validation of the Model 100 4.12 Technical Evaluation 103 4.12.1 Upscalng of NABF Input and Output Parameters 103 4.13 Economic Evaluation 107 4.13.1 Deterministic Approach 107 4.13.2 Probabilistic Approach 111 CHAPTER FIVE 128 UNIV ERSIT Y O F IB ADAN L IB RARY xii SUMMARY, CONCLUSION AND RECOMMENDATIONS 128 5.1 Summary 128 5.2 Conclusion 129 5.3 Recommendations 130 5.4 Contributions to Knowledge 130 5.5 Suggestions For Further Study 131 REFERENCES 132 APPENDICES 141 APPENDIX A 141 APPENDIX B 145 APPENDIX C 148 APPENDIX D 149 UNIV ERSIT Y O F IB ADAN L IB RARY xiii LIST OF FIGURES Fig. 1.1: Categorisation of oil recovery mechanisms (After Carcoana, 1992) 2 Fig. 1.2: Categories of EOR Technologies (Sun et al., 2017). 4 Fig. 1.3: Nanotechnology Applications in Different Areas (After Nasrollahzadeh, et. al., 2019) 6 Fig. 1.4: Pictorial Representation of the Nanoskin Formation at the Pore Throat of a Reservoir Rock (Modified after El-Diasty, 2015) 10 Fig. 1.5: Regional stratigraphy of the Niger Delta showing different formations (After Ozumba, 2013) 16 Fig. 2.1: Disjoining Pressure in the Wedge Structure (Wasan and Nikolov, 2003) 22 Fig. 2.2: Role of nanoparticles in wettability alteration 25 Fig. 3.1: Soxhlet Extraction Apparatus 37 Fig. 3.2: Ring Tensiometer 42 Fig. 3.3: Vacuum Saturator 44 Fig. 3.4: Relative permeability tester used for coreflooding 46 Fig. 3.5: Schematic diagram of the coreflood set up 47 Fig. 4.1: Plot of Helium and Liquid Saturation Porosities 68 Fig. 4.2: Liquid Permeability Plot for the Core Samples 70 Fig. 4.3: Plot of Interfacial tension (IFT) against Nanofluid Concentration (CNF) 77 Fig. 4.4: Recovery Factor against Nanofluid Concentration 85 Fig. 4.5: Nanoflooding Recovery Factor against Injection Rate 87 Fig. 4.6: Oil recovery performance against injected pore volume of fluid during BF, NF and NABF. 92 Fig. 4.7(a): Brine flooded sample to residual oil saturation 95 Fig. 4.7 (b): SEM image of sample Y5 (brine flooded sample) 95 Fig. 4.8 (a): Nanofluid flooded core to residual oil saturation 97 Fig. 4.8 (b): SEM image of sample Y6 97 Fig. 4.9 (a): NAB flooded core to residual oil saturation 99 Fig. 4.9 (b): SEM image of sample Y3 99 Fig. 4.10 (a): Distribution of Input Parameter (ORFNABF) for NpNABF 113 Fig. 4.10 (b): Distribution of Input Parameter (ORFBNABF) for NpNABF 114 UNIV ERSIT Y O F IB ADAN L IB RARY file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485104 file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485105 file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485106 file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485107 file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485108 file:///C:/Users/lenovo%20pc/Documents/Yetunde's%20folder/PhD%20thesis/Proposal%20Write-ups%20(Nanotechnology)/Write-ups/2021/Yetunde%20Omotosho%20Phd%20Thesis%20(turnitin6).docx%23_Toc93485109 xiv Fig. 4.10 (c): Distribution of Input Parameter (A) for NpNABF 115 Fig. 4.10 (d): Distribution of Input Parameter (h) for 116 Fig. 4.10 (e): Distribution of Input Parameter (𝝓) for NpNABF 117 Fig. 4.10 (f): Distribution of Input Parameter Sw for NpNABF 118 Fig. 4.11: Cumulative Ascending Probability Curve for Oil Recovery after NABF 119 Fig. 4.12: Normal Distribution showing Variability in of Materials, $ 122 Fig. 4.13: Normal Distribution showing Variability in Injection OPEX/depth ($/m) 123 Fig. 4.14: Normal Distribution showing variability in Total Injection CAPEX, MM$ 124 Fig. 4.15: Cumulative Asecnding Probability Curve for P10, P50 and P90 Threshold Oil Price, $/bbl. 125 UNIV ERSIT Y O F IB ADAN L IB RARY xv LIST OF TABLES Table 4.1: Weight, Dimension, Volume and Density Measurements 63 Table 4.2(a): Preliminary Data for the Calculation of Porosities of the Core Samples 65 Table 4.2(b): Porosities of the Core Samples 66 Table 4.3: Summary Table for Sample Characterisation 72 Table 4.4: Summary of Fluid Characterisation 74 Table 4.5: IFT Measurements 76 Table 4.6: Vacuum Saturation Results 79 Table 4.7: Drainage and Brine Flooding Experiment Results 82 Table 4.8: Nanofluid Flooding Results 83 Table 4.9: Residual Oil Saturation and Displacement Efficiency 86 Table 4.10: Initial Fluid Saturations of the Core Samples Y5, Y6 And Y8 89 Table 4.11: Comparison of oil recovery efficiencies and residual oil saturations 93 Table 4.12: Average Properties of the Core 104 Table 4.13: Estimated Incremental Oil Recovery from NABF 105 Table 4.14: Estimation of the Reservoir Pore Volume of Nanofluid Injected 106 Table 4.15: Nanofluid Cost 108 Table 4.16: Injection CAPEX and OPEX 109 Table 4.17: Total Cost and Threshold Oil Price Per Barrel 110 Table 4.18: Input Assumptions and Results for Probabilistic Estimation of Oil Recovery after NABF 112 Table 4.19: Summary of Input Variables for the Threshold Oil Price Determination 121 UNIV ERSIT Y O F IB ADAN L IB RARY xvi LIST OF ABBREVIATIONS BF Brine Flooding EOR Enhanced Oil Recovery IFT Inerfacial Tension CN Cost of Nanofluid Cnf Nanofliluid Concentration, wt % knf Nanofluid permeabilty, md ko End point permeability of oil, md kw End point permeability of brine, md LHP Lipophobic Hydrophilic Polysilion NABF Nanofluid-Alternating-Brine Flooding NF Nanofluid Flooding ORF Oil Recovery Factor, fraction or per cent POT Threshold oil Price, $/bbl PV Pore Volume RPT Relative Permeability Tester Soi Initial Oil Saturation, fraction or per cent Sn Nanoskin factor Sor Residual Oil Saturation, fraction or per cent SEM Scanning Electron Microscopy/Microscope Swi Initial Water Saturation, per cent qinj Nanofluid injection rate, cm3/min qo Oil flow rate, cm3 qw Water flow rate, cm3 μnf Nanofluid viscosity, cp µo Oil viscosity, cp µw Brine viscosity, cp Wnp Weight of nanoparticle, g MMbbl Million Barrels UNIV ERSIT Y O F IB ADAN L IB RARY 1 CHAPTER ONE INTRODUCTION 1.1 Background to the Study Majority of the conventional oil fields in the Niger Delta basin are already or almost at their peak production phase, which is a precursor to the declining production phase. The current task then becomes optimising recovery of hydrocarbon from the conventional fields and delaying their time to abandonment in a cost effective manner as well as exploiting the unconventional reservoirs. Additional reserves are usually achieved by the development of new discoveries and improvement of oil recoveries from existing reservoirs. Oil recoveries from existing reservoirs may be achieved through three major phases. The first phase is the natural recovery phase also known as the primary recovery phase. In this phase, oil recovery results from the utilisation of the natural or inherent energy within the reservoir. The mechanisms involved in this phase include rock and liquid expansion, solution gas drive, gas cap drive, gravity drainage or a combination of two or more of the aforementioned mechanisms. Only 10-15% of the original oil in place (OOIP) is recoverable because some of the hydrocarbons are normally trapped within the porous media during this phase (Carcoana, 1992). Trapping occurs as a result of capillary forces which are influenced by interfacial forces and wettability. The next phase is the secondary oil recovery phase which uses conventional methods (waterflooding and gas injection) to achieve increased recovery and recovery may be up to about 50% during this phase. The residual oil after secondary recovery which remains largely as isolated, trapped droplets (ganglia) are usually confined within the pores or films around the rock particles depending on rock wettability. Mobilisation of the residual oil after the exhaustion of the secondary energy methods then becomes the target of enhanced oil recovery (EOR) (Ahmed, 2010). Figure 1.1 depicts the phases of oil recovery in reservoirs. UNIV ERSIT Y O F IB ADAN L IB RARY 2 Fig. 1.1: Categorisation of oil recovery mechanisms (After Carcoana, 1992) 10-15% RF Cumulative RF UNIV ERSIT Y O F IB ADAN L IB RARY 3 EOR involves the addition of supplementary energy from heat, miscible gases, chemicals or other agents, to a depleted reservoir or heavy oil reservoirs to achieve incremental oil recovery. EOR methods could be classified into thermal, miscible gas, chemical, microbial, electromagnetic, acoustic methods depending on the agents used to achieve additional oil recovery (Shafiai and Gohari, 2020). Displacement of residual oil during EOR processes may be microscopic; which is at pore scale or macroscopic at the volumetric scale. The oil recovery phases or methods need not be applied in chronological order, especially in the case of heavy oil reservoirs where thermal injection is usually the only feasible method of recovery. As resources are becoming more expensive and risky to explore in new terrains, enhanced oil recovery techniques have increasingly become the new focal areas. EOR techniques achieve recovery by improving mobility ratio between injected and in- place fluid, hence improving sweep efficiency; and eliminating or reducing capillary and interfacial forces thus improving displacement efficiency (Latil et al., 1980). Phenomena such as oil swelling, wettability modifications, emulsification of oil and oil viscosity reduction also account for these improved recoveries. The conventional EOR methods such as thermal, chemical and miscible gas methods have seen limited field applications because of certain challenges surrounding their deployments in reality. Most of these limitations centre around high costs, loss of injection fluids, unfavourable mobility ratios and environmental risks. Some of these limitations of conventional EOR methods are summarised in Figure 1.2. UNIV ERSIT Y O F IB ADAN L IB RARY 4 Fig. 1.2: Categories of EOR Technologies and their Challenges (Sun et al., 2017). UNIV ERSIT Y O F IB ADAN L IB RARY 5 Nanotechnology has the potential to mitigate some of the current issues in the oil and gas industry. It has the ability to change EOR methods and processes. Introduced by Feyman in 1959, nanotechnology is precisely an idea of manipulating molecules and atoms on a nanoscale (10-9). The smaller these particles become, the less they are affected by gravity and then, van der Waals forces and surface tension become more prominent. Nanotechnology has been applied and has been proposed for application in many areas. Some of these areas include biomedicine, electronics, food and agriculture, manufacturing industry, to mention a few. Figure 1.3 shows some of the areas of application of nanotechnology. It has been applied to petroleum drilling and cementing operations to a reasonable extent. Nevertheless, it has yet to be widely used in oil and gas exploration and production technology. At least one dimension of a nanoparticle is between 1 and 100 nm. Fullerenes (C60), graphene, carbon nanotubes, quantum dots, polymeric metals, and metallic oxides are some examples of nanoparticles. At the nano-scale, the surface and quantum mechanics phenomena become important. As a result, nanoparticle behaviour differs significantly from that of their bulk counterparts (Kapusta et al., 2011). UNIV ERSIT Y O F IB ADAN L IB RARY 6 Fig. 1.3: Nanotechnology Applications in Different Areas (After Nasrollahzadeh et. al., 2019) Petroleum: Drilling operation, waste water treatment UNIV ERSIT Y O F IB ADAN L IB RARY https://www.sciencedirect.com/science/article/abs/pii/B9780128135860000043#! 7 Nanoparticles have the unique and advantageous ability to alter some reservoir rock-fluid interaction properties such as wettability and fluid-fluid interaction properties such as interfacial tension (IFT). Apart from those, their small size allows them to pass through porous media with relative ease. They have the ability to allow for the release of chemicals at certain locations in the formation which can create or break emulsions. In addition, they can improve fluid properties such as viscosity. Laboratory testing shows that small concentrations (<1 wt %) of nanoparticles can raise the viscosity of water-based fluids without significantly changing their density and could offer potential improvement in the efficiency of oil recovery (Kapusta et al., 2011) A dispersion where solid particles in nano-sizes are carried in suspension in a base fluid or heat-transfer fluid, usually water, brine, ethanol, oil, ethylene glycol or gas, is referred to as nanofluid. Recent research has demonstrated that nanoparticles distributed in fluids can mobilise trapped oil in porous formations, and that they can be combined with surfactants/polymers to improve their effects and range of mobilization (Negin et al., 2016; Rostami, 2019). Pore channels in rocks are larger than nanoparticles indicating their ability to penetrate through hydrocarbon formations without much retention (Li et al., 2013). Nanofluids have been offered as a low-cost, ecologically friendly alternative to surfactants and polymers, which are both expensive and possibly harmful to the environment (Dahle, 2014). An inorganic ceramic material composed of Silica dioxide (SiO2) or Silica is normally used as a nanoparticle in most EOR nanofluid as seen in many studies (Ju et al., 2006; Li et al., 2013; Dahle, 2014). Silica nanoparticles are commonly used because they are cheap and easily accessible. They also provide other advantages because their surface can easily be coated to alter their chemistry for more favourable application; rheological, heat and mechanical properties (surface chemistry, shape and size) can be adjusted for specific objective as well as improvement in sedimentation stability because surface forces counteract gravity (Miranda et al., 2012). UNIV ERSIT Y O F IB ADAN L IB RARY 8 Other types of nanoparticles which have been investigated for EOR process include the oxides of Zinc, Aluminium, Magnesium, Tin, Zirconium, Iron and Nickel. Aluminium, Nickel and Iron oxides were concluded as effective candidates for nano-enhanced oil recovery process. This could be inferred from the improvement in thermal conductivity of the crude oil and viscosity reduction caused by breaking of Carbon-Sulphur bond by Aluminium and increase in viscosity of the carrying fluid (brine in this case) induced by Nickel and Iron; both of which resulted in favourable mobility ratio for the process. Zinc and Magnesium oxide fell into disfavour owing to the resultant permeability damage they caused in the process (Ogolo et al., 2012). Two main nanofluids characteristics make them widely different from other EOR agents. First is their large surface area to volume. In comparison to other EOR agents such as surfactants, a smaller amount of nanoparticles is required to accomplish the same impact (Almahfood and Bai, 2018). Secondly, their quantum effects can affect the optical, electric and magnetic behaviour of that material (Nanowerk, 2016). Nanofluids target improvement of microscopic displacement efficiency since it is concerned with wettability alteration and IFT reduction. It is a chemical EOR technique. 1.2 Statement of the Research Problem Nanoparticle dispersions in injection water (nanofluids) have been shown in studies to boost oil recovery by up to 65% (Hendraningrat et al., 2013a; Li et al., 2013). However, in certain situations, damage to the formation has been observed, thereby incurring high formation remedial costs, with recovery limited to less than 10% (Torsater et al., 2013). The damage is described by a novel concept called nanoskin, as shown in Figure 1.4. Nanoskin is defined as a continuous thin sheet formed as a result of accumulation of nanoparticles at the rock pore surface and pore throat, limiting oil recovery. No appreciable permeability reduction has been achieved from previous attempts of using nanofluid flooding alone, and in fact, the potential of synergism of nanofluid-alternating-water flooding for reversal of permeability impairment is yet to be investigated within the limit of all literature reviewed (Ju et al., 2006, Zhang, 2014, Li et al., 2013) . The nanofluid "slugs" could potentially UNIV ERSIT Y O F IB ADAN L IB RARY 9 mobilise more oil, while the brine can desorb retained particles and prevent major porosity/permeability impairment. Several factors affect nanofluid flooding recovery; these include concentration of nanoparticles, size of nanoparticles, salinity, temperature, wettability of nanoparticles, the rock grain size, the clay content, reservoir permeability, and injection rate. Increase in concentration has proved to improve the displacement efficiency of nanofluids because of the consequent increase in viscosity and spreading of nanoparticles on the surface. However, according to Maghzi et al. (2012) and El-Diasty (2015), a concentration beyond 3.0 wt % has been found to reduce ultimate recovery due to blockage of pores and throats by dispersed nanoparticles. The optimal concentration of nanoparticles for maximum recovery is the one at which the adsorption sites on the pore walls are saturated with nanoparticles. For nanoparticles concentration above the optimal concentration, the effect of permeability reduction is more than that of wettability alteration such that the overall effect is reduction in the recovery factor. With increasing concentration, the rate of nanoparticle retention on the pore surfaces and the pore spaces increases, which causes further reduction in permeability. Consequently, a reduction in the overall recovery efficiency occurs despite the wettability alteration effect, which results from the adsorption of these particles through the porous media. An appropriate combination of concentration and other factors, becomes very key to minimisation of particle retention, potentially resulting in the elimination of nanoskin, leading to improved oil recovery. Defining optimal conditions for these factors therefore becomes imperative. Apart from the combination of nanoparticle size, concentration as well as salinity, less attention has been received for other factors. In addition, one of the major factors that make an EOR method attractive is its unit cost of implementation per barrel of recovered oil which determines the threshold crude oil price per barrel for profitable implementation. To achieve this, the impact of technical and economic factors on implementation of EOR becomes paramount. Limited emphasis has however, been placed on this in previous studies (Ju et al., 2006, Li et al., 2013). UNIV ERSIT Y O F IB ADAN L IB RARY 10 Fig. 1.4: Pictorial Representation of the Nanoskin Formation at the Pore Throat of a Reservoir Rock (Modified after El-Diasty, 2015) UNIV ERSIT Y O F IB ADAN L IB RARY 11 1.3 Significance of the Study Nanofluid flooding is a relatively new chemical EOR method that has proven to be more effective than other types of chemical EOR (Alkali, Polymer, Surfactant flooding), recovering an appreciable amount of oil initially in place. Investigation of nanofluid flooding in Niger Delta reservoirs is still sparsely studied, necessitating wider research for its potential enhancement of oil recovery. Most of the reservoir rocks in the Niger Delta region are water-wet; nanoparticles have the tendency to alter the wettability to neutrally wet, reducing residual oil to critically low saturation and thus, improving recovery efficiency. At relatively predetermined high concentration, nanofluid serves as a mobility control agent and may be employed after waterflooding and later followed by same to achieve maximum recovery efficiency in EOR. This synergism of brine and nanofluid is apparently more environmentally friendly than other flooding processes requiring the use of surfactants and some other toxic chemicals, alongside nanofluid flooding. Nanofluid flooding with silica nanoparticles is relatively cheaper and easy to manage alternative compared to other chemical EOR methods. In addition, the recent continuous plunge and general instability in global oil prices makes the design of threshold oil price for investment in nano-EOR highly imperative. 1.4 Research Aim and Objectives The main goal of the research was to establish the technical and economic viability of nanofluid flooding (NF) alternately with brine flooding (BF) in typical Niger Delta sandstone core samples. The technical and economic viability of silica NF for enhanced oil recovery in Niger Delta has been established by Ajulibe et al. (2018). However, a combination of nanofluid, reservoir rock and fluid properties are responsible for improved recovery efficiency during NF. Critical to the improved recovery efficiency is the ability to prevent or minimise positive nanoskin effect, caused by high retention of nanoparticles at the rock pore throats which ultimately leads to formation damage. If reservoir rock and fluid properties are held as control and two nanofluid conditions, i.e., concentration and injection rate, are varied, possible minimisation of nanoskin effect and consequently, improved oil UNIV ERSIT Y O F IB ADAN L IB RARY 12 recovery efficiency may be achieved. Hence, the overall objective of the study was to boost recovery efficiency using synergism of nanofluid-alternating-brine flooding (NABF) at optimal nanofluid concentration and injection rate and evaluate the economics of NABF in typical Niger Delta reservoirs The specific objectives include: i. To carry out preliminary characterisation of eight core samples and crude oil sample obtained from typical Niger Delta reservoir, ii. To formulate and characterise brine solution, nanofluids of concentrations ranging from 0.01-3.00 wt % using brine as the base fluid and measure the interfacial tension (IFT) between the brine and crude oil sample, in the presence of each nanofluid concentration, iii. To determine initial water (or brine) saturation of core samples using vacuum saturation, iv. To perform coreflood (drainage) experiment to determine initial oil saturations of core samples, v. To investigate oil recovery efficiencies under brine flooding (unsteady state method). This will serve as the control, vi. To investigate and compare recovery efficiencies under NF using a range of concentrations of silica nanofluids at different injection rates, vii. To find the best nanofluid concentration and injection rate for optimal recovery efficiency. viii. To investigate oil recovery efficiency under NABF, ix. To compare the Scanning Electron Microscopy (SEM) of the core samples investigated for BF, NF and NABF, x. To develop an analytical model for estimating nanoskin factor and compare with experimental results, xi. To perform technical evaluation of NABF flooding for a case field from upscaled experimental results and xii. To perform economic evaluation for the estimation of deterministic oil price threshold and risked-oil price threshold for NABF implementation. UNIV ERSIT Y O F IB ADAN L IB RARY 13 1.5 Scope of the Study The scope of the study was on tertiary flooding with silica nanoparticles. The critical factors; namely concentration and injection rate, and mechanisms influencing recovery by flooding with silica nanofluid were studied at laboratory scale. Measurements of fluid petrophysical properties such as rock porosity and permeability, fluid viscosity and API gravity were carried out in the laboratory. Interfacial tension (IFT) tests were done for both the control fluid (brine) and nanofluid. IFT tests served as screening tests to obtain low IFT nanofluids and served as confirmatory tests for the incremental recoveries during NF and NABF. The experimental approach involved materials and specimen preparation, coreflood test. SEM analyses of the states of pore surface and pore geometry prior and post-flooding served as a basis for comparison of brine flooded, nanofluid and nanofluid-alternating-brine flooded core samples. The economic analysis was limited to the definition of threshold oil price, which is the minimum oil price for profitable implementation of NABF. Risk analysis of uncertain technical and economic input variables viz., oil recovery, capital expenditure and operating expenses were carried out to define a degree of confidence for the threshold oil price 1.6 The Study Area The study area was the Niger Delta Agbada Formation where the sandstone core and crude oil samples were obtained. The Niger Delta is a prominent and prolific hydrocarbon producing basin in Nigeria where intensive exploration and production activities have been going on since early 1960's as a result of the discovery of commercial oil in Oloibiri-1 well in 1956. The Niger Delta Basin is situated in the West African continental margin at the apex of the Gulf of Guinea, between latitudes 300N West and 600N and longitudes 500E and 800E (Reijers et al., 1996). The Niger Delta is bordered on the northwest by a subsurface extension of the West African Shield, the Benin Flank. The eastern edge of the basin overlaps the Calabar Flank to the south of the Oban Masif (Murat, 1972). Well sections through the Niger Delta generally display three vertical lithostratigraphic subdivisions: an upper delta top facies; a middle delta front lithofacies; and a lower pro-delta lithofacies (Reijers et al., 1996). These lithostratigraphic units correspond respectively with the Benin UNIV ERSIT Y O F IB ADAN L IB RARY 14 Formation (Oligocene-Recent), Agbada Formation (Eocene-Recent) and Akata Formation (Paleocene-Recent) according to Short and Stauble (1967). The Akata Formation is the primary source rock and is composed mainly of marine shales, with sandy and silty beds which are thought to have been laid down as turbidites and continental slope channel fills. It is estimated that the formation is up to 7,000 metres thick (Doust and Omatsola, 1990). The Agbada Formation is the major petroleum-bearing unit in the Niger Delta. The formation consists mostly of shore face and channel sands with minor shales in the upper part, and alternation of sands and shales in equal proportion in the lower part. The interbedded marine shale of the lowermost Agbada Formation is possibly a contributor to the primary source rock of the Niger Delta. The thickness of the formation is over 3,700 metres. The Niger Delta contains one petroleum system referred to as the tertiary Niger Delta (Agbada-Akata) Petroleum System (Ekweozor and Daukoru, 1994; Kulke, 1995). The Benin Formation is made up of continental sands and gravels and is about 280 meters thick, but can be up to 2,100 metres thick in the area of maximum subsidence (Whiteman, 1982). The Delta is divided into structural and stratigraphic belts called depobelts by major growth-fault trends that prograde from northwest to southeast. Hydrocarbons can be found in all of the Niger Delta's depobelts, in high-quality sandstone reservoirs that are part of the major deltaic sequence (also known as the 'paralic sequence'). Each of these depobelts has a deltaic sequence that is distinct in age and characterizes consecutive phases in the delta's history. The majority of the bigger accumulations occur in roll-over anticlines in the hanging-walls of growth faults, where they might be trapped in dip or thrust. The area contains as much as 34.5 billion barrels (5.5x109 m3) of recoverable oil and 94 trillion cubic feet (2.7x109 m3) of proved natural gas reserves and up to 600 trillion cubic feet of possible reserves (1.7 x 1013 m3) of associated and unassociated gas since more than 60 years of discovery. The oil and gas fields contain thousands of individual reservoirs, most of which are sandstone pockets trapped within oil-rich shale strata. The Niger Delta region has as many as 574 fields discovered (481 oil and 93 natural gas fields). The Success rate of hitting oil in the past has been as high as 45% (Akintola et al., 2015). Most fields are small, ranging up to 315 Million barrels (50 × 106 m3), though several larger fields have UNIV ERSIT Y O F IB ADAN L IB RARY 15 recoverable reserves in excess of 503 Million barrels (80 × 106 m3). The hydrocarbons are found in multiple pay sands with relatively short columns, and adjacent fault blocks usually have isolated accumulations (Doust, 1990). Figure 1.5 reveals the regional stratigraphy of the Niger Delta cutting across several formations. UNIV ERSIT Y O F IB ADAN L IB RARY 16 Fig. 1.5: Regional stratigraphy of the Niger Delta showing different formations (After Ozumba, 2013. UNIV ERSIT Y O F IB ADAN L IB RARY 17 The Agbada Formation comprises principally sandstones and unconsolidated sands from which most petroleum in the Niger Delta is produced. The depositional environment and depth of compaction constitute the predominant factors that formed the characteristics of the Agbada Formation. The reservoirs range in thickness from less than 15 meters to more than 45 meters in thickness (Evamy et al., 1978) and are Eocene to Pliocene in age. The thicker reservoirs are most likely layered channel composite bodies (Doust and Omatsola, 1990). The principal Niger Delta reservoirs, according to Edwards and Santogrossi (1990), are Miocene paralic sandstones with 40 percent porosity, 2 darcys permeability, and a thickness of 100 meters. Growth faults in the down-thrown block, where reservoir thickness is greatest, are the most powerful controlling factor for lateral variation in reservoir thickness (Weber and Daukoru, 1975). The reservoir sandstone has a wide range of grain sizes, with fluvial sandstones being coarser than their delta front counterparts; point bars fine upward, while barrier bars have the best grain sorting. Much of this sandstone is nearly unconsolidated, with some argillo-silicic cement as a minor component (Kulke, 1995). Because of the young age of the sediment and the coolness of the delta complex, porosity decreases with depth. Niger Delta fields are composed of mainly structural traps although few stratigraphic traps have been discovered. The development of the structural traps is associated with synsedimentary deformation of the Agbami paralic sequence (Evamy et al., 1978). These traps stretch form the north (older depobelts) to south (younger depobelts); a reflection of progressive instability of the shale under compaction and over pressure. Multiple growth faults, antithetic faults and collapsed crest structures including simple rollover structures and clay filled channels, constitute some of the structural trapping elements (Doust and Omatsola, 1990) in the province. On the flanks of the delta, stratigraphic traps are likely as important as structural traps (Beka and Oti, 1995). In this region, pockets of sandstone occur between diapiric structures. Towards the delta toe (base of distal slope), this alternating sequence of sandstone and shale gradually grades to essentially sandstone. The primary seal rock in the Niger Delta is the interbedded shale within the Agbada Formation. The shale provides three types of seals—clay smears along faults, interbedded sealing units against which reservoir sands are juxtaposed due to faulting, and vertical seals UNIV ERSIT Y O F IB ADAN L IB RARY 18 on the flanks of the delta, major erosional events of early to middle Miocene age formed canyons that are now clay-filled (Figure 1.5). These clays form the top seals for some important offshore fields (Doust and Omatsola, 1990). For the purpose of the research, a field in an onshore mature lease located in the northern Niger Delta was used as a case study. Oil and gas were first discovered in the lease in 1967 and production came on stream in 1972. The licence covers an area of approximately 358 km² (88,464 acres). It comprises seven producing fields and two single well discoveries that had been produced in the past but are currently shut-in. The total number of production and injection wells in the lease are 94 of which 24 are currently producing. The case study field has 12 wells with 6 currently producing. It is also currently being waterflooded to maintain pressure. The produced water-oil-ratio (WOR) is relatively high, having reached 5 stb/stb. For the case study field, we assumed a direct line drive pattern; i.e. one injection well to one producer. Current oil production rate is 5,000 bbls per day with about 50% original oil in- place (OOIP) estimated to be unrecoverable after waterflooding. Primary recovery factor before waterflooding was 30%. Estimated additional recovery factor from waterflooding was 20%. Average reservoir thickness was about 23 metres and well spacing was 121, 406 sq. metres (30 acres). UNIV ERSIT Y O F IB ADAN L IB RARY 19 CHAPTER TWO LITERATURE REVIEW Manipulating a matter on an atomic or a molecular scale is referred to as Nanotechnology. It is defined as the construction of functional materials, device and systems by controlling matter at the nano-scale level (one-billionth meter), and the exploitation of their novel properties and phenomena that emerge at that scale. Reportedly, one nanometer-scale polysilicon material could change the wettability of porous surfaces of sandstone and consequently affect the flow of water and oil when injecting the suspension of the nanoparticles in an oil reservoir (Ju and Dai, 2002). 2.1 Nanotechnology as a Possible EOR Technique By producing better materials, nanoparticles have been applied to upstream petroleum operations. They have been utilised as tracers, and nanoparticle dispersions have been used to treat asphaltene, scale, and paraffin deposition issues. The development of new forms of smart fluids is another growing application of nanotechnology in the petroleum business. Surfactants/polymers, microemulsions, colloidal dispersion gels, and other nano-formulas utilised in drilling, oil recovery, and other applications are among these innovative nano- formulas (Dahle, 2014). Cocuza et al. (2011) provided an overview of nanotechnology applications and critically highlighted the potential benefits that can come from transposing the same or adapted solutions to the oil industry. For enhanced or improved oil recovery purpose, the new- generation nano-agents should both affect the properties of the injected fluid, in terms of viscosity, density, thermal conductivity and specific heat, and modify the fluid-rock interaction properties, for example in terms of wettability. These nano-agents, according to reports, include two different types of polysilicon nanoparticles in oil fields to improve oil UNIV ERSIT Y O F IB ADAN L IB RARY 20 recovery and enhance water injection. These are Lipophobic Hydrophilic Polysilicon (LHP) and Hydrophobic Lipophilic Polysilicon (HLP) nanoparticles. Two approaches exist for building nanoparticles, these are the top-down and the bottom-up approaches. The top- down approach is also referred to as miniaturized technology that are employed in integrated circuits, sensors, telecommunications, environmental monitoring or bio-oriented diagnostics. Nevertheless, the true nano-revolution relies on the full exploitation of the bottom-up approach, i.e. the creation of smart materials by exploiting their self- organisational capacity. It can be seen as the attempt to emulate nature in its intrinsic ability to build up and organise itself into complex structures starting from elementary atoms and molecules. Greff and Babadagli (2011) reported that Nickel improved the recovery of the steam stimulation process by 10%. Nanoparticles are distinguished by nearly 100 times larger specific surface area than microparticles. The sample experimented contained 0.5 wt % of the particles. The Nano-sized particles showed higher viscosity reduction than micron-sized particles. The larger specific area of nanoparticles resulted in more reactivity compared to the microparticles owing to more contact area with the oil phase of the former compared to the latter. The first pilot field tests using nano-sized particles as catalyst were in Liaohe oil fields, northeastern China (Li et al., 2007). In addition, extraordinary materials such as nano-scale sensors are likely inventions that can enhance oil recovery under harsh conditions, like deep water and areas with low temperature and salinity (Zhang, 2014). 2.2 Principles of Nano-enhanced Oil Recovery According to Enslayed and Fattah (2014), the applications of Nanotechnology in EOR can be summarised in three approaches; Nanocatalysts, Nanoemulsions and Nanofluids. Nanocatalysts are defined as metallic nanoparticles injected into heavy oil reservoir for the purpose of breaking carbon sulphur bond in the asphaltenes (present in the heavy oil). The nanocatalysts are injected along with steam and the resultant breaking of C-S bonds in the heavy oil increases the proportion of saturates and aromatics. This chemical reaction, UNIV ERSIT Y O F IB ADAN L IB RARY 21 referred to as aquathermolysis, leads to irreversible viscosity reduction in the heavy oil. Nickel and iron are catalysts and have proven to catalyse such reactions. Nanoemulsion is a kind of pickering emulsion that is stabilized by nanoparticles instead of surfactant and is more stable under harsh condition of temperature and salinity. The large viscosity of nano-stabilised emulsions can help to manage mobility ratio during flooding which provides a viable method to push highly viscous oil from the subsurface, rather than polymers that are relatively large and have high retention on reservoir rock. The nanoparticle-stabilised emulsion droplets are tiny enough to allow to pass through most pores, and flow freely through the reservoir. Due to irreversible adsorption on their droplet surfaces, they also remain stable in harsh circumstances in reservoirs. 2.3 Displacement Mechanisms of Nanofluid Flooding 2.3.1 Structural Disjoining Pressure Experimental investigations of the recovery mechanisms in EOR application of nanofluids have been carried out by several authors (Wasan and Nikolov, 2003; Chengara et al., 2004; Wasan et al., 2011). The recovery mechanism was referred to as structural displacement mechanism and authors have attributed the mechanism to Brownian motion and electrostatic repulsion between nanoparticles. The electrostatic force of repulsion increases with the concentration of nanoparticles but decreases with particle size. The authors revealed that nanoparticles present in the three phase region between oil, water and rock tend to force themselves between the discontinuous phase and the solid rock surface. They create a wedge-like structure which separates the formation fluid (oil) from the pore wall and enhances the spreading behaviour of nanofluid (Fig. 2.1). UNIV ERSIT Y O F IB ADAN L IB RARY 22 Fig. 2.1: Disjoining Pressure in the Wedge Structure (Wasan and Nikolov, 2003) UNIV ERSIT Y O F IB ADAN L IB RARY 23 The displacement efficiency of nanofluids increased with decrease in particle sizes. The smaller the particle sizes, the higher the charge density, the greater the electrostatic force of repulsion and the stronger the structural disjoining pressure. The mechanism of displacement of nanoparticle in particle dispersion (NPD) may be explained by the principle of structural disjoining pressure. A film of wedge-shape assemblage, which separates the discontinuous phase(s) (oil, gas, water or paraffin) from the surface of the formation, is normally formed. This results in the additional recovery than would be possible with conventional additives or fluids (McElfresh, 2012). Hendraningrat et al. (2013b), in their work, studied the parameters involved in structural disjoining pressure mechanism such as lowering of interfacial tension (IFT) and alteration of wettability. Water-wet Berea core plugs with permeability in the range 9-400 mD were investigated in laboratory coreflood experiments using different nanofluid concentrations. Nanofluid concentrations of 0.01, 0.05 and 0.1 wt% were synthesised with synthetic brine. It was observed that IFT decreased as nanofluid concentration increased indicating a potential for EOR. Another challenge that was discovered is the impairment of porosity and permeability as nanofluid concentration increased. El-Diasty (2015) also carried out an experiment to investigate the effects of nanoparticle size and concentration on oil recovery in Bahariya formation in Egypt. Different silica nanoparticle sizes of 5, 20, 40 and 60 nm and concentrations 0.01-3.00 wt% were investigated. A size of 20 nm for silica nanoparticles and concentration of 3.0 wt% were considered optimum for injection rate of 2 cc/min. The nanofluid (20 nm - 3.00 wt%) flooding recovered 65% of the Initial-Oil-In-Place (IOIP) at breakthrough compared to that of water flooding which yielded 36% of IOIP at breakthrough 2.3.2 Displacement by Interfacial Tension Reduction and Wettability Alteration After waterflooding, almost all the remaining oil is immobile. The discontinuous residual oil which is the target of the nanofluid exists in the form of small spherical globules behind pore throats and cannot pass through them (Anderson, 1987). Oil recovery by nanofluid results from two main mechanisms. These include IFT reduction and Wettability alteration. UNIV ERSIT Y O F IB ADAN L IB RARY 24 The alteration to wettability can be from water wet to oil wet depending on the nature and type of the nanoparticles. 1. Effect of Nanofluid on Interfacial Tension At the oil and water interface, the force acting tangentially to the interface is referred to as interfacial tension. Reduction of interfacial tension decreased the work of deformation needed for oil droplets to move through the pore throat. Therefore, the trapped oil packets are mobilised and can pass through the pore throat easily. In addition, capillary pressure acts as a barrier in pore throat for the displacement of mobilized oil from one pore to another (Fig. 2.2) (Roustaei et al., 2012). Nanoparticles are known to structure themselves at the oil/brine interface, thereby reducing the contact between the two phases. This results in the lowering of the IFT. The IFT reduces as the concentration of the nanofluid increases (Li et al., 2013a; Dahle, 2014). UNIV ERSIT Y O F IB ADAN L IB RARY 25 Fig. 2.2: Role of nanoparticles in wettability alteration and consequently direction of capillary curvature in the pore throat before (a) and after surface modification with nanoparticles (b and c) (Source: Roustaei et al., 2012). UNIV ERSIT Y O F IB ADAN L IB RARY 26 2. Effect of Nanofluid on Wettability Wettability is the tendency of a fluid to spread on to a solid surface in the presence of another immiscible fluid. The preferential spreading occurs as a result of the individual fluid differential adhesion to the solid surface and the interfacial tension. A surface may be oil wet or water wet; a contact angle is formed due to force balance between the spreading coefficient of the solid surface in equilibrium contact between oil and water (Agi et al., 2018). Ju et al. (2002) investigated the mechanism of oil recovery in Lipophilic Hydrophilic Polysilicon (LHP) nanoparticles for changing the wettability of porous media theoretically. To quantitatively anticipate changes in relative and effective permeability of the oil and water phases, as well as oil recovery in sandstone after waterflooding, a one-dimensional two-phase mathematical model was presented, and a simulator was created. The distribution of particle concentration, the reduction in porosity and liquid permeability, the LHP volume retention on pore walls and pore throats across a dimensionless distance, and oil production performance were all studied using numerical models. They recommended LHP concentration in the range of 0.02-0.03%wt to enhance oil recovery; any further increase in concentration will lead to formation damage due to reduction of permeability. In addition, they concluded that oil recovery can be obviously improved by flooding with LHP. Wettability alteration by silica nanoparticles in glass micro models had been established using experimental and numerical approaches (Rostami et al., 2019). Initially water-wet and imposed oil-wet micromodels were investigated.-and flooded with nanofluids. Comparisons of experimental flooding scenarios and numerical simulation results were done for the two differently saturated glass micromodels. The result of the two agreed and 3. Effect of Nanofluid on Mobility Jikish (2012) conducted a research on the use of nanoparticles as stabilising agents for CO2 flooding. Nanoparticle-stabilised foams could be a novel way to create superior CO2 EOR mobility control agents. Surfactant-produced foams may disintegrate in tough reservoirs due to adsorption on reservoir rock and high temperature, therefore aqueous nanoparticle UNIV ERSIT Y O F IB ADAN L IB RARY 27 dispersions may be a viable option. Commercial fumed nanosilica can be purchased at very low cost, at less than USD 4/lbm. The costs can be reduced further by use of other nanoparticles (e.g., nanoclays or fly ash). Proof-of-concept tests in real porous media have shown that it is possible to propagate these dispersions through a porous medium without the adsorption or trapping of nanoparticles in pores. The results are promising at laboratory scale. More tests are needed to show the ability of nanofoam to improve conformance for better volumetric sweep efficiency. Although this technology is still in its early development, some operators have expressed interest in limited field testing. 2.4 Synergy of EOR Methods for Oil Recovery Optimisation One of the ways to overcome the limitations of EOR processes, which have been investigated, is synergising two or more EOR methods to leverage on the advantages of individual methods and also nullify their individual shortcomings. For example, Orodu et al. (2019) investigated the enhanced oil recovery potential of nanocomposites formed from the combination of Al2O3 nanoparticles and uncommon biopolymers. These investigation were based on rheology and stability of the biolymer. Niger Delta region and Berea sandstone core plug samples were used. The incremental oil recovery after waterflooding (secondary recovery) was 5–12% and 5–7% for potato starch nanocomposite (PSPNP) and gum Arabic nanocomposite (GCNP) respestively. Akanji et al. (2019) examined the applicability of the synergy of alkaline-surfactant- polymer-flooding in comparison with surfactant-polymer flooding for EOR in Angolan field. Poly (vinyl) alcohol was used as the polymer agent while rhamnolipid and Sodium hydroxide (NaOH) were used as the surface-active agent (surfactant) and alkaline medium respectively. The alkaline-surfactant-polymer combination presented more reduction in surface tension and Interfacial tension (IFT). Another investigation was carried out by Udoh et al. (2018) to assess the prospect of combining environmentally friendly bio-surfactants with controlled salinity water injection. The bio-surfactants used were rhamnolipid and protein enzyme. Combined controlled salinity water injection with protein enzyme indicated reduction in IFT from 3.4-2.50 mN/m, while with rhamnolipid, IFT increased from 0.11- 0.34 mN/m. UNIV ERSIT Y O F IB ADAN L IB RARY 28 2.5 Transport of Fluids in Porous Media Darcy’s law governs the flow of fluid in porous media. Hence, for incompressible Newtonian flows, the continuity equations of oil (o) and water (w) phases are given by: 𝜕 𝜕𝑡 (Ф𝑆𝑙) − 𝜕 𝜕𝑡 ( 𝐾𝑙 𝜇𝑙 𝜕𝑃𝑙 𝜕𝑥 ) = 0, 𝑓 = 𝑜, 𝑤 (2.1) With initial and boundary conditions at 𝑆𝑓=𝑆𝑓0 and 𝑃𝑓=𝑃𝑓0 at t=0; 𝐾𝑤 𝜇𝑤 𝜕𝑃𝑤 𝜕𝑥 = 𝑞 𝑎𝑡 x=0, 𝐾𝑤 𝜇𝑤 𝜕𝑃𝑤 𝜕𝑥 + 𝐾𝑜 𝜇𝑜 𝜕𝑃𝑜 𝜕𝑥 = 𝑞 𝑎𝑡 𝑥 = 𝐿, where x is the distance from the inlet of the core or porous medium, t is time, Ф is the porosity, 𝑃𝑓, 𝑆𝑓, and 𝜇𝑓 are pressure, saturation and viscosity of phase 𝑓, respectively, and effective permeability of phase 𝑓 is kf=kr/k. Capillary force is given by equation (2.2) (Donaldson et al., 1991): Pc= Po-Pw = (a + bSw) / (1+cSw) (2.2) where Sw is water saturation and where constants a, b and c are empirical parameters. 2.5.1 Transport of Nanofluids in Porous Media The interaction between nanoparticles and pore walls is caused by five different types of energy. Attractive potential energy of London-van der Waals repulsion, energy of electric double layers, Born repulsion, acid-base interaction, and hydrodynamic energy are some of these. The attraction force between nanoparticles and porous walls is stronger than the repulsive force when the total energy is negative, resulting in more nanoparticle adsorption Khilar and Fogler (1999). Desorption of nanoparticles from porous walls will occur otherwise. The total energy between particles and porous walls controls the dynamic equilibrium between adsorption and desorption. Blocking occurs when the diameter of LHP UNIV ERSIT Y O F IB ADAN L IB RARY 29 particles exceeds the pore throat size, or when several LHP particles smaller than the pore throat size clump together to clog the pore throat. Ju et al. (2006) based the model for simulating the transport of nanofluids (Lipophobic and hydrophilic polysilicon (LHP)) in porous media on the assumptions below: i. Under isothermal conditions, flow is one-dimensional, and the rock and fluids are assumed to be incompressible; ii. Aqueous LHP solution is homogeneous; iii. Flow of oil and water in porous media follows Darcy's law, and gravity force is ignored; iv. LHP particles are discretized into n size intervals; v. Fluid viscosity and density are constant, and both oil and water are Newtonian vi. Chemical reactions are not taken into account LHP can only exist in water (hydrophilic). Because the nanoparticles' diameters range from 10 to 500 nm, Brownian diffusion effects should be taken into account. As a result, the continuity equation for the nanoparticle size interval i in phase f can be written as 𝑢𝑤 𝜕𝐶𝑖 𝜕𝑥 + Ф𝑆𝑤 𝜕𝐶𝑖 𝜕𝑡 − 𝜕 𝜕𝑡 (Ф𝑆𝑤𝐷𝑖 𝜕𝐶𝑖 𝜕𝑥 ) + 𝑅𝑖 = 0 (2.3) Ci=0, at t=0 Ci=Ci, in at x=0, at initial conditions UNIV ERSIT Y O F IB ADAN L IB RARY 30 where Ci is the volume concentration of LHP particles in interval i in the water phase, Di is the dispersion coefficient of LHP particles in size interval i in the water phase, Ri is the net losing rate of LHP particles in interval i in the water phase, and C i,in is the concentration of LHP particles in interval i in the injected fluids. 2.6 Preparation of Nanofluids In the preparation of nanofluid, stability of the suspension is an important consideration to achieve a considerable efficiency both at the microscopic and macroscopic levels. Several methods are employed to enhance the stability (Devendiran and Amirtham, 2016). Some of them include: 1. pH Value Alteration: The pH value at which a particle carries zero charge electric charge or where minimal forces of hydration are observed is known as the isoelectric point (IEP). When IEP gets close to the pH of nanofluids, instability occurs. The Zeta Potential is zero at the IEP, and the repulsive forces between NPs in suspension are low, with a propensity to consolidate at the suspension's base. Russel et al., 1992). Therefore, a high hydration force is requisite for the enhancement of stability (Wen and Ding, 2005). 2. Using surfactants: Surfactants can act as a link between NPs and base fluids, allowing for continuity between the two. Hydrophilic NPs, such as oxide NPs, will disperse readily in polar base fluids such as water. When hydrophobic NPs must be dispersed in polar base fluids and hydrophilic NPs must be dispersed in non-polar base fluids, surfactants must be added to stabilize the nanofluids. The inclusion of surfactant has an effect on the thermophysical properties of nanofluids, which should be noted (Yu et al., 2012). 3. Using ultrasonic vibration: Ultrasonication baths or probe-based ultrasonic devices are often employed to disperse NP aggregates. Ultrasonic devices with probes operate at a very high frequency. As a result of the separation of extremely small metal particles from the surface of the metal probe, there is a risk of contamination of nanofluids. This could have a negative impact on nanofluid stability (Ruan and Jacobi, 2012). UNIV ERSIT Y O F IB ADAN L IB RARY 31 2.7 Retention of Nanoparticles in Porous Media Continuous deposition of nanoparticles on pore surfaces and pore throats cause particle retention (Ju et al., 2006). Under the influence of colloidal and hydrodynamic forces, resorbed particles could be desorbed. However, there is a possibility of re-adsorption on different pore body sites or pore throat trapping. By modifying the Liu and Civan’s model (1993), Ri in Eq. (2.3) is given by 𝑅𝑖 = 𝜕 (𝑉𝑖+𝑉𝑖 ∗) 𝜕𝑡 (2.4) where 𝑉𝑖 is the volume of LHP particles i in contact with the water phase available on the pore surfaces per unit bulk volume of sandstone, 𝑉𝑖 ∗is the volume of LHP particles i entrapped in pore throats from water phase per unit bulk volume of sandstone due to plugging and bridging. 2.8 Economic Evaluation of EOR Processes The field applicability of any EOR process will depend greatly upon its economic implication. Oil price and costs of implementation of an EOR technique are key factors that drive its application. During the regime of high oil prices, many EOR techniques usually come on stream. In the period of low prices, careful scrutiny of the process and economic analysis is imperative. Conventional economic evaluation involves the use of net present value (NPV), internal rate of return (IRR), unit technical cost (UTC) and profitability index (PI). Sometimes, the pay- back period could also serve as a useful economic decision tool. However, the above approaches are usually applied deterministically and thus, do not account for uncertainties that come with using single-point estimates of inputs into the economic model. Some authors have pointed out the shortcomings with deterministic approach some of which include inability to incorporate managerial flexibilities, uncertainties emerging from price and cost volatilities, assumption of irreversibility of the decision. UNIV ERSIT Y O F IB ADAN L IB RARY 32 Apart from economic uncertainties, technical uncertainties may pose a great challenge in the implementation of an EOR process. Alkhatib and King (2011), for instance, pointed out that challenges arise due to uncertainty in field application e.g. reservoir heterogeneity, surfactant absorption, etc. for wide scale implementation of surfactant EOR. They emphasised that managing these uncertainties is essential for optimal implementation policy. Surfactant flooding has been proposed as a feasible way of evaluation and decision making using real options theory. The Real options technique was based on the Least Squares Monte Carlo (LSM) algorithm. Scenarios based on a synthetic reservoir model were used to test the algorithm. Finding the best timing to start the surfactant flood was one of the options considered. The reservoir's expected life was ten years. The start of years 4, 5, 6, and 7 were chosen as the decision nodes. The Schlumberger ECLIPSE was used as the numerical simulator for the surfactant flood, and a MATLAB code was used to conduct the various simulations and the LSM algorithm. The variables which include residual oil after chemical flood (Sorc) and surfactant adsorption (Ds), were assumed to be the state variables at different time and then at the same time. The ideal surfactant flood beginning times were discovered to be at year 6. In comparison to the no-option scenario of starting the flood at the beginning of the reservoir life (year 0), the optimal injection policies recommended achieved, on average, an increase in recovery efficiencies of 0.123, 0.147 and 0.141 for Cases 1, 2 and 3 respectively in contrast to the no-option scenario of initiating the surfactant flood at the start of the reservoir life (year 0). These values represent the value of the flexibility in initiating surfactant flooding. This method is being considered for more complex and realistic situations. Fathi and Ramirez (1983) have previously looked into the best injection policies for surfactant flooding. The goal of the optimization was to optimize the amount of oil recovered while lowering the cost (or amount) of chemicals utilized. To solve this dynamic computational problem, a steepest-descent gradient method was employed as the computational methodology. The algorithm's performance was tested for surfactant injection in a one-dimensional flooding situation. Two types of interfacial tension (1FT) behavior were considered. These are Type A system where the 1FT is a monotonically UNIV ERSIT Y O F IB ADAN L IB RARY 33 decreasing function with solute concentration and Type B system where a minimum 1FT occurs at a nominal surfactant concentration. For Type A system, the shape of the optimal injection strategy was not unique; however, there was a unique optimum for the amount of surfactant needed. For Type B system, the shape of the optimal injection as well as the ·amount injected) was unique. Joshi et al. (1998) applied Monte Carlo Simulation to the Wilmington steamflood project to quantify the risk and uncertainties associated with the project. For the determination of production rates and economic analysis, Monte Carlo Simulation was used. Production calculations accompanied economic analysis were done based on statistical models, serving as a sensitive method confirming the convergence of the Monte Carlo Simulation. Stochastic assumption were made for the main inputs, such as porosity, net pay and oil saturations and distributions that followed the assumption included triangular, normal and triangular (10th/90th) distribution. Probabilistic Net Cash Flow (NCF), Net present Value (NPV) and Internal Rate of Return (IRR) were presented as the simulation outputs in form of probability density curves, probability density curves, cumulative probability density curves, tornado diagrams, etc. Zhong et al. (2013) used Black and Scholes model and Differential Equation to evaluate the value of a polymer injection project in the North oil fields of China. They pointed out the sources of uncertainty in the application of EOR techniques which include external risks, .i.e., from the macroeconomic environment, global oil market, financial market, competitors, government policy, natural disasters, and internal risks which may originate from employee’s skills, change in management, occasional events. Technical risks arise from reserve estimates and estimated production data. The uncertainties were captured by parameters which were incorporated in the Differential equation. The input to their model had some assumptions. The assumptions include Geometric Brownian Motion of oil price, no risk-free arbitrage opportunity, no transaction costs, taxes, etc. an option value, V above the value obtained from the traditional NPV was obtained from their models. Ajulibe et al. (2018) investigated the viability of silica nanofluid for EOR application in Niger Delta with focus on the economics. Using a comparative economic approach, the UNIV ERSIT Y O F IB ADAN L IB RARY 34 evaluation of the economic feasibility of Alkali-Surfactant-Polymer (ASP), Water- Alternating Gas (WAG) and Silica nanofluid EOR projects were investigated using discounted cash flow method. The NPVs for ASP, WAG and Silica naofluid were $15.45M, $25.30M and $57.88M, respectively. The IRR for all the EOR options were all above the hurdle rate 15.0% that was used. Hence, all three projects were profitable, however, silica nanofluid options was the most potentially profitable. UNIV ERSIT Y O F IB ADAN L IB RARY 35 CHAPTER THREE METHODOLOGY 3.1 Materials The materials used for the study were eight representative sandstone core samples (labelled Y1, Y2, Y3, Y4, Y5, Y6, Y7 and Y8) and crude oil obtained from Agbada Formation in the Niger Delta region; silica nanoparticles and reconstituted brine. The equipment included digital weighing balance, vernier caliper, porosimeter, permeameter, soxhlet extraction apparatus, scanning electron microscopy (SEM) apparatus, viscosimeter, pycnometer, vacuum saturator and relative permeability tester (core-flood set-up) 3.2 Preliminary Experimental Set Up The experimental set up could be broadly categorised into two; namely preliminary experimental set up and main experimental set up. The preliminary set up involved preparation, formulation and characterisation of materials, measurement of interfacial tension (IFT) as well as vacuum saturation of core samples, while the main set up involved the coreflood set up. The coreflood set up could further be categorised into four parts which include drainage (also referred to as oil flooding) and BF, NF and NABF. The BF was the control while NF and NABF were the experimented methods. 3.2.1 Preparation of Core Samples The core samples were cleaned using soxhlet extraction, which is the most popular procedure for cleaning core samples, as shown in Figure 3.1. Methanol was heated to the boiling point of 65-70oC during soxhlet extraction. The vapour rose through the core and into the condenser, where it was condensed by cool flowing water. The re-condensed methanol dripped into the thimble's core sample, cleaning it of any water or other contaminants. The condensed liquid was mechanically discharged into the boiling flask UNIV ERSIT Y O F IB ADAN L IB RARY 36 when it reached the top of the tube. After cleaning, the samples were dried in 70oC oven for 6 hours and then cooled for another 6 hours. UNIV ERSIT Y O F IB ADAN L IB RARY 37 Fig. 3.1: Soxhlet Extraction Apparatus UNIV ERSIT Y O F IB ADAN L IB RARY 38 3.2.2 Characterisation of Core Samples i. Weight, Volume and Density Measurements The dry weight, Wc, of each core sample was measured using a digital weighing balance while the diameter, dc and length, lc were measured using a vernier caliper. The bulk volume, Vb and bulk density, 𝜌𝑐 of each core sample were calculated using equations (3.1) and (3.2) respectively. 𝑉𝑏 = 𝜋 𝑑𝑐 4 4 (3.1) 𝜌𝑐 = 𝑊𝑐 𝑉𝑏 (3.2) ii. Porosity Measurement The porosity measurements of the core samples were done using the Helium Ultrapore Porosimeter and liquid saturation method. The Helium porosimeter measures porosity of a dry core sample. In a closed cell, it uses the principle of gas expansion. The core sample was placed in the Helium Ultrapore Porosimeter and helium gas in a reference cell expanded into a sample cell, at constant temperature. Using Boyle’s law, the helium porosimeter determines the volume of the sample chamber, as shown in equation (3.3). 𝑉𝑐 = (𝑃−𝑃𝑟)𝑉𝑟 𝑃𝑐−𝑃 (3.3) Where the initial pressure in the reference cell and sample chamber are 𝑃𝑟 and 𝑃𝑐 respectively, and the equilibrium pressure once the valve is opened, is P. The difference in the volume of the empty reference cell Vr and the chamber of core sample Vc, is the grain volume Vg. The grain density, 𝜌𝑔 , is also computed from the ratio of weight and grain volume of the core sample. The scale on Helium porosimeter is graduated in volumes and UNIV ERSIT Y O F IB ADAN L IB RARY 39 so, volumes are directly read from it, and effective porosity may be determined as ratio of pore volume, VpHe to bulk volume, Vb, as shown in equation (3.4) Ф𝐻𝑒 = 𝑉𝑝𝐻𝑒 𝑉𝑏 = 𝑉𝑏−𝑉𝑔 𝑉𝑏 = 𝑉𝑏−(𝑉𝑐−𝑉𝑟) 𝑉𝑏 (3.4) The Helium porosimeter is preferable over other porosimeters because Helium gas is inert and so, does adsorb on pore surface. In addition, the particle size of the gas is small and thus can penetrate the tiny pores. The gas also has high diffusivity and hence useful in measuring low permeability rocks. The liquid saturation method employs the difference in weights between the dry core sample Wc, and the saturated core sample, Ws, to obtain the liquid saturated pore weight, Wl (which in this case, is brine) as shown in equation (3.5). The pore weight is then converted to pore volume, Vpl, by dividing it by the liquid density, 𝜌𝑙 . The resulting pore volume is then divided by the bulk volume, Vb to obtain the liquid saturation effective porosity, Ф𝑙𝑠, in %, defined by equation (3.6). The porosity values used for the flooding experiments in this study, were those obtained from the liquid saturation method since the experiment fluids were in liquid state. 𝑊𝑙 = 𝑊𝑠 − 𝑊𝑐 (3.5) Ф𝑙𝑠 = 𝜌𝑙 𝑋 𝑊𝑙 𝑉𝑏 (3.6) UNIV ERSIT Y O F IB ADAN L IB RARY 40 iii. Permeability Measurement Liquid permeabilities, k, of the core samples were estimated using a liquid Hassler permeameter. The permeability measurement was based on Darcy’s law by measuring the flow time of a single fluid through a constricted tube. The experiment was repeated for three times to estimate the average liquid permeability of the core plug. 3.2.3 Formulation and Characterisation of Injection Brine Synthetic brine was made from 32.2 g of laboratory grade Sodium Chloride dissolved in 1000 cm3 (1 litre) of de-ionised water. The 32.2 g/litre concentration of NaCl is revealed by PVT analysis of formation water found in Niger Delta. Brine density and viscosity were then measured by pcynometer and viscosimeter, respectively. 3.2.4 Characterisation of Crude Oil Degassed and de-watered crude oil sample obtained from the same Niger Delta region using decantation method. Its properties such as density and viscosity were measured. The density was measured using a pycnometer while the dynamic and kinematic viscosities were estimated using a viscosimeter. 3.2.5 Formulation and Characterisation of Different Nanofluid Concentrations Nanofluid was formulated by suspending LHP nanoparticles (size range: 20-70 nm, surface area: 135-140 m2/g and purity: 98.0-99.5%), procured from Burgoyne Urbidges Laboratory, India in the synthetic brine. The suspension was prepared in four distinct concentrations: 0.01, 0.50, 2.00 and 3.00 wt%, labelled Si1, Si2, Si3 and Si4, respectively. These were concentrations of nanoparticles that gave lower IFT values with crude oil, as compared to IFT value of brine and crude oil. A magnetic spinner was used to mix the nanofluid suspensions to achieve uniformity for up to 5 minutes. The density and viscosity of each nanofluid concentration were measured using pycnometer and viscosimeter. UNIV ERSIT Y O F IB ADAN L IB RARY 41 3.2.6 Measurement of Interfacial Tension between Nanofluid and Crude Oil Interfacial tension (IFT) between each nanofluid concentration and crude oil was measured to ensure that its value was within low IFT range. The IFT measurement was carried out using a ring tensiometer. The ring tensiometer uses a metallic ring lowered below the interface of two fluids. The metallic ring was lowered below the interface of the nanofluid and crude oil. The force required to pull the metal ring through the interface between the nanofluid and crude oil to the nanofluid medium is the interfacial tension and is read off in dynes/cm on the scale of the tensiometer. The experimental was repeated three times for each concentration to establish the range of values. Figure 3.2 depicts the picture of a tensiometer. UNIV ERSIT Y O F IB ADAN L IB RARY 42 Fig. 3.2: Ring Tensiometer IFT disc Tension ring Immiscible fluids: nanofluid, brine and oil UNIV ERSIT Y O F IB ADAN L IB RARY 43 3.2.7 Vacuum Saturation with Brine The core samples were saturated with brine in the vacuum chamber of a saturator (made by VINCI technologies) as shown in Figure 3.3, for about 24 hours. The saturation pressure was 2,100 psia, typical of pressure obtained at reservoir condition. The saturated weight of each core sample was measured. To obtain the volume of brine in each saturated sample, the sample was weighed and the weight of the dry sample was subtracted from the weight of the saturated sample. The differential weight obtained was divided by the density of the reconstituted brine to obtain the volume of brine in each sample as shown in equation (3.7). Initial volume of brine in the saturated sample, Viw = 𝑊𝑙𝑠−𝑊𝑐 𝜌𝑤 , (3.7) Where 𝑊𝑙𝑠=weight of brine saturated sample, g; Wc= weight of dry sample, g; 𝜌𝑤 = density of brine, g/cm3 UNIV ERSIT Y O F IB ADAN L IB RARY 44 Fig. 3.3: Vacuum Saturator UNIV ERSIT Y O F IB ADAN L IB RARY 45 3.3 Coreflood Set Up The coreflooding process was carried out using a reservoir permeability tester (RPT). The RPT is a complex system that consists of a core holder with three accumulators for water (brine), oil and EOR fluid; as well as interconnection of pipes, tubing, pumps, pressure gauges, regulators and pressure valves and flow valves to control flow direction. It also consists of digital and analogue metres for adjusting and monitoring flow conditions. The vacuum pump is used to control flow of fluid into each of the accumulators before flooding begins. The effluent is collected at the core holder’s outlet. The picture of the coreflood set- up is as shown in Figure 3.4. The schematic of the coreflood set up is illustrated in ure 3.5 The brine saturated core sample was loaded in the sleeve of the core holder and kept in place with spacers to avoid unsolicited invasion of fluid into the space around the core chamber during fluid injection. The fluid pump was then switched on and corresponding valves open to admit fluid (brine, oil and nanofluid) into the accumulators. The temperature condition in the RPT was maintained at 28 oC. UNIV ERSIT Y O F IB ADAN L IB RARY 46 Fig. 3.4: Relative permeability tester used for corefloodingUNIV ERSIT Y O F IB ADAN L IB RARY 47 Fig. 3.5: Schematic diagram of the coreflood set up showing: 1) Pump fluid; 2) Injection line; 3) Fluid pump; 4) Valve 5) Pump fluid in accumulator A; 6) Piston plate; 7) Brine in accumulator –A; 8) Oil in accumulator–B; 9) Nanofluid in accumulator–C; 10) Oil line; 11) fluid line; 12)Core holder; 13) Core plug in the core holder 14) Pressure gauge; 15) Temperature gauge; 16) Sleeve pressure; 17) Effluent collector A B A C A UNIV ERSIT Y O F IB ADAN L IB RARY 48 3.3.1 Primary Drainage Process (Oil Flooding) The degassed and dewatered crude oil pumped into accumulator B as shown in Figure 3.5 above, was injected into the brine saturated core sample (containing initial saturation of 100% brine). The drainage process was performed at a confining pressure of 300 psia and a flow rate of 2 cm3/min to displace brine until no brine flowed into the effluent collector. 3-5 pore volume (PV) of crude oil was injected to reach a point of further displacement of brine from the core. The initial water saturation, Swi, was determined through material balance (i.e. initial volume of brine in the saturated sample minus volume of brine produced; hence, the OOIP was estimated from the volume of brine produced. The core plug was then aged for 24 hours for wettability restoration and oil-water distributions refinement at pore level. This process was repeated for all the core samples (Y1, Y2, Y3, Y4, Y5, Y6, Y7 and Y8). A similar flow rate of 2 cm3/min was used for all the flooding processes to achieve uniform basis. The initial water saturation can be calculated as shown in equation (3.8) Swi = 𝑉𝑖𝑤−𝑉𝑝𝑤 𝑉𝑖𝑤 (3.8) where Viw =initial volume of brine in the saturated sample, Vpw =volume of brine produced, or original oil in place (OOIP), cm3 3.3.2 Secondary Brine Flooding (BF) Before secondary brine flooding, the core samples were at initial water, Swi and initial oil saturations, Soi. The secondary brine flooding served as the control. By opening the brine accumulator A (Fig. 3.5), each sample ((Y1, Y2, Y3, Y4 and Y5) was flooded with brine at 2cm3/min and volumes of effluents produced and pressure drop were measured and recorded as a function of time. The flooding continued until no more oil was produced. However, when stable pressure was obtained, end-point permeability of the core to water kw (at residual oil saturation) was calculated using Darcy’s law. Material balance was used to calculate the residual oil saturation Sor (i.e., OOIP minus volume of oil produced). Also, the UNIV ERSIT Y O F IB ADAN L IB RARY 49 core was aged for 24 hours for wettability restoration and oil-water distributions refinement at the pore level. The brine flooding oil recovery factor is determined as depicted in equation (3.9) 𝑂𝑅𝐹𝐵𝐹 = 𝑉𝑤𝑟 𝑂𝑂𝐼𝑃 , (3.9) where 𝑂𝑅𝐹𝐵𝐹 is brine flooding oil recovery factor, Vwr = volume of oil recovered during brine flooding, cm3; OOIP = original oil in place, cm3 3.3.3 Nanofluid Flooding (NF) with Changing Concentration and Injection Rate Accumulator C (Fig. 3.5) was opened and nanofluid containing concentration of 0.01 wt% was injected at 0.5 cm3/min into the core chamber containing core Y1 that was previously flooded with brine (secondary brine flooding), until no more oil was recovered. The volume of oil recovered was subtracted from the residual oil volume before NF to obtain residual oil saturation after NF at the initial rate. The rate was then increased to 1.0, 2.0 and 3.0 cm3/min and oil volume recovered was recorded. The flooding procedure was repeated with the other core samples (Y2, Y3 and Y4) for nanofluid concentrations of 0.5, 2.0 and 3.0 wt%, respectively, using injection rates ranging from 1.0 to 3.0 cm3/min step-wisely for each of the core samples. The combination of nanofluid concentration and injection rate which yielded the highest oil recovery was recorded. These were the optimum nanofluid and injection rate. For the purpose of SEM analysis and comparison, Y5 was left only as a brine flooded core. The NF recovery factor can be written as: 𝑂𝑅𝐹𝑁𝐹 = 𝑉𝑁𝐹 𝑂𝑂𝐼𝑃 , (3.10) where ORFNF is the oil recovery factor for NF, VNF is the volume of oil recovered during NF, cm3; 𝑂𝑂𝐼𝑃 is the original oil in place, cm3 UNIV ERSIT Y O F IB ADAN L IB RARY 50 3.3.4 Nanofluid Flooding (NF) with Optimal Concentration and Injection Rate Accumulator C (Fig. 3.5) was emptied and refilled with the optimal nanofluid concentration. Core sample Y6 which had only been drained during the primary flooding process was placed in the core chamber of the RPT. This was then flooded with the optimal nanofluid concentration at the optimal injection rate recorded in the previous experiment in section 3.3.3. Oil produced was collected in an effluent collector until no more oil droplet was produced. 3.3.5 Nanofluid-Alternating-Brine Flooding (NABF) Accumulator A and C (Fig. 3.5) were filled with brine and nanofluid with optimal concentration, respectively. Core sample, Y8, initially drained during the primary oil flooding process was placed in the core holder of the RPT. Nanofluid was first injected into the core sample at the optimal injection rate and confining pressure of 300 psia until no more oil was recovered. Accumulator A was then opened and brine was pumped at the optimal injection rate into the core sample until no more oil was produced. The volume of oil recovered under each flooding process was recorded and residual oil saturations were estimated using material balance. 3.4 Scanning Electron Microscopy Scanning Electron Microscopy was carried out using the Scanning Electron Microscope (SEM) to obtain high resolution images and compare the changes that occurred on the pore surfaces of each of the core samples before and after the completion of the flooding processes. The SEM uses highly accelerated electron beam, producing higher resolutions compared to optical microscope, to investigate the surface texture, chemical composition, crystalline structure and orientations of materials which the sample is made up of. The electron beam interacts with atom of the sample to produce imagery of the surface topography and composition of the sample. Areas ranging from approximately 1 cm to 5 microns in width can be imaged in a scanning mode using conventional scanning electron microscopy techniques (magnification ranging from 20X to approximately 30,000X, spatial resolution of 50 to 100 nm). A thin section of each core plug (Y5, Y6, Y8) was analysed UNIV ERSIT Y O F IB ADAN L IB RARY 51 with SEM at a magnification of 5,000X and resolution of 2 µm. SEM with smaller and larger magnifications and resolutions were also captured. Y5 is the BF core, Y6 is the NF core while Y8 is NABF core. The SEM sections of the three core samples were interpreted and compared. 3.5 Model Development for Nanoskin Factor 3.5.1 Physical Description The concept of nanoskin was introduced by the author and is defined as a thin sheet of nanoparticles deposited and retained in the reservoir rock pore surfaces and throats during nanofluid flooding which causes porosity and permeability impairment and subsequent formation damage. As shown earlier in Figure 1.4, nanoskin formation could be explained by the principle of adsorption of nanoparticles and the removal of nanoskin, by the principle of desorption. The nanoskin factor is analogous to skin factor caused by mud cake deposition during drilling operations, which affects oil production. While flooding with nanofluid, same effect can occur but unlike skin effect, the nanoskin effect is not restricted to the well bore region but also propagates further to the reservoir. Many factors such as nanoparticle size, nanofluid concentration, salinity, injection pressure, injection rate, pore geometry, reservoir rock properties, temperature, contribute to nanoskin effect, however, for the purpose of this study, the factors were be limited to two, viz., nanofluid concentration and injection rate. The model for nanoskin factor was developed using Darcy’s equation. 3.5.2 Simplifying Assumptions The assumptions for the development of the model include: 1. Homogenous system 2. Fluids are incompressible 3. Two-phase flow 4. Linear piston-like displacement 5. No injection fluid loss UNIV ERSIT Y O F IB ADAN L IB RARY 52 6. Saturation of a fluid is the same throughout the porous medium (core plug) 3.5.3 Mathematical Model The nanoskin model is a linear model. The model consists of reservoir rock parameters such as porosity, permeability, cross-sectional area; reservoir fluid parameters, viscosity; flow parameters such as oil and water flow rates; oil recovery factor, and injection fluid properties such as nanofluid concentration, nanoparticle surface area and injection rate. Nanoskin factor, Sn, is the dependent variable while nanofluid concentration, Cnf and injection rate, qinj, are the independent variable while other variables are fixed. 3.5.4 Governing Equations i. Darcy’s equation Darcy’s law for a linear flow may be expressed as given in equation (3.11a) ∆𝑃 ∆𝐿 = −𝑞𝐴 𝑘𝜇 (3.11a) where ∆𝑃 ∆𝐿 = 𝑛𝑜𝑟𝑚𝑎𝑙 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 𝑑𝑟𝑜𝑝, q=flow rate, k =permeability A =cross-sectional area of flow ii. Frontal Advance Equation This is modification of Darcy’s equation and is governed by the principle of conservation of mass Rate of mass flow in-Rate of accumulation =Rate of mass flow out UNIV ERSIT Y O F IB ADAN L IB RARY 53 The frontal advance equation is given by equation (3.11b) ( 𝑑𝑥 𝑑𝑡 ) 𝑆𝑤 = ( 𝑞𝑡 𝜙𝐴 ) ( 𝑑𝑓𝑤 𝑑𝑆𝑤 ) 𝑆𝑤 (3.11b) Where 𝜈𝑠𝑤=velocity of any specified value of Sw A= Cross-sectional area in cm2 qt = Total flow rate (cm3/min) 𝜙= Porosity, fraction ( 𝑑𝑓𝑤 𝑑𝑆𝑤 ) 𝑆𝑤 = Slope of curve of fw vs. Sw at Sw Since we assume that saturation is the same throughout the porous medium, ( 𝑑𝑓𝑤 𝑑𝑆𝑤 ) 𝑆𝑤 = 1 Equation (3.11b) then becomes ( 𝑑𝑥 𝑑𝑡 ) 𝑡 = 𝑞𝑡 𝜙𝐴 (3.11c) ( 𝑑𝑥 𝑑𝑡 ) 𝑡 = total velocity of the two phases (oil and water) 𝑞𝑡 = 𝑞𝑜 + 𝑞𝑤 UNIV ERSIT Y O F IB ADAN L IB RARY 54 𝑞𝑜= flow rate of the water phase 𝑞𝑤= flow rate of the oil phase 3.5.5 Model Development During injection of nanoparticles into the well, an additional pressure drop results due to formation of nanoskin. Hence, total pressure drop may be given as: ∆𝑃 ∆𝐿 │total = ∆𝑃 ∆𝐿 │ns + ∆𝑃 ∆𝐿 │normal (3.12) This may be expressed as ∆𝑃 ∆𝐿 │total = 1 𝐴 [ 𝜇𝑤𝑞𝑤 𝑘𝑤 + 𝜇𝑜𝑞𝑜 𝑘𝑜 ] (1 + 𝑆𝑛) (3.13) We introduce the porosity term to account the effective flow from equation (3.11b). In addition, since the pressure drop is due to flow of the injectant; equation (3.13) becomes ∆𝑃 ∆𝐿 │ns= 1 𝜙𝐴 [ 𝜇𝑤𝑞𝑤 𝑘𝑤 + 𝜇𝑜𝑞𝑜 𝑘𝑜 ] 𝑆𝑛= 𝑞𝑡 𝜇𝑛𝑓 𝐴𝑛𝑓𝑘𝑛𝑓 (3.14) 𝑞𝑡 = 𝑞𝑖𝑛𝑗, assuming no fluid retention/loss (although nanoparticles could be retained); hence, 𝑠𝑛 = 𝑞 𝑖𝑛𝑗 𝜇𝑛𝑓 𝐴𝑛𝑝𝑘𝑛𝑓 1 𝜙𝐴 [ 𝜇𝑤𝑞𝑤 𝑘𝑤 + 𝜇𝑜𝑞𝑜 𝑘𝑜 ] (3.15) wt % Nanofluid concentration =Weight of nanoparticles/Weight of brine Cnf= Wnp/Ww, (3.16) Hence, Wnp=Cnf*Ww (3.17) Surface Area, SA= Area/Weight (3.18) UNIV ERSIT Y O F IB ADAN L IB RARY 55 SA= Anp/Wnp (3.19) Anp= SA*Wnp (3.20) Anp=SA*Cnf*Ww (3.21) Hence, 𝑠𝑛 = 𝑞 𝑖𝑛𝑗 𝜇𝑛𝑓 𝑆𝐴∗𝐶𝑛𝑓∗𝑊𝑤∗𝑘𝑛𝑓 1 𝜙𝐴 [ 𝜇𝑤𝑞𝑤 𝑘𝑤 + 𝜇𝑜𝑞𝑜 𝑘𝑜 ] (3.22) 𝑞𝑜 = 𝑂𝑅𝐹 ∗ 𝑞𝑖𝑛?