Understanding and Control of Microbially Influenced Corrosion in Simulated Oilfield Seawater Injection Systems Huiyun Zhong Master of Engineering, Environmental Science and Engineering
A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2019 School of Chemical Engineering Advanced Water Management Centre
Abstract
Microbially influenced corrosion (MIC) is the main cause of the localized corrosion, causing pipeline system leakages during the water injection process in secondary oil recovery. Biocides are often dosed to inhibit and kill the microbes which cause MIC. However, the MIC development in the early stages of water injection process was barely covered, and the demand for more innovative, effective and cost-efficient solutions to deal with MIC is still high. The overall objective of this thesis is to gain a better understanding of MIC process in secondary oil recovery and develop an effective MIC controlling strategy using free nitrous acid (FNA i.e. HNO2), which could be applied in practical oilfield operation either alone or combined with other corrosion inhibitory chemicals.
A continuously-fed biofilm reactor simulating the water injection process was operated to allow biofilm to develop on the carbon steel coupons to study the MIC development. The development of MIC process was monitored for 5 months with open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning electron microscopy (SEM), 3D optical profiling and weight-loss measurement. MIC development was found to comprise 3 phases: Initialization, Transition, and Stabilization. The Initialization phase involved the formation of the corrosion product layer and the initial attachment of the sessile microbes on the metal surface. In the Transition phase, the MIC process gradually shifted from charge-transfer-controlled reaction to diffusion-controlled reaction. The Stabilization phase featured mature and compact biofilm on the metal surface, and the general corrosion rate decreased due to the diffusional effect, while the pitting corrosion rate was enhanced at a lower carbon source level, which supported the mechanism of direct electron uptake from the metal surface by SRB.
Intermittent dosages of FNA, which was previously found to be a biocide, were applied to a simulated water injection system containing carbon steel coupons with mature biofilm, to study its effect on MIC mitigation. In each treatment, 0.49 mg-N/L FNA was dosed by using 200 mg-N/L nitrite at pH 6 for 24 h. The corrosion properties were monitored by OCP, EIS, LPR, 3D optical profiling, and weight-loss measurement. The biofilm viability was monitored by measuring cellular ATP level. The general corrosion rate (calculated by weight-loss measurement) was decreased by up to 31%, which was supported by LPR tests and reduced ATP levels of the corrosion-inducing biofilm. The 3D optical profiling results showed that FNA decreased the average pitting corrosion rate by 59%, with 2 intermittent treatments with an 82-day interval over 304 days. Intermittent dosing of FNA has strong potential to be an effective and efficient strategy for controlling MIC in oil recovery infrastructure.
I Imidazoline and its derivatives are widely used corrosion inhibitors for the protection of oilfield pipelines. As a typical imidazoline derivative, N-b-hydroxyethyl oleyl imidazoline (HEI-17) was selected to be applied with FNA to investigate the combined effect on the MIC behaviour of carbon steel. The effect of the combined application was compared with pure HEI-17 treatment and with no treatments. The corrosion properties were monitored with OCP, EIS, LPR, 3D optical profiling, and weight-loss measurement. Following a single dose of FNA, the general corrosion rates in the experimental reactor dropped up to 50% of that in the reactor receiving continuous HEI-17 dosing (0.27 ± 0.04 vs. 0.54 ± 0.08 mm/y), but gradually recovered to 93.4% of that in the Control + HEI- 17 reactor in 2.5 months. After the FNA treatment, the pitting corrosion was decreased by 64.6% compared with Control + HEI-17 for a month from measuring the cumulative distribution of the pitting depth. HEI-17 treatment alone showed moderate pitting corrosion inhibition effect (approx. 27%), and the FNA treatment inhibited the formation of deep pits effectively. The combined application of HEI-17 and FNA has shown synergetic effects and high efficiency in mitigating MIC in the simulated water injection system. This treatment strategy has strong potential to be applied in the practical oilfield operations.
Overall, this thesis focuses on MIC, the most common and detrimental problem in oilfield operations, and supplies future MIC understanding and combating with comprehensive outcomes from thorough laboratory experiments. The FNA-based strategy has the potential to be an economical and green substitute for the current traditional MIC treating procedures in real oil recovery infrastructures.
II Declaration by author
This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.
I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.
III Publications included in this thesis
Zhong H., Shi Z., Jiang G., Song Y., Yuan Z. (2019). Development of microbially influenced corrosion on carbon steel in a simulated water injection system. Materials and Corrosion 70, 1826– 1836. This paper is modified and incorporated in Chapter 5.
Contributor Statement of contribution Designed experiments (65%)
Conducted experiments (95%) Author Zhong H. (Candidate) Analysed, processed data and discussed results (70%)
Wrote and edited paper (70%) Designed experiments (10%)
Conducted experiments (5%) Author Shi Z. Analysed, processed data and discussed results (10%)
Wrote and edited paper (5%) Designed experiments (10%)
Author Jiang G. Analysed, processed data and discussed results (5%)
Wrote and edited paper (10%) Author Song Y. Analysed, processed data and discussed results (5%) Designed experiments (15%)
Author Yuan Z. Analysed, processed data and discussed results (10%)
Wrote and edited paper (15%)
IV Zhong H., Shi Z., Jiang G., Yuan Z. (2020). Decreasing microbially influenced metal corrosion using free nitrous acid in a simulated water injection system. Water Research 172, 115470. This paper is modified and incorporated in Chapter 6.
Contributor Statement of contribution Designed experiments (65%)
Conducted experiments (95%) Author Zhong H. (Candidate) Analysed, processed data and discussed results (70%)
Wrote and edited paper (70%) Designed experiments (10%)
Conducted experiments (5%) Author Shi Z. Analysed, processed data and discussed results (10%)
Wrote and edited paper (5%) Designed experiments (10%)
Author Jiang G. Analysed, processed data and discussed results (5%)
Wrote and edited paper (10%) Designed experiments (15%)
Author Yuan Z. Analysed, processed data and discussed results (15%)
Wrote and edited paper (15%)
V Submitted manuscripts included in this thesis
Zhong H., Shi Z., Jiang G., Yuan Z. Synergistic inhibitory effects of free nitrous acid and imidazoline derivative on metal corrosion in a simulated water injection system. Water Research. Submitted. This paper is modified and incorporated in Chapter 7.
Contributor Statement of contribution Designed experiments (65%)
Conducted experiments (100%) Author Zhong H. (Candidate) Analysed, processed data and discussed results (70%)
Wrote and edited paper (85%) Designed experiments (10%) Author Shi Z. Analysed, processed data and discussed results (10%) Designed experiments (10%) Author Jiang G. Analysed, processed data and discussed results (5%) Designed experiments (15%)
Author Yuan Z. Analysed, processed data and discussed results (15%)
Wrote and edited paper (15%)
VI Other Publications during candidature
Peer-reviewed journal papers
Song Y., Wightman E., Tian Y., Jack K., Li X., Zhong H., Bond P.L., Yuan Z., Jiang G. (2019). Corrosion of reinforcing steel in concrete sewers. Science of the Total Environment, 649:739-748.
Wang Z., Zheng M., Xue Y., Xia J., Zhong H., Ni G., Liu Y., Yuan Z., Hu S. (2020). Free ammonia shock treatment eliminates nitrite-oxidizing bacterial activity for mainstream biofilm nitritation process. Chemical Engineering Journal, 124682.
Conference presentations
Zhong H., Shi Z., Jiang G., Yuan Z. (2018). Effects of biofilm development on microbially influenced corrosion in a simulated water injection system. Oral presentation in The University of Queensland’s 10th Annual, Faculty of Engineering Architecture & Information Technology Postgraduate Conference, Brisbane, Australia. 6th June, 2018.
Contributions by others to the thesis
This thesis includes contributions made by others, particularly to sample analysis. These contributions are acknowledged as follows:
Dr. Beatrice Keller-Lehmann, Ms. Jianguang Li, and Mr. Nathan Clayton operated the ion chromatography (IC), gas chromatography (GC), high performance liquid chromatography (HPLC) and Flow Injection Analyzer (FIA) to analyse dissolved sulfur species, nitrous oxide, organic acids and dissolved nitrogen species.
Dr. Steven Mason from The School of Chemistry and Molecular Biosciences trained and assisted with the operation of Confocal Laser Scanning Microscope for Live/Dead staining and the multi- mode plate reader for ATP levels’ analysis.
Dr. Lien Chau from Australian National Fabrication Facility provided training and assisted on the 3D profiler.
Dr. Kim Sewell and Mr. Ron Rasch from Centre for Microscopy and Microanalysis helped with SEM training and operation for biofilm and coupon surfaces’ morphology analysis.
VII Statement of parts of the thesis submitted to qualify for the award of another degree
No works submitted towards another degree have been included in this thesis.
Research Involving Human or Animal Subjects
No animal or human subjects were involved in this research.
VIII Acknowledgements
My PhD life has been a life-changing experience with challenges and I could never have made my way here without the support and help I received.
First and foremost, I would like to express my deepest gratitude to my principal supervisor, Professor Zhiguo Yuan. I was so lucky to be offered the opportunity to pursue my PhD under his professional guidance. This thesis would have never been possible without his profound knowledge and broad experience. He has always been responsive and enthusiastic, and I can get energized every time we met. I appreciate it sincerely for his patience and encouragement, from which I gained strength to overcome the professional difficulties.
As my advisor, Dr. Guangming Jiang was always inspiring and helpful with his sound and valuable advice. His passion in research encouraged me to devote more time and effort into my research topics. I am so grateful for his detailed and careful comments on my drafts. I can always learn from the discussions and feedbacks.
I can never be more grateful to have Dr. Zhiming Shi join my advisory team when the experiment reached the bottleneck. He taught me step by step with his laboratory experience and I appreciate him for his availability to provide guidance with his expertise. Meeting with him was always relaxing and encouraging.
I have received so much support and kindness from all my colleagues in AWMC. They were always generous and helpful, making the centre a fun and enjoyable workplace. My sincere thanks go to Dr. Beatrice Keller-Lehman, Ms. Jiaguang Li, and Mr. Nathan Clayton for their analytical support. Many thanks for Ms. Vivienne Clayton and Mr. Charles Eddy for taking care of all the administrative matters. I appreciate Dr. Eloise Larsen for the prudent manuscript review and editing.
I thank all my lab fellows for making the experiments more enjoyable with their company. And the roommates in the PhD office were not only helpful colleagues but also lovely friends. The memory with them will always be cherished.
I acknowledge The University of Queensland and China Scholarship Council for funding the International Tuition Fee Scholarship and the Living Allowance Scholarship.
IX My deep gratitude goes to my parents. They have been caring and loving unconditionally for as long as I can remember. They taught me to look on the bright side with the tenderness in their hearts. This PhD journey was never lonely with their love and support.
At last, I would like to thank my boyfriend Mr. Bingzheng Wang. We were so lucky to start and complete our PhD together in the centre. I can always count on him, and he has been my closest partner who shared my ups and downs during the PhD journey.
X Financial support
This research was supported by UQ International (UQI) Tuition Fee Scholarship and China Scholarship Council Living Allowance Scholarship.
Keywords corrosion, microbially influenced corrosion, pitting, sulfate-reducing bacteria, electrochemical tests, corrosion control, free nitrous acid, imidazoline derivative, water injection, secondary oil recovery
XI Australian and New Zealand standard research classifications (ANZSRC)
ANZSRC code: 090703 Environmental Technologies, 80%
ANZSRC code: 091207 Metals and Alloy Materials, 20%
Fields of Research (FoR) Classification
FoR code: 0907, Environmental Engineering, 80%
FoR code: 0912, Materials Engineering, 20%
XII Table of Contents
Abstract ...... I
Declaration by author ...... III
Publications included in this thesis ...... IV
Submitted manuscripts included in this thesis...... VI
Other Publications during candidature ...... VII
Contributions by others to the thesis...... VII
Statement of parts of the thesis submitted to qualify for the award of another degree ...... VIII
Research Involving Human or Animal Subjects...... VIII
Acknowledgements ...... IX
Financial support ...... XI
Keywords...... XI
Australian and New Zealand standard research classifications (ANZSRC) ...... XII
Fields of Research (FoR) Classification ...... XII
Table of Contents ...... XIII
List of Figures & Tables ...... XVII
List of Abbreviations used in the thesis...... XXII
Chapter 1 Introduction ...... 1 1.1 Background ...... 1 1.2 Organization of the thesis ...... 3
Chapter 2 Literature Review ...... 5 2.1 Overview of MIC in the oilfield ...... 5 2.2 Microorganisms and biofilm in MIC ...... 7 2.2.1 Sulfate-reducing bacteria (SRB) ...... 7 2.2.2 Iron-reducing bacteria (IRB) ...... 10 2.2.3 Acid-producing bacteria (APB) ...... 10 2.2.4 Methanogenic archaea (Methanogens) ...... 11 2.2.5 Metal-oxidizing bacteria (MOB) ...... 11 2.2.6 Microbial consortia ...... 12 2.2.7 Development of biofilm ...... 12
XIII 2.3 Control and mitigation of MIC ...... 13 2.3.1 Mechanical cleaning ...... 14 2.3.2 Cathodic protection ...... 14 2.3.3 Coating ...... 14 2.3.4 Corrosion inhibitors ...... 15 2.3.5 Ultrasonic treatment (UT) ...... 15 2.3.6 Biocides ...... 16 2.3.7 Nitrate and nitrite...... 17 2.4 Inhibitory and biocidal effects of FNA on microbes ...... 19 2.4.1 Inhibitory effect of FNA on microbes ...... 19 2.4.2 Biocidal effect of FNA on microbes ...... 22 2.4.3 Mechanisms for FNA inhibitory and biocidal effects ...... 22 2.5 Current laboratory methodologies of MIC study ...... 26 2.6 Conclusion of literature review and research gaps ...... 27
Chapter 3 Research objectives ...... 29
Chapter 4 Methods and materials ...... 31 4.1 Simulated water injection system set-up and operation ...... 31 4.2 Synthetic produced water ...... 32 4.3 Electrochemical measurements ...... 32 4.4 ATP level measurement ...... 33 4.5 Corrosion rate by weight loss ...... 33 4.6 Pitting analysis with a 3D optical profiler ...... 34 4.7 Visualization of biofilm and corrosion products ...... 35 4.8 LIVE/DEAD staining ...... 35 4.9 Chemical analysis...... 35
Chapter 5 Development of microbially influenced corrosion on carbon steel in a simulated water injection system ...... 37 5.1 Introduction ...... 37 5.2 Materials and methods ...... 37 5.2.1 Experimental procedure ...... 37 5.2.2 Analysis ...... 37 5.3 Results ...... 37 5.3.1 Reactor operation and biofilm development ...... 38 5.3.2 Electrochemical measurements ...... 39
XIV 5.3.3 Weight-loss measurements ...... 44 5.3.4 Surface morphologies after removing corrosion products ...... 44 5.4 Discussion ...... 46 5.4.1 Three stages of the corrosion development ...... 46 5.4.2 Effect of diffusional influence on corrosion ...... 47 5.5 Conclusions ...... 48 5.6 Supplemental information ...... 49
Chapter 6 Decreasing microbially influenced metal corrosion using free nitrous acid in a simulated water injection system...... 55 6.1 Introduction ...... 55 6.2 Materials and methods ...... 55 6.2.1 Experimental procedure ...... 55 6.2.2 Analysis ...... 56 6.3 Results and discussion ...... 56 6.3.1 Reactor performance...... 56 6.3.2 General corrosion by weight-loss measurement ...... 58 6.3.3 3D optical profiling of corroded coupon surface ...... 59 6.3.4 FNA’s effect on electrochemical properties of the carbon steel coupons ...... 62 6.3.5 Potential for FNA dosing as a corrosion control technology ...... 67 6.4 Conclusions ...... 69 6.5 Supplemental information ...... 70
Chapter 7 Combined corrosion inhibitory effects of free nitrous acid and imidazoline derivative in a simulated water injection system ...... 77 7.1 Introduction ...... 77 7.2 Materials and methods ...... 77 7.2.1 Experimental procedure ...... 77 7.2.2 Analysis ...... 78 7.3 Results and discussion ...... 78 7.3.1 Reactor performance...... 78 7.3.2 General corrosion by weight-loss measurements ...... 81 7.3.3 3D optical profiling of corroded coupon surface ...... 82 7.3.4 Electrochemical measurements ...... 84 7.3.5 Combined imidazoline derivative and FNA treatment as a potential technology for corrosion control in water injection systems ...... 92
XV 7.4 Conclusions ...... 93 7.5 Supplemental information ...... 95
Chapter 8 Conclusions and future work ...... 105 8.1 Main conclusions of the thesis ...... 105 8.2 Recommendations for future research ...... 107
List of References ...... 110
XVI List of Figures & Tables
Figures
Figure 2–1 Schematic of the water injection process in secondary oil recovery (Gieg et al. 2011). . 6 Figure 2–2 Electrical microbially influenced corrosion (Venzlaff et al. 2013). The electrons are transported via proteins in the outer membrane (OM), periplasm (PP) and cytoplasmic membrane (CM) to be used by the enzymes for sulfate reduction (SR) in the cytoplasm (CP). The enzyme for sulfate activation (SA) and protein for sulfate uptake (SU) allow sulfate to be reduced to sulfide. IB indicates ion bridge...... 9 Figure 2–3 Illustration of five stages of biofilm development (Stoodley et al. 2002)...... 13 Figure 2–4 Schematic illustration of the intersections of nitrogen and sulfur cycles in oil reservoirs (Gieg et al. 2011)...... 17
Figure 2–5 Chemical structures of HNO2...... 23 Figure 2–6 Proton transportation and ATP production in a denitrifying bacterial cell. The electron transport chain generates the proton motive force across the cell membrane, which is then used to generate ATP by the ATP-ase (Zhou et al. 2011)...... 24 Figure 2–7 Illustration of S-nitrosothiols formed by SH groups reacting with FNA (Park 1993). .. 24 Figure 2–8 A model proposed to illustrate PAO1’s response to FNA stress and its survival strategies. The red and green shapes represent the encoding genes “highly” up- or down-regulated, respectively; the blue and purple shapes stand for the encoding genes “moderately” up- or down-regulated, respectively; and the black shapes denote encoding genes with no change with
FNA treatment (Gao et al. 2016a)...... 25 Figure 4–1 The schematic of the simulated water injection system...... 32 Figure 4–2 A 3D profiling image obtained by the 3D optical profiler...... 34 Figure 5–1 SEM images of biofilms on coupons taken on Day 30 (a), Day 60 (b), and Day 130 (c); sulfate and sulfide in reactor effluent (d), and lactate, propionate and acetate in reactor effluent (e) during the course of the experiment. All data reported are from R1...... 38 Figure 5–2 OCP of the carbon steel working electrode in R1...... 39 Figure 5–3 Nyquist (a, b, and c) and Bode (d, e, and f) plots of the carbon steel working electrode in R1. The electrochemical equivalent circuits (g), (h) and (i) were used to fit the EIS data reported
in a & d, b & e, and c & f, respectively...... 40 Figure 5–4 Rp value calculated from linear polarization plots of the carbon steel working electrode in R1...... 43 Figure 5–5 Corrosion rates of the coupons calculated from the weight-loss measurements in R1.
Error bars represent standard errors (n = 3)...... 44
XVII Figure 5–6 Pit depth profile of randomly selected lines (a), and the depth distribution (b) of the coupon surfaces (Figure S5–2) in R1...... 45
Figure S5–1 Carbon steel coupon surface after 130 days’ immersion in R1...... 49 Figure S5–2 3D images of the coupon surfaces on Day 46 (a), Day 90 (b) and Day 130 (c) in R1. 49 Figure S5–3 Photo of the coupon surface after 24 days’ immersion in R1...... 49 Figure S5–4 Volatile fatty acid (VFA) and sulfur species concentration in R2...... 50 Figure S5–5 Volatile fatty acid (VFA) and sulfur species concentration in R3...... 50 Figure S5–6 Nyquist plots of the carbon steel working electrode in R2...... 51 Figure S5–7 Nyquist plots of the carbon steel working electrode in R3...... 52 Figure 6–1 Lactate (a), acetate (b), sulfate (c), sulfide (d), and biofilm ATP (e) concentrations in Experimental and Control reactors, respectively. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment (black arrows) were conducted respectively...... 57 Figure 6–2 General corrosion rate calculated by the weight-loss measurement for coupons taken from Experimental and Control reactors, respectively. At Day 244 and Day 326, the 1st FNA and
2nd FNA treatment (black arrows) were conducted respectively...... 58 Figure 6–3 Pitting corrosion generated from 3D profiling. The cumulative distribution of the pitting depth in Experimental and Control reactors, respectively at Day 183 (a), Day 244 (right before 1st FNA treatment) (b), Day 326 (right before 2nd FNA treatment) (c), Day 370 (d), Day 451 (e), and Day 487 (f). The dashed lines in (a) to (f) show the depth when the cumulative distribution reaches 90%. The pitting depth at 90% cumulative distribution is summarized in (g). The pitting corrosion rates calculated from (g) are shown in (h)...... 60 Figure 6–4 OCP (a), and polarization resistance (Rp) calculated from LPR measurements (b) of the carbon steel working electrode in Experimental and Control reactors. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment (black arrows) were conducted respectively...... 62 Figure 6–5 Nyquist (a, b, & c) and Bode (d, e & f) plots of the carbon steel working electrode in the Experimental reactor. Z’ and Z” represent the real and imaginary parts of the impedance, respectively. |Z| is the absolute impedance indicated by the red arrow, while the phase angle is indicated by the black arrow in the Bode plots (d, e & f). (a) and (d), (b) and (e), and (c) and (f) show the results from Day 237 to Day 258 (D237-D258), Day 270 to Day 329 (D270-D329), and Day 339 to Day 487, respectively. At Day 244 and Day 326, the FNA treatments were applied respectively (legends marked as red), and the plots show the results right before the FNA dosages (D244 & D326). (g) and (h) are the equivalent circuit models used to fit the
measured EIS plots (a-f). Rs is the solution resistance, Rf and Qf are the resistance and
capacitance of the biofilm, respectively. Wo denotes the finite length Warburg (FLW) element...... 63
XVIII Figure S6–1 Fluorescence images of stained live (green) and dead (red) cells on the surface of the coupons in reactor 1 exposed to nitrite concentrations of 50, 100, and 200 mg-N/L at pH 6, leading to FNA concentrations of 0.12, 0.24, and 0.49 mg-N/L, respectively. The exposure was for 3, 6, 12, and 24 h...... 70 Figure S6–2 The viable fraction of biofilm cells assessed by live and dead staining after FNA treatment at concentrations of 0.12, 0.24, and 0.49 mg-N/L, for 3, 6, 12, and 24 h, respectively.
...... 70 Figure S6–3 Nyquist plot of the carbon steel working electrode in the Control reactor from Day 190 to Day 486 (D190 to D486). Z’ and Z” represent the real and imaginary parts of the impedance, respectively...... 71 Figure S6–4 The pH levels in the Control and Experimental reactors...... 71 Figure S6–5 The nitrite concentrations in the Experimental reactor...... 71 Figure S6–6 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Experimental reactor...... 73 Figure S6–7 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control reactor...... 75 Figure 7–1 The molecular structure of HEI-17...... 77 Figure 7–2 Lactate (a), acetate (b), sulfate (c), sulfide (d) concentration, and cellular ATP level (e) in biofilms in Control, Control + HEI-17, and Experimental reactors, respectively. On Day 47, the FNA treatment (black arrow) was conducted in the Experimental reactor...... 79 Figure 7–3 Corrosion rate calculated by the weight - loss measurements in Control, Control + HEI- 17, and Experimental reactors, respectively. On Day 47, FNA treatment (black arrow) was
conducted in the Experimental reactor...... 81 Figure 7–4 Pitting corrosion estimated from 3D profiling. The cumulative distribution of the corrosion depth in Control, Control + HEI-17, and Experimental reactors on Day 30 (a), Day 47 (FNA treatment in Experimental) (b), Day 61 (c), Day 75 (d), Day 90 (e), Day 106 (f), and Day 120 (g). The dashed lines in (a) to (g) show the depth when the cumulative distribution reaches 90%. The 90%-ile pitting corrosion depths are summarized in (h). The averaged pitting corrosion rates after Day 47 (between Day 47 and Day 90, and between Day 90 and Day 120) calculated by the built-in function LINEST in Microsoft Excel from (h) (see regression lines shown in (h)) are shown in (i). Error bars represent the standard deviation of the pitting corrosion rates...... 82 Figure 7–5 OCP (a) and polarization resistance (Rp) calculated by the LPR measurements (b) of the carbon steel working electrode in Experimental and Control. On Day 47, the FNA treatment
(black arrow) was conducted in Experimental...... 84
XIX Figure 7–6 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Experimental. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 70 (D33 to D70), and Day 75 to Day 118 (D75 to D118), respectively. The embedded diagram shows the details of the Nyquist plots on Day 1, Day 24, and Day 28. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz). At Day 47, the FNA
treatment (black arrow) was conducted...... 85 Figure 7–7 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Control + HEI-17. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 75 (D33 to D75), and Day 79 to Day 118 (D79 to D118), respectively. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz)...... 86 Figure 7–8 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Control. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 75 (D33 to D75), and Day 79 to Day 118 (D79 to D118), respectively. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz)...... 86 Figure 7–9 Equivalent circuits used to fit the EIS results in Figure 7–6, Figure 7–7, and Figure 7–8.
Rs is the solution resistance, Rf and Qf are the resistance and capacitance of the biofilm,
respectively. Wo denotes the finite length Warburg (FLW) element...... 88 Figure S7–1 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control reactor...... 97 Figure S7–2 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control + HEI-17 reactor...... 100 Figure S7–3 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Experimental reactor...... 103 Figure S7–4 The theoretical transition of carbon steel surface by FNA treatment in the Experimental reactor. (a) demonstrates the MIC mechanism by SRB. (b) denotes the surface change after FNA treatment. FNA removes the biofilm on the steel surface, and HEI-17 forms a protective layer on the exposed surface (blue line)...... 104
Tables
Table 2–1 Reported mechanisms of MIC by SRB (Beech and Gaylarde 1999, Dominique and Wolfgang 2011, Kakooei et al. 2012)...... 7
XX Table 2–2 List of biocide types used to control MIC (Kelland 2014, Papavinasam 2014, Rossmoore 1995)...... 16 Table 2–3 Nitrite's effect on biogenic souring and MIC in laboratory test...... 18 Table 2–4 Bacteriostatic effects of FNA on microbes...... 20 Table 5–1 Fitting results of EIS tests of carbon steel working electrode in R1 during 142-day experimental study...... 42 Table 5–2 Surface roughness values of carbon steel coupons on Day 46 (a), Day 90 (b) and Day 130 (c) in R1...... 45 Table S5–1 Fitting results of EIS tests of carbon steel working electrode in R2 during 142-day experimental study...... 53 Table S5–2 Fitting results of EIS tests of carbon steel working electrode in R3 during 142-day experimental study...... 54 Table 6–1 Fitting results for the EIS measurements of the Experimental reactor. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment were conducted respectively, and the data show the results right before the FNA dosages...... 65 Table 7–1 Fitting results for the EIS measurements of the Experimental (Figure 7–6). FNA treatment was applied on Day 47 (bold)...... 89 Table 7–2 Fitting results for the EIS measurements of the Control +HEI-17 (Figure 7–7)...... 90 Table 7–3 Fitting results for the EIS measurements of the Control (Figure 7–8)...... 91
XXI List of Abbreviations used in the thesis
AOB Ammonium-oxidizing bacteria
APB Acid-producing bacteria
APS Adenosine-5’- phosphosulfate
ASTM American Society for Testing Material
ATP Adenosine triphosphate
BCSR Biocatalytic cathodic sulfate reduction
CLSM Confocal laser scanning microscope
CM Cytoplasmic membrane
CP Cytoplasm
EIS Electrochemical impedance spectroscopy
EPS Extracellular polymeric substance
FIA Flow injection analyzer
FISH Fluorescence in situ hybridization
FLW Finite length Warburg
FNA Free nitrous acid
GAOs Glycogen-accumulating organisms
HEI-17 N-b-hydroxyethyl oleyl imidazoline
HRT Hydraulic retention time
HPLC High performance liquid chromatography
IB Ion bridge
IC Ion chromatography
IOB Iron-oxidizing bacteria
IRB Iron-reducing bacteria
LPR Linear polarization resistance
Methanogens Methanogenic archaea
XXII MIC Microbially influenced corrosion
MOB Metal-oxidizing bacteria
NOB Nitrite-oxidizing bacteria
OCP Open circuit potential
OM Outer membrane
PAOs Polyphosphate-accumulating organisms
PBS Phosphate buffered saline
PI Propidium iodide
PP Periplasm ppb Parts per billion ppm Parts per million
Ra Arithmetic average roughness
Rq Root mean square roughness
SA Sulfate activation
SAOB Sulfide anti-oxidant buffer
SEM Scanning electron microscopy
SH Sulfhydryl
SRB Sulfate-reducing bacteria
SOB Sulfide-oxidizing bacteria
SU Sulfate uptake
TCA Tricarboxylic acid
THPS Tetrakis hydroxymethyl phosphonium sulfate
UT Ultrasonic treatment
UV Ultraviolet
VFA Volatile fatty acids
XXIII Chapter 1 Introduction
1.1 Background
Oil and gas products are of vital importance for people’s lives in transportation, heating, and electricity fuels, etc. Also, the oil and gas industry is among the largest industries all around the world (Inkpen and Moffett 2011). Petroleum is vital to many other industries and accounts for more than 90% of the world’s mineral trade, which results in the most complex economic issues for the greatest number of countries (Ross 2012). In 2005, 61% of global energy consumption was supplied by crude oil and natural gas (Arunachalam and Fleischer 2008). Total global energy demand is predicted to increase by 50-60% in 2030 than that in 2005, which means an incredibly rising demand for oil and gas, though nuclear and renewable sources may increase in the following decades (Holditch and Chianelli 2008). In 2030, global oil demand will grow to approx. 37.6 to 50.4 billion bbl/year (Papavinasam 2014).
Since most oilfields in the world are in the middle or late recovery period, corrosion has become a skyrocketing problem in the secondary oil recovery, which normally involves water flooding process. Microbially influenced corrosion (MIC) refers to the corrosion caused or enhanced by microbial activities, which has been estimated to be responsible for about 40% of pipeline system failures in the oil industry, while the percentage could be much higher in water injection systems (Graves and Sullivan 1966). MIC could not only be responsible for the localized corrosion and leakage of the pipelines, but also lead to production decrease and potential safety issues. It is reported that 70% to 95% of internal leaks of the pipeline are caused by localized corrosion mainly induced by MIC (AlAbbas 2013).
Generally, sulfate-reducing bacteria (SRB) are believed to be the major bacteria involved in MIC, and have been detected in almost all the streams of the oil industry, including production, transportation, and storage facilities (Hamilton 1985, Rajasekar et al. 2007, Voordouw et al. 1993). In MIC processes, the formation of the corrosive biofilm on the metal surface is the crucial step (Sheng et al. 2007). Biofilm growth is a dynamic process and is largely influenced by the environmental conditions including the surface status, the diffusion limitation, and the shear stress applied by the moving liquid (Wang and Zhang 2010). Gaylarde and Johnston found that preventing the attachment of SRB to a metal surface decreased the corrosion rate (Gaylarde and Johnston 1980). Further studies on the effect of initial attachment of SRB on corrosion failed to reach a consensus since no correlation between the initial number of SRB attached and the subsequent corrosion rate
1 was found (Noor et al. 2012). It was found that a porous biofilm entraps microbes’ metabolites and also creates gradients of pH near substrates in the biofilm, thus forms a protective film in some instances (J.E.G. González 1998).
To uncover the mystery of MIC, not only the traditional, well-characterized SRB cultures (e.g. Desulfovibrio vulgaris) (Javed et al. 2015, Liu et al. 2017b), but also the roles of strains isolated from produced water (Miranda et al. 2006) and the oil sludge (Chen et al. 2017) were studied. Also, SRB was coupled with other bacteria to verify the mixed-cultured biofilm’s effect on corrosion (Batmanghelich et al. 2017, Liu et al. 2015a). However, the MIC development in the early stages of water injection process was barely covered. The water injection process in secondary oil recovery is a dynamic process with continuous flow, which transports ions and short-chain fatty acids through the pipeline (Grace 2013, Papavinasam 2014). In addition, antimicrobials are usually used to inhibit and kill the bacteria in the water before injection, so at the beginning of injection, the pipes are exposed to a water flow with minor bacteria instead of an enriched SRB solution. With ongoing injection, bacteria gradually attach to the internal pipe surface and form the detrimental biofilm on the surface (Eckert and Skovhus 2018).
To address MIC, methods designed to either remove and kill the microbes or change the surface properties to protect the steel are used, but they are usually restricted by the application conditions and often prohibitively expensive (dos Santos et al. 2014, Nemati et al. 2001b). The demand for more innovative, effective and cost-efficient solutions to deal with corrosion is still high.
Recently, free nitrous acid (FNA), i.e. the protonated form of nitrite, has been reported to inhibit, at parts per billion (ppb) levels, a broad range of microbes, becoming biocidal at parts per million (ppm) levels (Jiang et al. 2011b, Vadivelu et al. 2006b, Zhou et al. 2008). Jiang et al found that the biocidal effect on sewage biofilms, which are mainly composed of SRB and methanogens, was due to FNA rather than nitrite (Jiang et al. 2011b). However, the effect of FNA on steel corrosion cannot be directly inferred from previous sewer work due to the more complex nature of metal corrosion. Metal corrosion in the water injection system occurs in liquid phase, rather than in gaseous phase as in a sewer. The corrosion process is influenced by many biological and physicochemical processes. First of all, H2S produced by SRB could directly interact with iron, inducing the anodic reaction of iron dissolution (Bai et al. 2015). SRB could also extract electrons directly from Fe0, especially when associated with low carbon levels, which would cause pitting corrosion underneath the biofilm (Gu et al. 2009, Zhong et al. 2019). Further, the metal acts as an electrode submerged in the electrolyte, thus the corrosion process is also influenced by many other ions, such as chloride, in the medium.
2 In the practical oil production process, the pipelines are usually maintained with mixed chemicals, including corrosion inhibitors and biocides (Heidersbach 2018, Kermani and Chevrot 2012). As widely used corrosion inhibitors in oilfields, imidazoline and its derivatives showed their advantages with high inhibition efficiency, easy production, and low toxicity (Kelland 2014). Although FNA’s effect on MIC needs further investigation, its strong biocidal effect has shown that it could be a potential biocide alternative to conventional biocides in oil production infrastructure. An additional option is the combined use of FNA and corrosion inhibitors to reduce MIC of metal corrosion in water injection systems in oil production.
The overall aim of this PhD thesis is to gain a better understanding of MIC process in secondary oil recovery by analysing the corrosion behaviour of the carbon steel coupons in a simulated water injection system. The thesis further aims to gain a thorough understanding of the potential corrosion inhibition effect of FNA on MIC, which could be applied in practical oilfield operation either alone or combined with other corrosion inhibitory chemicals. This research is expected to provide support for the future MIC control in water injection systems in oil production.
1.2 Organization of the thesis
This thesis is organized into eight chapters.
Chapter 1 generally and briefly introduces the background and the thesis aim and organization.
Chapter 2 reviews the literature on the topic in detail. The current MIC problems in oilfields are covered briefly, which is followed by a comprehensive introduction of the microorganisms involved in MIC and the reported mechanisms. The traditional ways of controlling and mitigating of MIC are summarised, of which nitrate and nitrite as biocides are particularly reviewed to compare with the FNA studies carried out in this thesis. The current laboratory methodologies of MIC analysis are reviewed. At last, the research gaps are highlighted based on the critical literature review.
Chapter 3 identifies the key research objectives.
Chapter 4 describes the methods and materials used in the thesis.
Chapter 5 to 7 present the research outcomes based on the research objectives. Chapter 5 reports the long-term development of MIC on carbon steel in a simulated water injection system. Chapter 6 applies FNA treatment to the simulated water injection system and shows the potential of FNA based technology for corrosion control in secondary oil recovery process. Chapter 7 tests FNA used in
3 combination with a maturely used corrosion inhibitor, hydroxyethyl imidazoline (N-b-hydroxyethyl oleyl imidazoline as a represent).
Chapter 8 summarises the overall conclusions and main achievements of the studies, and recommends future research directions.
4 Chapter 2 Literature Review
This chapter reviews and summarises the findings of the previously published studies which are relevant to the topic. In section 2.1, the overall situation of MIC in oilfields is described and explained. Then the microbes which are highly involved in MIC and the importance of biofilm on corrosion are introduced in section 2.2. Section 2.3 briefly introduces the control and mitigation of MIC, of which nitrate and nitrite application are reviewed more detailly. FNA’s inhibitory and biocidal effects on microbes are illustrated in section 2.4. The current laboratory methodologies of MIC study are introduced in section 2.5. At last, the main research gaps are identified in section 2.6.
2.1 Overview of MIC in the oilfield
Corrosion is a natural phenomenon and is as old as the history of metals, which is the deterioration and degradation of a material because of its interaction with surroundings (Ahmad 2006, Fontana 1986, Garverick 1994, Jones 1996, Speight 2014b). The term, corrosion, was exclusive to metals and alloys up to 1960s, while now it was used to denote all types of natural and man-made materials’ deterioration (Ahmad 2006). The corrosion problem has become one of the headaches since the utility of carbon steel in the oil and gas industry. In America, the loss caused by corrosion is estimated to be $1,372 billion annually in the oil and gas production (Simons 2008). Of onshore oil and gas production sector in the USA, 8% ($320 million) of the annual capital expenditure and 76% ($1.052 billion) of the annual operating expenditure are directly related to corrosion control (Papavinasam 2014).
The most common material, at least 80% (Papavinasam 2014) of equipment and pipes, in oil and gas industry is carbon steel, which could be divided by different grades to meet the requirement of varied strength, fluid temperature and resistance to corrosion (Baron 2010). Also, carbon steel is cheap and widely available (Baron 2010, M.A. Hegazy 2015). According to American Iron and Steel Institute, the main alloying constituent of carbon steel is carbon in the range of 0.12–2.0%, while the maximum content of manganese, silicon and copper are 1.65%, 0.6%, and 0.6% respectively with no specified minimum content. Thus, carbon steel contained at least 95% iron and corrosion is actually the electrochemical reaction of steel with the surroundings.
Four factors are essential for corrosion to take place: anode, cathode, electrolyte, and conductive path, which make a corrosion cell (Ahmad 2006). Carbon steel itself could be 3 factors (anode, cathode, and conductive path) for a corrosion cell, so once it comes into contact with an electrolyte, corrosion will gradually occur. Both cathodic and anodic reaction occurs simultaneously on the corroding metal
5 surface. Different forms of corrosion are taking place in oil pipelines, such as general or uniform, crevice, galvanic, intergranular, pitting, erosion-corrosion, selective leaching, and stress cracking corrosion.
MIC refers to the corrosion caused or enhanced by microbial activities, which is ubiquitous in water flooding recovery (Graves and Sullivan 1966). At the first stage of oil production, the pressure conserved in the reservoir lifts the oil to the ground. Gradually, the pressure decreases and the pushing force is not strong enough. At this stage, water will be injected into the oil reservoir to maintain the pressure and also push oil to the oil well, which is called secondary oil recovery. The water source can be local aquifer, processed water source (e.g., sewage treatment plant output) or surface source (e.g., river) for onshore injection, and seawater for offshore flooding (Gieg et al. 2011).
Produced water is the water pushed to the surface with oil, which is believed to be the largest waste effluent in the oil industry (Shpiner et al. 2009). As oilfields mature, they produce increasing quantities of water, which will boost microorganisms' growth. Due to the high discharge standard and cost, produced water is usually reinjected. Figure 2–1 shows the water injection process with produced water re-injection. 'Zone of influence' is the part which is affected mostly by water injection. This is due to the lower temperature caused by the injection of the cold water. During operation, the producing well can be changed to an injection well, while injection well can be switched to producing well, thus making the 'Zone of influence' more extensive.
Figure 2–1 Schematic of the water injection process in secondary oil recovery (Gieg et al. 2011).
As aforementioned, MIC is caused or influenced by microorganisms, but it is still an electrochemical process in essence (Javaherdashti 2008). Microbes can affect the extent, severity, and course of corrosion. To initiate MIC, a carbon source, an energy source, an electron acceptor, an electron donator, and water must be present besides the microorganisms.
6 MIC is not considered a new form of corrosion; however, it mostly results in localized corrosion which could be the joint form of pitting, cracking, crevice corrosion, enhanced erosion-corrosion, under-deposit corrosion, and dealloying (Little et al. 2007). In an oil reservoir, there is a complex microbial community with all possible microbial reactions happening in sequence or simultaneously. In the following section, the main corrosion-related bacteria are presented.
2.2 Microorganisms and biofilm in MIC
2.2.1 Sulfate-reducing bacteria (SRB)
Oxygen is always removed from the injection water by using oxygen scavengers to control the oxidation of steel. However, this can be the cradle for the anaerobic bacteria, SRB, which is the most significant microorganism for MIC in the oilfield. Instead of using oxygen as the electron acceptor, SRB uses sulfate as an alternative with the consequent production of sulfide (Javaherdashti 1999). SRB are ubiquitous, from more than 70 meters deep in clay to seawater (Miller 1970, Obuekwe et al. 1981b). It can thrive in the pH range of 4.0 to 9.5 (Barton and Tomei 1995) and survive under pressures up to 500 atmospheres (Stott 1988). There are many different mechanisms of the MIC by SRB proposed by the researchers, which are listed in Table 2–1.
Table 2–1 Reported mechanisms of MIC by SRB (Beech and Gaylarde 1999, Dominique and Wolfgang 2011, Kakooei et al. 2012).
Corrosive process/substance Main mechanisms
Cathodic depolarization by hydrogenase Consumption of the cathodic product, atomic hydrogen, by (Wolzogen Kuhr and van der Vlugt 1934) hydrogenase
Depolarization by iron sulfide (King et al. Formation of an iron/iron sulfide galvanic cell, cathodic 1973) reduction of molecular hydrogen on iron sulfide side
Depolarization by hydrogen sulfide (Costello Cathodic reduction of microbially produced hydrogen − − 1974) sulfide: H2S + e →HS + H2
Elemental sulfur (Schaschl 1980) A concentration cell formed with elemental sulfur acting as the reactant
Iverson's mechanism (Iverson 1983) A volatile and corrosive iron phosphite metabolite produced
Acidification of anode (Crolet 1992, Daumas et Formation of iron sulfide corrosion products: Fe2+ + HS− → al. 1988) FeS + H+
Biocatalytic cathodic sulfate reduction (BCSR) Direct electron uptake from metal surfaces (Gu et al. 2009)
7 2.2.1.1 The classical theory: cathodic depolarization
According to cathodic depolarization theory, SRB can consume the cathodic hydrogen by hydrogenase (Stott et al. 1988). The main probable effect of SRB on corroding metal was postulated that the oxidization of the molecular hydrogen generated at the cathodic sites on the metal surface, accelerating the cathodic polarization. The governing chemical and electrochemical reactions of this mechanism are as follows (Javaherdashti 2008):
Anodic reaction: 4Fe ® 4Fe2+ + 8e- (2-1)
+ - Cathodic reactions: 8H2O ® 8H + 8OH (2-2)
+ - 8H + 8e ® 8Hads (2-3)
2- + - 2- SRB depolarization: SO4 + 8H + 8e ® S + 4H2O (2-4)
Corrosion products: Fe2+ + S2- ® FeS (2-5)
2+ - 3Fe + 6OH ® 3Fe(OH)2 (2-6)
2- - Overall reaction: 4Fe + SO4 + 4H2O ® 3Fe(OH)2 + FeS + 2OH (2-7)
Although this theory was for the first time to explain MIC by SRB in a chemical and electrochemical way, it has been found to have many flaws as follows:
• This theory could not explain the corrosion caused by hydrogenase-negative strains (Little et al. 2000). Also, It has been confirmed that it is impossible for hydrogenase to work on atomic hydrogen (Stott 1993, Venzlaff et al. 2013).
• The effect of H2S and corrosive metabolites is not addressed in this theory (Dominique and Wolfgang 2011). • A species of nitrate-reducing SRB was reported to be able to grow by consuming hydrogen more efficiently. After replacing the sulfate by nitrate in the medium, this kind of bacteria efficiently oxidized hydrogen from the metal, but they didn’t enhance corrosion like sulfate- reducing bacteria cultures. This phenomenon proved that the uptake of cathodic hydrogen wasn’t the mere reason for MIC induced by SRB (Javaherdashti 2008).
2.2.1.2 Biocatalytic cathodic sulfate reduction (BCSR)
8 The shortcomings described above promoted many researchers to do more work on explaining MIC by SRB. As listed in Table 2–1, despite the theories' similarities and dissimilarities, the role of SRB in corrosion has become less important except the latest proposed BCSR theory. This theory assumes that MIC by SRB occurs in two overall steps: first, the anodic iron’s dissolution releases electrons, and then, SRB assimilated the electrons and utilized for sulfate reduction within the cells’ cytoplasm. This process was regarded to be biocatalytic. The theory builds on a corrosive SRB biofilm on steel surface, and the biofilm is believed to be directly responsible for MIC and maintains the BCSR reaction by biofilm biocatalysis.
Till now, there have been many reports about SRB species' direct electron obtain from a metal surface. Novel marine, corrosive types of SRB have been found to be capable of using metallic iron as the sole electron donor (Dinh et al. 2004). When SRB biofilm forms on a metal surface, there will be parts underneath the biofilm that lack organic carbon source, which could force SRB to turn to elemental iron for electrons (Xu and Gu 2014). In carbon dioxide/bicarbonate-buffered medium, SRB was directly enriched with metallic iron and sulfate with no other growth substrates successfully (Hang 2003). A new SRB species, Desulfobacterium corrodens, were isolated from the culture. They were rod-shaped and very closely related to Desulfobacterium catecholicum genetically, but were very different physiologically (Javaherdashti 2008). Venzlaff et al. (Venzlaff et al. 2013) have proposed a simplified microbiological model of SRB’s direct electron uptake as shown in Figure 2– 2.
Figure 2–2 Electrical microbially influenced corrosion (Venzlaff et al. 2013). The electrons are transported via proteins in the outer membrane (OM), periplasm (PP) and cytoplasmic membrane (CM) to be used by the enzymes for sulfate reduction (SR) in the cytoplasm (CP). The enzyme for sulfate activation (SA) and protein for sulfate uptake (SU) allow sulfate to be reduced to sulfide. IB indicates ion bridge.
9 2.2.2 Iron-reducing bacteria (IRB)
Metal-reducing bacteria's effect on metals such as nickel, copper, gold, and silver could date back to over 50 years ago (Simpson 1999). Similarly, iron reducers derive benefit by using Fe3+ as a terminal electron acceptor in their metabolism (Videla et al. 2008). These organisms are usually facultative anaerobes which turn to a reducible ion instead of oxygen as a terminal electron acceptor under anaerobiosis (Little and Lee 2014). IRB prefer neutral pH and have been found in cold temperature and under mesophilic and thermophilic conditions.
IRB's role in biocorrosion is difficult to identify because of its metabolic diversity and environmental requirements. Both corrosive and inhibitory effects have been reported. Evidence for IRB's corrosive effect has been assumed as follows: First, IRB reduces the generally insoluble Fe3+ compounds, a ferric oxide protective layer, to the soluble Fe2+, exposing the metal to the corrosive environment (Obuekwe et al. 1981a). After that, the concentration cells form inside the biofilm (Dubiel et al. 2002, Videla and Herrera 2009). Javaherdashti et al. (Javaherdashti et al. 2006) reported that mild steel in a medium with a culture of IRB suffered a higher corrosion rate than in abiotic conditions, showing IRB’s corrosion enhancement.
In contrast, IRB was shown to be capable to inhibit corrosion as it consumes oxygen via their aerobic respiration. A laboratory study by Lee et al. (Little et al. 1997) found short-term protection of biofilms composed of mix culture of IRB and SRB. Based on these studies’ contradictory results, it is not certain that under real-world environments, IRB will prevent or promote corrosion.
2.2.3 Acid-producing bacteria (APB)
APB are capable of producing inorganic or organic acids by metabolizing organic compounds and even CO2. Acidophilic sulfur oxidizing bacteria, such as Thiobacillus spp., can oxidize sulfide and sulfur to sulfate (Beech and Gaylarde 1999). Acetic acid produced by Acetobacter aceti was reported to accelerate stainless steels corrosion by destroying the protective calcareous film formed via cathodic polarization (Little et al. 1988). The pH underneath an APB biofilm can be considerably lower than pH 7 due to organic acid production. Because organic acids are normally weak acids, which means their concentrations are much higher than the protons' they release. Thus, to reach the same pH level with inorganic acids, organic acids are more corrosive. Clostridia and Butyribacteria were reported to be the major causes of internal corrosion of natural gas pipeline (Dias and Bromel 1990). Generally, the produced organic acids can increase corrosion in 4 ways, including binding metal ions, the provision of additional cathodic reactants, destruction of the passive film, and preventing passivation that accelerates the metal dissolution collectively (Sand 1997).
10 2.2.4 Methanogenic archaea (Methanogens)
Methanogens are anaerobes that live in moderate environments where they are nutritionally associated with syntrophic and fermentative H2-producing microorganisms (Archer and Harris 1985, Whitman et al. 1992). There are 3 major groups of methanogens according to their nutritional properties: hydrogenotrophic (using H2, formate, or other alcohols), methylotrophic (metabolizing C1-compounds with methyl groups) and acetoclastic (utilizing mainly acetate) (Garcia et al. 2000, Whitman et al. 1992). However, the boundary lines are not clear between these 3 groups. In comparison to the other reactions, reduction of CO2 to CH4 with H2 as an electron donor is the most energetically favorable reaction under standard condition (Deppenmeier et al. 1996, Garcia et al. 2000).
- + 0 4H2 + HCO3 + H ® CH4 + 3H2O DG ' = -135.5 kJ/mol CH4 (2-8)
When sulfate is absent, hydrogenotrophic methanogens can enhance MIC by catalyzing 4Fe + 5H+ +
− 5HCO3 → CH4 + 4FeCO3 + 3H2O (Dinh et al. 2004, Uchiyama et al. 2010). There are reports about methanogens growing and producing methane in medium containing iron as the only source of electrons (Daniels et al. 1987, Hang 2003).
Recently, more research has been done on methanogens' effect in MIC, which apparently, has been overlooked. Taku Uchiyama and colleagues (Uchiyama et al. 2010) showed that methanogens are another major MIC microbes by isolating Methanococcus maripaludis strain KA1 from the sludge of an oil storage tank which can enhance the corrosion rate of iron almost 10-fold, while in the previous findings, M. thermolithotrophicus could increase the corrosion rate by, at most, 2-fold (Boopathy and Daniels 1991). It is showed that methanogens played an important role to biocorrosion in environments with or without SRB (Boopathy and Daniels 1991). However, SRB usually outcompete methanogens with suitable electron donors and sulfate in the environment (Zhang et al. 2003). Okoro et al. (Okoro et al. 2016) used a sulfate-free medium in the corrosion tests and the methane productions and corrosion rates were found to be strongly correlated.
2.2.5 Metal-oxidizing bacteria (MOB)
MOB are microaerophilic that needs minimal oxygen to survive. They can be classified into 3 categories (Ghiorse 1984): microbes that work as a catalyst in the oxidation of metals, microbes that accumulate abiotically oxidized metal precipitates, and others that derive energy through the oxidation of metals. Iron-oxidizing bacteria (IOB, e.g., Gallionella and Leptothrix) can oxidize Fe2+, either in ions form dissolved in the medium or in precipitation, to Fe3+ (Emerson and Moyer 1997).
11 The rate of ferrous ions’ oxidation to ferric ions can be hundreds of time higher than the abiotic chemical oxidation reaction under biocatalysis by IOB (Liu et al. 2016a, Starosvetsky et al. 2008). Therefore, iron/manganese-precipitating and iron/manganese-oxidizing bacteria are among the most corrosive microorganisms (Moradi et al. 2011). Conditions that are favorable for localized corrosion are created by their metabolism, particularly in cases of metal passivity.
2.2.6 Microbial consortia
There is almost no possibility to find pure microorganisms’ species in nature, so the study of microbial consortia’s role is becoming increasingly essential to the understanding of MIC (Beech and Gaylarde 1999).
IRB are often found to exist with SRB on the metal surfaces, which have been the evidence for the assumption that oxygen consumption by IRB promotes the growth of SRB by creating redox conditions (Videla and Characklis 1992). In a simulated produced water system with an initial DO of 4.2 mg/L, the mixture of SRB (Desulfotomaculum nigrificans) and IOB (Pseudomonas sp.) caused more severe pitting corrosion, while the case with only SRB had a higher weight loss (Liu et al. 2015a).
SRB has been speculated to proliferate at spots of corrosion due to APB’s metabolisms (Soracco et al. 1988). The acids produced by APB can be used by SRB and methanogens for nutrients. The corrosion of C1020 pipeline steel in medium with or without mixed SRB populations (Desulfovibrio sp. and Desulfobacter spp.) and the acetogenic bacterium (Eubacterium limosum) was compared (Dowling et al. 1992). Little effect on corrosion rate was shown with E. limosum alone, but a significantly higher corrosion rate was recognized when incubated with the Desulfovibrio sp.. It was speculated that E. limosum can promote Desulfovibrio sp. growth and sulfide production with certain by-products.
2.2.7 Development of biofilm
A biofilm is a population or community of microorganisms living in organized structures attached to surfaces, inter-surfaces, which are embedded in a matrix of extracellular polymeric substances (EPS) of microbial origin (ASTM 2013, Davies 2003). As aforementioned, the anaerobic corrosive biofilm in the seawater injection system is comprised of multiple synergistic microbial species, where each bacterium forms a specific microenvironment, which is determined by surrounding cells, the nutrients and the EPS matrix (Stoodley et al. 2002). Sessile cells (microbes in biofilm) share the same
12 genotypes with planktonic ones (cells in bulk solution), but their phenotypes may be different (Donlan 2002).
Biofilm growth is a dynamic process and can be largely influenced by environmental conditions including the shear stress of the bulk liquid and the substrate diffusion limitation (Wang and Zhang 2010). The biofilm structure is mainly determined by the production of slime matrix of EPS, which is normally composed of polysaccharides, proteins, and nucleic acids and provides the structural support for the biofilm (Flemming et al. 2000). EPS can promote the colonization process on the surface by attaching negatively charged bacteria such as SRB to the charged surfaces.
Figure 2–3 shows the dynamic five-stage development of biofilm. The first stage is the cells’ initial attachment to the surface. In the oil production process, the crude oil and other organic compounds can attach to the steel, working as conditioners on the surface. They can change the physiochemical properties of the interface, including electrical charge and the hydrophobicity (Papavinasam 2014, Speight 2014a). Stage two is the irreversible attachment by the production of EPS, which happens after the reversible attachment in the first stage, to develop a mature biofilm and maintain contact with the substratum. The third stage is the early development of biofilm architecture, while it matures in stage four, forming a complex architecture, with pores, channels, and redistribution of bacteria away from the substratum (Davies et al. 1998). Stage five is the detachment of single cells from the biofilm, which will return to the planktonic mode of growth and may start from stage one by attaching to a new surface.
Figure 2–3 Illustration of five stages of biofilm development (Stoodley et al. 2002).
2.3 Control and mitigation of MIC
The control and mitigation of MIC are key to maintaining the integrity of a pipeline to both achieve its design life and, where required, allow life extension. The mitigation process is based on 13 technologies slowing or stopping corrosion in the pipeline (Kermani and Chevrot 2012). The MIC control and mitigation methods used in oilfields will be reviewed in this section.
2.3.1 Mechanical cleaning
Mechanical cleaning refers to physical methods that remove deposits formed on internal pipeline surfaces, such as pigging. Pipeline pigs are devices that are placed inside the pipe and traverse the pipeline to clean and inspect internally (Javaherdashti 2008). Pigs could be divided into 3 types by functionality: cleaning pigs, sealing pigs, and inline inspection (Papavinasam 2014). Water and solids in the crude-oil pipelines sometimes accumulate in low spots, so they are routinely pigged to avoid the formation of corrosion cells (Videla 2002). This operation is especially necessary when flow velocities are less than 0.91 m/s (Papavinasam 2014). Multiphase pipelines probably have to be pigged more frequently to reduce liquid holdup and minimize the slug volumes which are generated by the system. The chemical and microbiological characterisation of solids removed by pigs are helpful in defining the corrosion threat (Kermani and Chevrot 2012). Mechanical cleaning is usually used in conjunction with biocides addition in the water treatment program to achieve successful removal of microorganisms involved in MIC (Videla 2002).
2.3.2 Cathodic protection
Cathodic protection is used mainly to mitigate external corrosion on buried or submarine pipelines, but also there are reports of cathodic protection being used to control internal corrosion. This can be achieved in two ways: sacrificial anodes by connecting the material to a more active metal, or impressed current systems to apply a high enough direct current to protect the material. Cathodic protection was regarded to be effective on MIC inhibition as it can increase local pH at metal surface, inhibiting corrosive microorganisms’ activities (Javaherdashti 2008).
2.3.3 Coating
The protective coating is to produce a barrier which will isolate the surface from the environment to mitigate corrosion (Speight 2014b). In certain circumstances, such as water injection lines, coating systems are chosen for corrosion control and also to reduce the quantity of the solid corrosion product. The coating may fail to maintain the pipeline integrity as no ideal coating material exists for the complex production environment (Prasai et al. 2012).
14 2.3.4 Corrosion inhibitors
The corrosion inhibitors are chemicals that decrease or prevent corrosion when added in small concentrations, usually at a batch treatment of inhibitor at 1% to 20% or a dose in the range of 10 to 1000 ppm continuously (Raja and Sethuraman 2008). The type and amount of corrosion inhibitor required depend on system corrosivity. The use of a corrosion inhibitor can achieve a corrosion rate typically less than 0.1 mm/year in most cases.
Good corrosion inhibitors usually have three basic structural requirements: an anchoring group, a backbone, and a substituent group. The anchoring groups are functional for the corrosion inhibitors’ attachment onto the metal surface. The anchoring groups generally contain one or more heteroatoms, such as sulfur (S), nitrogen (N), oxygen (O), or phosphorous (P) (Kelland 2014). The bonding strength and surface coverage of the anchoring group to the metal can be enhanced by the backbone which normally contains additional substituent groups. In the oil and gas industry, the common corrosion inhibitors used are usually surface-active, polar, organic molecules, of which most are amines or amine salts derivatives (Fink 2015). They are normally capable to interact with several interfaces, including metal-oil, metal-water, oil-water, oil-gas, water-gas, and metal-gas.
Imidazoline based corrosion inhibitors have been widely used for the protection of oilfield pipelines for many years, though the inhibitory mechanism hasn’t been fully explained (Wang et al. 2011). The inhibition efficiency of 0.08 mmol/dm3 1-(2-thioureidoethyl)-2-alkyl imidazoline was tested to be 88.6% after 72 h immersion (Wang et al. 2011). 20 ppm hydroxyethyl could inhibit corrosion of 1018 carbon steel by 87.2% when exposed to 3% NaCl medium at 50 °C (Villamizar et al. 2007).
2.3.5 Ultrasonic treatment (UT)
Powerful UT is another method of inhibiting MIC, of which the mechanism is regarded to be due to the cavitation bubbles produced by the acoustic pressure in the liquid (Pound et al. 2005). High temperatures (thousands of degrees) and intense pressures (hundreds of atmospheres) generate locally when the cavitation bubbles collapse, which destroy the microbial cells. Furthermore, chemicals such as H2O2 and hydroxyl radicals are formed, making the environment hostile to microorganisms.
The key to the successful MIC inhibition of UT is the generation of enough cavitational forces that kill or deactivate corrosive microbes to ensure high inhibitory efficiency of MIC (Flemming et al. 1996). However, UT may also destroy the material itself along with the biofilm destruction, so its application is restricted to certain surfaces (Flemming 1990).
15 2.3.6 Biocides
Most of the biocides usage in the oil industry is in raw seawater injection projects used to maintain pressure and enhance oil recovery. Biocides are chemicals that are effective, at relatively low concentrations (tens to thousands of ppm), in killing bacteria and minimizing bacterial growth (Keasler et al. 2011). Generally, biocides can be divided into two categories: 1) oxidizing biocides that penetrate and destroy cells, and 2) non-oxidizing biocides which damage the cell membrane or destroy the biological energy processing mechanisms (Given et al. 1997). The commonly used oxidizing biocides include chlorine, bromine, ozone, chlorine dioxide, and so on. These chemicals need to be carefully selected in practical application, because they may interact with other chemicals, such as corrosion inhibitors, or initiate corrosion of structural materials (Turkiewicz et al. 2013). The main biocides used in the oil industry are shown in Table 2–2.
Table 2–2 List of biocide types used to control MIC (Kelland 2014, Papavinasam 2014, Rossmoore 1995).
Types Biocides Properties Oxidizing Chlorine Effective against bacteria and algae; oxidizing; pH-dependent
Bromine Effective against bacteria and algae; oxidizing; wide pH range
Ozone Effective against bacteria and biofilms; oxidizing; pH- dependent
Chlorine dioxide (ClO2) Effective against bacteria, in a lesser extent against fungi and algae; oxidizing; pH-independent Non- Amine Quarternary ammonium Formation of an electrostatic bond to affect permeability and oxidizing compounds protein denaturing Aldehyde Formaldehyde Low-cost, effective to kill biofilm
Glutaraldehyde Reacts with basic constituents of proteins, such as -NH2, -OH, -SH and -COOH, in cell walls, cell membranes and cytoplasm
Acrolein Strong biocide; H2S scavenger; iron sulfide dissolvant
Sulfur- Methylene bis Blocking the transfer of electrons in microorganisms; containing thiocyanate (MBT) expensive; high concentration when used by itself
isothiazolone Blocking food transport through the cell wall; inhibiting microbial respiration Other THPS Collapse of cell membranes (cell lysis); causing cross-linking of proteins
2,2-dibromo-3- pH sensitive; instable in water; kills fast and degrades quickly nitrilopropioamide to ammonia and bromide ions (DBNPA)
16 Non-oxidizing biocides, such as aldehydes, quaternary phosphonium compounds, and quaternary ammonium surfactants, are less prone to cause corrosion than oxidizing biocides (Kelland 2014). Glutaraldehyde is the most commonly used commercial biocide, which has advantages over other biocides as it can be used in a wide range of temperatures and pH (Turkiewicz et al. 2013). It could cause 5-log kill on biofilm at 1000 ppm after 2-hour treatment, or 100 ppm after 24-hour treatment (Keasler et al. 2011). THPS (tetrakis hydroxymethyl phosphonium sulfate) is also maturely used in oilfield operations, which has low toxicity and dissolves iron sulfide in pipelines (McIlwaine 2005, Turkiewicz et al. 2013).
2.3.7 Nitrate and nitrite
Nitrate and nitrite don't belong to any of the above types, but they are sometimes classified as biostats, which do not actually kill bacteria but interfere with their metabolic processes to control their growth (Kelland 2014).
Nitrate injection has been tested excessively in laboratory tests and applied maturely to inhibit sulfate- reducing bacteria (SRB) and control biogenic souring in oilfields (Hubert et al. 2003, Larsen et al. 2004, Nemati et al. 2001b, Telang et al. 1997, Voordouw 2011). Figure 2–4 shows the basic mechanism of nitrate and nitrite inhibition, uncovering a complex net of biochemical, biological, and abiotic interactions (Gieg et al. 2011). Nitrate is preferably utilized as electron acceptor due to its higher oxidation potential compared to sulfate. Therefore, some SRB tend to shift from sulfate to nitrate reduction (Hubert and Voordouw 2007, Korenblum et al. 2010). Nitrate can also serve as the terminal electron acceptor for nitrate-reducing bacteria (NRB), which can be inhibitory by competing with SRB for carbon sources (Marques et al. 2012). However, contradictory effects have been found by nitrate dosage on carbon steel: it may either reduce corrosion rates (Dunsmore et al. 2004) or promote higher corrosion rates (Nemati et al. 2001a).
Figure 2–4 Schematic illustration of the intersections of nitrogen and sulfur cycles in oil reservoirs (Gieg et al. 2011). 17 It was in 1996 that for the first time nitrite was found to be effective in controlling biogenic souring problem in a crushed sandstone column at 60 °C (Reinsel et al. 1996). A possible stoichiometry for nitrite scavenging hydrogen sulfide has been proposed (see equation 2-9). Nitrite inhibits SRB by acting as a competitive inhibitor of the terminal enzyme in the sulfate reduction pathway, dissimilatory sulfide reductase (Dsr) (Kelland 2014). Dsr has a higher affinity for nitrite than sulfide, which allows nitrite to inhibit SRB reducing sulfate under physiological conditions. Nitrite injection has been conducted into a sour oil well, a sour gas well and an oil dehydrator by P.J. Sturman and colleagues (Sturman et al. 1999), and the result showed Nitrite had a significant and immediate reduction effect on aqueous- and gas-phase H2S. Besides this oilfield trail, laboratory tests of nitrite's effect on biogenic souring and MIC were summarized in Table 2–3.
- - + 0 3H2S + 3HS + 4NO2 + 7H ® 0.75S8 + 2N2 + 8H2O (2-9)
Table 2–3 Nitrite's effect on biogenic souring and MIC in laboratory test.
Experimental Design Nitrite addition Inhibition Corrosion rate Reference remarks Batch / Inoculum Medium Bioreactor set-up Sand-packed Produced Saline for 0.71 mM over 4 No sulfide NA (Reinsel et upflow water growth of hours production al. 1996) bioreactor; marine SRB 60 °C; pH 0.86 mM < 0.15mM sulfide NA 6.5-7.0 continuously produced Batch tests; Pure SRB Saline 0.1 mM No sulfide NA (Nemati et 30 °C; pH 7.0 Desulfovibrio Postgate’s production al. 2001c) sp. strain medium C Produced Synthetic 4 mM Strongly NA water brine suppressed the activity of SRB Sand-packed Produced Modified 22 mM Complete sulfide NA (Hubert et upflow water synthetic continuously removal al. 2003) bioreactor; brine 22 °C Sand-packed Produced Modified 20 mM Complete sulfide Almost no (Hubert et upflow water synthetic continuously removal weight al. 2005) bioreactor brine loss/corrosion with carbon steel Gradually added Low sulfide and Corrosion rate coupons in from 4 mM to 20 low nitrite was lowered at mM and remained concentration the bottom, but sand; 22 °C 20 mM for 67 increased at the days top of the bioreactors Sand-packed Produced Modified 0.1 mM No sulfide NA (Kaster et upflow water seawater production al. 2007) bioreactor; 60 °C; pH 7.2
18 2.4 Inhibitory and biocidal effects of FNA on microbes
As aforementioned, in the past few decades, increasing evidence has shown that nitrite is inhibitory to the metabolism of a variety of key microorganisms in wastewater treatment. However, since last decade, FNA, the protonated form of nitrite, rather than nitrite itself, has been proved to be the actual cause to inhibit microorganisms. This section will review on FNA's inhibitory and biocidal effect. First of all, FNA's concentration is determined through the following equation 2-10 (Anthonisen et al. 1976a):
- pH FNA as HNO2-N=NO2 -N/(Ka´10 ) (2-10) where Ka is the ionization constant of the nitrous acid, Ka=e-2300/(270+T(°C))
2.4.1 Inhibitory effect of FNA on microbes
Recently, FNA was found to be a metabolic inhibitor to a broad range of microbes at ppb levels and a strong biocidal agent at ppm levels under both aerobic and anaerobic conditions (Table 2–4).
Intermittent FNA dosing for the control of sulfide and methane productions in sewers is more economical compared to the other widely used chemicals (Jiang et al. 2011a). The estimated cost of FNA dosage to reach 80% control of sulfide production is or $2.10 /kg-S , much less than that of
$2.736-10.368 /kg-S using other chemicals including oxygen, hydrogen peroxide, NaClO, FeCl3 and
FeSO4 as well as other possible biological sulfide control in sewers (Zhang et al. 2008). Furthermore, FNA dosage suppressed methane generation. In the application of FNA for sewer biofilm control, so far adaptation and resistance have not been detected (Jiang and Yuan 2013).
19 Table 2–4 Bacteriostatic effects of FNA on microbes.
Types of Culture/System/Organism FNA (mg HNO2-N/L) Inhibition (%)/Remarks References microbes NOB Activated sludge 0.22-2.8 Threshold for inhibitory initiation (Anthonisen et al. 1976) 0.011 Threshold for anabolic process inhibition Enriched Nitrobacter culture NOB 0.023 100% inhibition on biosynthesis (Vadivelu et al. 2006b) (73%) 0.05 No inhibition on catabolism AOB Nitrosomonas Europaea 1.72 50% inhibition on ammonia monooxygenase activity (Stein and Arp 1998) 0.10 Inhibition threshold for anabolism Enriched Nitrosomonas culture AOB 0.40 100% inhibition on anabolism (Vadivelu et al. 2007) (82%) 0.50-0.63 50% inhibition on catabolism Pure culture: Pseudomonas Inhibition threshold for cell growth, but not for nitrate and nitrite Denitrifier 0.066 (Almeida et al. 1995) fluorescens reduction, and carbon source consumption 0.01-0.025 40% inhibition on nitrate reduction Denitrifier Activated sludge (Ma et al. 2010) 0.2 100% inhibition on nitrate reduction Enriched Accumulibacter culture PAOs 0.02 100% inhibition on P-uptake, 40% inhibition on denitrification (Zhou et al. 2007) (86%)
PAOs Denitrifying P-removal culture 0.0007-0.001 50% inhibition on N2O reduction (Zhou et al. 2008) 0.01 50% inhibition on P-uptake Denitrifying P-removal culture; 0.037 100% inhibition on P-uptake PAOs (Zhou et al. 2010) Accumulibacter accounts for 40% 0.02 60% inhibition on glycogen production 0.02-0.07 40% inhibition on PHA degradation 0.0005 50% inhibition on anabolism Enriched Accumulibacter culture PAOs 0.006 100% inhibition on anabolism (Pijuan et al. 2010) (90%) 0.002-0.01 50%-60% inhibition on catabolism
20 Enriched Competibacter culture GAOs 0.0015 50% inhibition on cell growth (Ye et al. 2010) (90%) PAOs and A stronger inhibitory effect of FNA on the anaerobic metabolism EBPR system 0.0022 (Maximum) (Ye et al. 2012) GAOs of PAOs than GAOs. Reduce average sulfide production by >80% dosed with FNA for Lab scale sewer biofilm system 0.26 Biofilm 12h every 5 days (Jiang et al. 2011b) fed with real wastewater 0.09 Inhibition threshold for the methane generation after 6h exposure Cause 1-log (90%) biofilm inactivation with an exposure time 0.2-0.3 longer than 6h Biofilm Anaerobic wastewater system (Jiang and Yuan 2013) ≥0.2 with 30 mg/L Cause 2-log (99%) biofilm inactivation with H2O2 at 30 mg/L and
H2O2 exposure time more than 6h Sulfide concentration recovered slowly after FNA dosage for 8h, 0.26 with 20% recovery level after 10 days Biofilm Field trials in real sewers (Jiang et al. 2013) 0.26 with 60 mg/L Sulfide concentration recovery is similar to only FNA dosage, but
H2O2 is not economical
NOB: Nitrite-oxidizing bacteria
AOB: Ammonium-oxidizing bacteria
PAOs: polyphosphate-accumulating organisms
GAOs: glycogen-accumulating organisms
21 2.4.2 Biocidal effect of FNA on microbes
The biocidal effects of FNA are sparsely reported so far and mainly distributed in the field of medical research. Phillips et al. found an approximately 100% killing of M.ulcerans at an FNA concentration of 4.3 mg HNO2-N/L was achieved over a 10 min exposure time (Phillips et al. 2004). They further proved that the killing was not simply due to the acid or nitrite environment, but also the action of FNA. Later, Yoon et al. (Yoon et al. 2006) claimed that at an FNA concentration of around 0.17 mg N/L, mucoid, mutants of Pseudomonas aeruginosa could be completely killed in 2 days.
Compared with medical research, reports related to the biocidal effect of FNA on microbes functioning in wastewater treatment are few. Jiang et al. found the biocidal effect on anaerobic, mixed culture biofilms on real sewage were due to the FNA rather than nitrite and pH levels (Jiang et al. 2011). A new strategy for the control of sulfide production in rising main sewers in Australia was developed. This demonstrated that intermittent dosing of FNA or FNA in combination with H2O2 was a cost-effective method for sulfide control.
Experiments have been conducted to assess the capability of FNA to improve the biodegradability of secondary sludge (Pijuan et al. 2012). In this study, ninety percent of the FNA treated biomass were consumed during 14-day digestion, while the percentage was much lower with the untreated biomass (41%). Following on from that, to achieve sludge reduction, Wang et al. verified a novel strategy based on FNA treatment (Wang et al. 2013b). Furthermore, Wang et al. demonstrated that FNA-based pre-treatment could enhance methane production from waste activated sludge (Wang et al. 2013a).
These research discoveries implicate great potential for use of FNA, as a potential antimicrobial agent, could be a promising methodology to be implemented in the control and mitigation of biological souring and MIC in oil production process.
2.4.3 Mechanisms for FNA inhibitory and biocidal effects
Despite there have been many pieces of evidences of the antimicrobial nature of acidified nitrite and applications in clinical treatments and bioengineering are emerging, up to date, the detailed mechanisms of acidified nitrite's effects on microbes are not yet clearly elucidated. Experimental data has shown acidified nitrite can possibly affect a variety of different metabolic processes, including the oxygen uptake, active transport of substrates across the cell membrane, and the oxidative phosphorylation (Zhou et al. 2011).
22 To uncover the mystery of FNA's effect, its biochemical properties should be understood at first.
HNO2 (Figure 2–5) is a weak and monobasic acid (pKa 3.37) known only in solution and mostly stable at pH ≥ 5.
Figure 2–5 Chemical structures of HNO2.
In acidic conditions, HNO2 is thought to decompose following the subsequent equations (2-11, 2-12,
2-13) in which dinitrogen trioxide (N2O3), NO• and NO2 are generated in aqueous solution (Duncan et al. 1997).
- + NO2 + H Û HONO (pKa=3.37) (2-11)
2HONO Û H2O + N2O3 (2-12)
N2O3 Û NO• + NO2 (2-13)
HNO2 is also a strong oxidizing agent. HNO2 can be alternatively converted into nitrosonium cation + (NO ) (2-14) in acidic conditions, an even stronger oxidizing agent than HNO2 (Duncan et al. 1997).
+ + HONO + H ® NO + H2O (2-14)
It has been proposed that FNA can work mainly in 3 ways. Firstly, FNA can act as an uncoupler, crossing the membrane and shuttling protons between the two sides without ATP formation (Rottenberg 1990). In order to push protons out of cells with FNA diffused through the cell membrane, a higher fraction of energy consumption will occur, which corresponds to a greater cell maintenance energy requirement and thus affects the cell growth rate (i.e. uncouple energy consumption from growth). Zhou et al. found that the intracellular ATP level of Accumulibacter was largely influenced upon its exposure to FNA (Zhou et al. 2010, Zhou et al. 2007). With increasing incubation time, the intracellular ATP level was reduced and the decreasing rate was correlated with the FNA levels. It is expected that the rapid loss of intracellular ATP pools is possible, which might result in cell death as a consequence of ATP depletion (Schimz 1980).
23
Figure 2–6 Proton transportation and ATP production in a denitrifying bacterial cell. The electron transport chain generates the proton motive force across the cell membrane, which is then used to generate ATP by the ATP-ase (Zhou et al. 2011).
Secondly, FNA may react with enzymes. For example, it was concluded that FNA was able to react with sulfhydryl (SH)-containing enzymes, key regulators of the tricarboxylic acid (TCA) cycle (O'Leary and Solberg 1976). This would adversely influence the energy generation process of cells since the NADH produced via TCA is converted to ATP (Figure 2–7). Rowe et al. found that FNA could oxidize the ferrous iron of an electron carrier, such as cytochrome oxidase, to ferric iron, which will significantly impact the activity of the electron transport chain (Rowe et al. 1979). It was reported that FNA was capable of reacting with glyceraldehydes-3-phosphate dehydrogenase, an enzyme involved in both glycolysis and gluconeogenesis (Hinze and Holzer 1986).
HNO2 S-nitrosothiols HNO 2 S-nitrosothiols CoA-SH CoA-SH Pyruvate Acetyl-CoA
Oxaloacetate citrate TCA
H2O
malate S-nitrosothiols isocitrate
HNO2 succinate CoA-SH CO2 GTP CoA-SH α-ketoglutarate
Succinyl-CoA
CO 2
Figure 2–7 Illustration of S-nitrosothiols formed by SH groups reacting with FNA (Park 1993).
In addition, the Calvin cycle, which is responsible to fix carbon in Ammonium-oxidizing bacteria (AOB) (Chain et al. 2003, Norton et al. 2008, Stein et al. 2007), produces glyceraldehyde-3-phosphate which is later consumed during biosynthesis. The possibility is that FNA also affects this anabolic
24 pathway due to the enzymatic reaction, inhibiting the Nitrosomonas growth as a result (Vadivelu et al. 2006a). Recently, the response of a model denitrifier, Pseudomonas aeruginosa PAO1, common in wastewater treatment, was studied (Gao et al. 2016a) (Figure 2–8). TCA cycle was inhibited by the lowered cellular redox state in the FNA-exposed cultures. As a possible survival mechanism, PAO1 rerouted to pyruvate fermentation and produced acetate at the end.
Figure 2–8 A model proposed to illustrate PAO1’s response to FNA stress and its survival strategies. The red and green shapes represent the encoding genes “highly” up- or down-regulated, respectively; the blue and purple shapes stand for the encoding genes “moderately” up- or down-regulated, respectively; and the black shapes denote encoding genes with no change with FNA treatment (Gao et al. 2016a).
Thirdly, the inhibitory and biocidal effects of FNA can be attributed to the formation of reactive nitrogen species. All the formed reactive nitrogen species (i.e. N2O3, NO, NO2) are good nitrosating agents, which diffuse readily across membranes (Denicola et al. 1996). They react rapidly with reduced thiols to form nitrosothiols, which are regarded to be crucial in microbial killing (Denicola et al. 1996). N2O3 can induce “nitrosative stress” by the modification of the proteins’ function (Yoon et al. 2006). NO is a widely known antimicrobial agent and has been shown to inhibit respiratory chain enzymes by deactivating iron-sulfur complexes, disrupt DNA replication by the inhibition of ribonucleotide reductase, and be involved in the biocidal action of HNO2. NO has been found to trigger biofilm dispersal in the opportunistic pathogen P. aeruginosa at low, non-toxic concentrations and then increase the motility and susceptibility to antimicrobials (Barraud et al. 2006). Also, NO2 can cause lipid peroxidation, which leads to cell membrane damage (Halliwell et al. 1992).
25 2.5 Current laboratory methodologies of MIC study
The current laboratory study of MIC mostly includes metallurgical, microbiological, and chemical analyses. To identify causative microorganisms, many approaches have been used: the culture of the organisms on solid or in liquid media, genetic microbial analysis, and microscopic methods to correlate microbial cells with the corrosion products. Cultivation methods are often used practices in the oil industry, by using selective growth media for selected microorganisms. A small amount of liquid or corrosion products collected from oilfields are added to a solid or liquid culture medium. However, only up to 15% of the microorganisms in pipelines can be cultivated and reflected by culture-based techniques (AlAbbas et al. 2012), because the predominantly microbial diversity is dominated in most ecosystems by uncultured microorganisms (Koch et al. 2002). This limitation inspired the application of molecular microbiological methods by targeting DNA, RNA, and proteins. The commonly used methods include fluorescence in situ hybridization (FISH), quantitative polymerase chain reaction (qPCR), amplification and sequencing of the 16S rRNA gene and denaturing gradient gel electrophoresis (DGGE).
Although the result is more accurate using molecular genetic methods, it is not a good option for daily routine tests because of the sophisticated equipment required. Many simple handy methods are utilized for quick tests. Adenosine triphosphate (ATP) luminescence is tested for quick enumeration of relative total bacteria in environments. Adenosine-5’- phosphosulfate (APS)-reductase test is used to detect SRB.
Pit morphology is an important parameter for MIC study. There are different voices on the relationship between pit shape and MIC. Some believe that initial pit formation led by different types of bacteria have special identifying features, while others relate the pit shape to the nature of the corrosion products on the surfaces. Microscopic methods are developed to evaluate biofilm, corrosion products, and corroded surfaces. Electron Microscopy including SEM, FE-SEM, TEM can provide detailed information about biofilm development, composition, distribution, and relationship to substratum/corrosion products (Little et al. 2007). Confocal Laser Scanning Microscope (CLSM) could create three-dimensional structures from the images collected at different depths within a biofilm. Epifluorescence Microscope separates cells from debris, while 3D optical profiler detects surface topography.
Since microbial activities and biofilm development can greatly influence the nature of the interface between water and metal, electrochemical measurements are carried out for corrosion monitor and measurement. Open circuit potential (OCP) analysis can measure the corrosion potential and redox
26 potential without extra external signal (Silverman 2011). Linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) are common techniques used for the measurement of corrosion rate and the kinetic behavior of the system, with a small external signal applied. OCP, LPR, and EIS are nondestructive and have been extensively utilized in MIC studies (Blais et al. 2016, Liu et al. 2016a, Miranda et al. 2006, Su et al. 2014). Large voltage or current signals are performed to do the potentiodynamic polarization tests to provide information about passivity, thermodynamic and kinetic of the corrosion process, but these tests will destroy the biofilm (Silverman 2011). Cyclic polarization measurement is usually used for localized corrosion monitor and prediction.
2.6 Conclusion of literature review and research gaps
Based on the above literature review, the main knowledge and technology gaps are generated:
(1) To control and mitigate the MIC on the internal surface of the pipelines in secondary oil recovery process, a clear and accurate understanding of the MIC process is important and required. Besides traditional, well-characterized SRB cultures (e.g. Desulfovibrio vulgaris) (Javed et al. 2015, Liu et al. 2017b), the roles of strains isolated from produced water (Miranda et al. 2006), and the oil sludge (Chen et al. 2017) were also studied. Also, SRB was coupled with other bacteria to verify the mixed cultured biofilm’s effect on corrosion (Batmanghelich et al. 2017, Liu et al. 2015a). However, the water injection process in secondary oil recovery is a dynamic process with continuous flow, which transports ions and short-chain fatty acids through the pipeline (Grace 2013, Papavinasam 2014). In addition, the pipes are exposed to a water flow with minor bacteria rather than an enriched SRB solution which were used as the immersing medium in most of the studies. With ongoing injection, bacteria gradually attach to the internal pipe surface and form the detrimental biofilm on the surface (Eckert and Skovhus 2018). The MIC development in the early stages of water injection process has been barely covered.
(2) Based on the reported strong biocidal effects of FNA on sewer biofilms (Gao et al. 2016b, Jiang et al. 2011b), we hypothesize that FNA would also be able to deactivate the biofilm in a water injection system associated with oil or gas production. However, the effect of FNA on steel corrosion cannot be directly inferred from previous sewer work due to the more complex nature of metal corrosion. Metal corrosion in the water injection system occurs in the liquid phase, rather than in the gaseous phase as in a sewer. The corrosion process is influenced by many
biological and physicochemical processes. First of all, H2S produced by SRB could directly interact with iron, inducing the anodic reaction of iron dissolution (Bai et al. 2015). SRB could also extract electrons directly from Fe0, especially when associated with low carbon levels,
27 which would cause pitting corrosion underneath the biofilm (Gu et al. 2009, Zhong et al. 2019). Further, the metal acts as an electrode submerged in the electrolyte, thus the corrosion process is also influenced by many other ions, such as chloride, in the medium.
(3) As afore reviewed, FNA is a strong biocide to the biofilms in anaerobic environments. In the practical oil production process, the pipelines are usually maintained with mixed chemicals, including corrosion inhibitors and biocides (Heidersbach 2018, Kermani and Chevrot 2012). As widely used corrosion inhibitors in oilfields, imidazoline and its derivatives showed their advantages with high inhibition efficiency, easy production and low toxicity (Kelland 2014). Although FNA’s effect on MIC needs further investigation, its strong biocidal effect has shown that it could be a potential biocide alternative in oil production infrastructure. Research on the combined effect of FNA and corrosion inhibitors will be beneficial in evaluating FNA’s potential as a MIC controlling chemical, and an innovative FNA-based strategy would be developed as a substitute to control and mitigate MIC in real oil production process.
28 Chapter 3 Research objectives
This chapter presents the research objectives of the thesis.
The overall aim of this PhD thesis is: to gain a better understanding of MIC process in secondary oil recovery by analysing the corrosion behaviour of the carbon steel coupons in a simulated water injection system; and then to develop an FNA-based technology for corrosion control in oilfields operation in isolation or in combination with other corrosion inhibitory chemicals. This research is expected to provide support for the future MIC control development, and develop effective and economical FNA-based strategies to tackle the corrosion problems in the oil industry. More specifically, three research objectives are addressed.
3.1 Investigating the development of MIC on carbon steel in a simulated water injection system
Hypothesis:
1. The behaviour and mechanisms of MIC on carbon steel surfaces are influenced by the development of the biofilm.
2. The carbon source concentration in the medium affects the biofilm’s corrosion behaviour on the carbon steel surfaces.
Detailed objectives:
1. To gain an improved knowledge of the influence of biofilm development on MIC behaviour on internal pipeline surface in the early phase of the water injection process.
2. To further reveal SRB-related corrosion mechanisms according to the nutrient level in the immersion medium.
3.2 Examining the effect of FNA intermittent dosing on MIC control in a simulated water injection system
Hypothesis:
1. FNA has biocidal effects on the corrosive biofilm on the carbon steel surfaces in the simulated water injection system.
29 2. FNA could potentially reduce the corrosion induced by the biofilm dominated by SRB.
Detailed objectives:
1. To test the effects of FNA on both the general corrosion and pitting corrosion rates of the carbon steel coupons with mature biofilms in the simulated water injection system.
2. To evaluate intermittent FNA dosage as a corrosion control strategy with repeated FNA treatments.
3.3 Determining the combined corrosion inhibitory effects of FNA and HEI-17 in a simulated water injection system
Hypothesis:
1. HEI-17 could inhibit the general corrosion on carbon steel coupon surfaces in the simulated water injection system.
2. FNA application combined with HEI-17 would show improved further corrosion inhibition effects on both general and pitting corrosion.
Detailed objectives:
To study the combined effects of continuous dosing of HEI-17 and intermittent dosing of FNA on both the general and pitting corrosion rates of the carbon steel coupons in the simulated water injection system.
30 Chapter 4 Methods and materials
This chapter describes general research materials and methods used in all the research objectives in this thesis. The Methods and Materials section in each of the following chapters covers the specific research approaches.
4.1 Simulated water injection system set-up and operation
The schematic of the simulated water injection system is shown as Figure 4–1. The bioreactor (height, 150 mm; inner diameter, 130 mm) is made of acrylic and has a volume of 1.99 L. Six plastic rods were inserted in the reactor, each holding 4 carbon steel coupons (Yangzhou Branch of Environmental Protection Equipment Limited, 20 ´ 10 ´ 2 mm). The carbon steel used in this study contains (by weight) 0.43% C, 0.58% Mn, 0.22% Si, 0.012% P, 0.008% S, 0.01% Cr, 0.01% Cu, 0.01% Ni, and 98.72% Fe. The reactors were operated at 25 °C. All the coupons were ground through 600, 800 and 1200-grit silicon carbide papers, then degreased ultrasonically in acetone, washed with anhydrous ethanol, dried and stored in a desiccator before use. All coupons were sterilized under a 30 wattage ultraviolet (UV) lamp for 30 min prior to being inserted to the reactor. The coupon holders are removable via ports on the lid for biofilm sampling.
An electrochemical cell was also set up in the reactor. A coupon was machined into a square shape for electrochemical measurements and only one surface (1cm ´ 1cm) was exposed, with the rest moulded in epoxy resin, as a working electrode. A copper wire was soldered to the coupon for electrical connection. A saturated Ag/AgCl electrode and a platinum net were used as the reference electrode and the counter electrode, respectively.
The reactor was initially seeded with 200 mL produced water collected from an oilfield (Mereenie, NT) in Australia, and then fully filled with synthetic seawater (see 4.2 for composition). For the first 36 days, the reactor was run batch-wisely by replacing 25% of the liquid in the vessel with fresh medium once every 4 days, providing time for bacteria in the inoculum to attach on the coupons (Batch Feeding). After that, the reactor was continuously fed with the synthetic medium by a peristaltic pump with a flow rate of 0.69 mL/min, which gives rise to a hydraulic retention time (HRT) of 48 hours (Continuous Feeding). The feeding was provided by a peristaltic pump (Watson-Marlow Pumps 323S). During feeding, an equivalent amount water was discharged from each reactor via the effluent port to a discharge bottle (effluent container). The headspace of each discharge bottle was connected to 1 mol/L NaOH solution to trap H2S in the gas. The reactor was constantly mixed with magnetic stirrer operated at 110 rpm.
31
Figure 4–1 The schematic of the simulated water injection system.
4.2 Synthetic produced water
A widely-used seawater recipe was used for the feed medium, comprising (per L distilled water):
NaCl 20.0 g, Na2SO4 3.0 g, MgCl2·6H2O 3.0 g, CaCl2·2H2O 0.15 g, KCl 0.5 g, KH2PO4 0.2 g, NH4Cl
0.3 g, NaHCO3 solution 30 mL, trace element 1 mL (Hubert et al. 2003). NaHCO3 and trace element solutions were prepared accordingly (Widdel and Bak 1992). The pH was adjusted to 7.2 with 2 mol/L HCl solution. Lactate was added to the synthetic seawater at 5 mol/L to provide the microbes with essential carbon source for their growth.
Before use, synthetic seawater was purged with pure nitrogen gas for 3 h to remove oxygen in the medium. A 5 L gas bag containing nitrogen gas was connected to the medium container to keep the medium oxygen free.
4.3 Electrochemical measurements
An electrochemical workstation (Bio-Logic SAS) was used to conduct the electrochemical tests, including OCP, EIS, and LPR. OCP represents the potential of the working electrode without affecting the electrochemistry on the electrode surface in electrical cells. Generally, metals with nobler OCP are more thermodynamically stable than materials with less noble OCP. The EIS and LPR measurements were conducted with small amplitude potential over OCP, so they were regarded as non-destructive methods for corrosion analysis (Feng and Cheng 2017).
32 Both LPR and EIS measurements were conducted after OCP reached steady state. The LPR measurement was carried out in the potential range from −0.02 V to +0.02 V vs. OCP at a sweep rate 0.166 mV/s to measure the polarization resistance (Rp), which is the slope of the polarization curve. The corrosion rate is revealed as it is inversely correlated with Rp.
During EIS measurements, a small sinusoidal voltage with amplitude Vo (= 10 mV) and variable frequency f of 10-2 to 105 Hz (f=ω/2π, where ω is the angular frequency) was applied to the system, and the response current I(ω,t) was measured. The impedance Z(ω,t) was calculated (Z=V/I), based on the Nyquist and Bode plots constructed. The Zview2 software (Scribner, Inc) was used to fit the EIS data with suitable equivalent circuit models. An equivalent circuit is a theoretical circuit that retains all of the electrical characteristics of the electrochemical cell, based on which the corrosion behavior on the working electrode surface could be investigated.
4.4 ATP level measurement
To measure the cellular ATP in the biofilm on the coupon surfaces, coupons were carefully sampled aseptically in an anaerobic chamber into 4 mL sterilized phosphate buffered saline (PBS, 1X, pH 7.4) in a 5 mL sterile bottle. Then the bottle was vortexed for 2 min to disperse the biofilm for the following ATP tests. The BacTiter-Glo™ Microbial Cell Viability Assay (Promega Corporation, G8231) was used to measure the ATP levels according to the manufacturers’ instructions. A volume of 300 µL of biofilm suspension was transferred to a 96-well flat bottom microplate (Greiner bio-one) to bind with 100 µL BacTiter-Glo™ Reagent. The plate was then inserted into a multimode plate reader (CLARIOstar® Plus) to measure the luminescence after 90 s incubation at 37 °C. The luminescence was collected as relative light units, and converted to ATP concentrations (nM) using a calibration curve made with a known ATP standard (Promega Corporation, USA) (Filloux et al. 2015).
4.5 Corrosion rate by weight loss
Corrosion rate was calculated from weight-loss measurements. The tests were conducted according to ASTM D-2688 (ASTM 2015). After the ATP level measurement in 4.4, the coupons were then immersed in Clarke’s solution, which is a mixture of concentrated HCl, 2% antimony trioxide (Sb2O3) and 5% stannous chloride (SnCl2), and vortexed for 2 min to remove the surface biofilm and corrosion products (Javed et al. 2015). The coupons were then cleaned thoroughly by sonication for 10 min to remove any remaining debris on the surfaces. The coupons were rinsed with distilled water, cleaned with anhydrous ethanol, and dried under high pressure N2 (99.5%). The coupon’s initial weight was
33 marked as W0, and the weight of the cleaned coupon was marked as Wn. Corrosion rate in mm/y was calculated as:
, !" = %.'(×*+ ×(./0.1) (4-1) # 345
3 where CRn is corrosion rate (mm/y), Wn is weight of coupon (g), ρ is density (g/cm ), A is surface area (cm2), and t is immersion time in the simulated water system (d).
4.6 Pitting analysis with a 3D optical profiler
Following the weight-loss measurements described in Section 4.5, a 3D optical profiler (Zeta 300) was used to obtain 3D images (e.g. Figure 4–2) and measure the surface roughness of the corroded coupons. Optical interference profiling is a well-established accurate method of measuring height variations on surfaces, using the wavelength of light as the ruler (Chand et al. 2011). The bottom and the top of the surface were located by adjusting the focus of the light. Then the surface was scanned by the light with a step size of 0.2 µm to generate the 3D images. The surface height distribution was generated by the built-in software. The pitting depth could be calculated from the height profiling data. The cumulative distribution of the pitting depth and the pitting depth at 90% cumulative distribution could be generated accordingly. Then, the averaged pitting corrosion rate could be calculated by the pitting depth at 90% cumulative distribution as follows:
6!" = %'(×71 (4-2) # 5
Where PCRn is pitting corrosion rate (µm/y), dn is pitting depth at 90% cumulative distribution (µm), and t is immersion time in the simulated water system (d).
Figure 4–2 A 3D profiling image obtained by the 3D optical profiler.
34 4.7 Visualization of biofilm and corrosion products
To visualize the biofilm and corrosion products on the coupon surfaces, scanning electron microscopy (SEM) analysis was conducted. The coupons were first fixed with 3% glutaraldehyde for 4 h. Then the coupons were dehydrated using a gradient of ethanol (25%, 50%, 75%, 90%, and 100% v/v). Then the coupons were dried in an anaerobic chamber. Prior to SEM observations, the coupons were coated with a thin carbon film. A HITACHI SU3500 was used for observation with a 20 kV accelerating voltage.
4.8 LIVE/DEAD staining
To evaluate FNA’s biocidal effect at different concentration, LIVE/DEAD staining tests were conducted to visualize biofilm and indicate the viability of bacterial cells in biofilms with a Zeiss 510 CLSM. A carbon steel coupon was stained using the green fluorescent SYTO®9 nucleic acid stain and the red fluorescent propidium iodide (PI) from LIVE/DEAD® BacLightTM Bacterial Viability Kits purchased from Molecular Probes® (L-7012, Invitrogen, Australia). The SYTO®9 stain labels all bacteria in a population with intact or damaged membranes, while PI stain penetrates only those bacteria with damaged membranes. Thus, bacteria with intact cell membranes (viable cells) are stained green, whereas bacteria with damaged membranes (dead cells) are stained red. The stained coupons were incubated in a dark place for 15 min at room temperature (20 °C), allowing the staining reactions to complete. Then a coupon was immersed in the fresh synthetic seawater medium to be observed with a 20x water objective lens. Two excitation/emission wavelengths were used for the two florescent stains: 488 nm/500 nm for SYTO®9 and 510 nm/635 nm for PI. Five images were taken for randomly chosen areas of each sample. Z-stacks measured at a 0.6 µm step size. To Quantify the live and dead cells, the relative abundance of green and red pixels was counted with DAIME (Digital image analysis in microbial ecology, by Holger Daimes). The viability of the biofilm was represented by the ratio of viable cells to the total cells in the biofilm.
4.9 Chemical analysis
To analyze dissolved inorganic sulfur species, 1.5 mL liquid sample was filtered (0.22 µm membrane) into 0.5 mL preserving solution of sulfide anti-oxidant buffer (SAOB) (Keller-Lehmann et al. 2006). Samples were then analyzed on an ion chromatograph (IC) with a UV and conductivity detector (Dionex ICS-2000) within 24 hours.
35 1 mL liquid was filtered for VFA analysis by High Performance Liquid Chromatography (PerkinElmer, Inc.) with an HPX-87H 300 mm ´ 7.8 mm, BioRad Aminex ion exclusion HPLC column operated at 65 °C.
To analyze the nitrogen species (nitrate, nitrite, and ammonia), 1 mL of the liquid sample was filtered and diluted 10 to 20 times depending on the nitrate/nitrite concentration. It was then analyzed by a Lachat QuikChem 8000 (Milwaukee) flow-injection analyzer (FIA).
36 Chapter 5 Development of microbially influenced corrosion on carbon steel in a simulated water injection system
5.1 Introduction
As discussed in section 2.6, the MIC development in the early stages of water injection process has been barely covered. The aim of this study is to gain an improved understanding of the influence of biofilm development on MIC behavior on internal pipeline surface in the early phase of the water injection process. Three continuously-fed biofilm reactors were operated to simulate the water injection systems. The development of the corrosion process and biofilm was monitored for 5 months with electrochemical measurements, weight-loss measurement, SEM and 3D optical profiler.
5.2 Materials and methods
5.2.1 Experimental procedure
During the first 36-day batch-wise operation, water samples were taken before the medium change and analyzed for concentrations of dissolved sulfur compounds (sulfide and sulfate) and fatty acids (lactic, propionic, and acetic acids), using methods described in section 4.9. During the subsequent 106-day continuous operation, water samples were taken every 48 hours from the sampling ports on the reactor lids for analysis of the above-mentioned parameters. pH in the reactor was monitored every 24 h by portable pH meters (Oakton 150).
5.2.2 Analysis
Electrochemical measurements were conducted every 24 hours during the experiment. From Day 8 to Day 142, two coupons were removed from each of the reactors in an anaerobic chamber every 15 ± 2 days for weight-loss measurement (section 4.5). Among these coupons, the ones taken on Day 30, 60 and 130 were used for SEM analysis (section 4.7). The coupons taken on Day 46, 90 and 130 were used for 3D profiling (section 4.6). Both arithmetic average roughness (Ra) and root mean square roughness (Rq) were generated by 3D optical profiling to evaluate the pitting corrosion.
5.3 Results
Triplicate reactors were operated and monitored in this study. The results of R1 are presented in this section as an example. Results of R2 and R3 are presented in Supplemental Material. The results from all three reactors were consistent, demonstrating reproducibility of the results.
37 5.3.1 Reactor operation and biofilm development
Coupons were sampled on Day 30, Day 60, and Day 130 for SEM imaging. As shown in Figure 5– 1a, on Day 30, the microbes started to attach to the surface and colonized at certain points with little EPS. Also, some cracked and flaky structures (arrows shown) appeared on the surface, which was previously also spotted in a wet H2S environment (Bai et al. 2015). On Day 60, EPS had covered most of the surface with the biofilm building up, and the flaky products were replaced with sphere particles (Figure 5–1b). Gradually, the biofilm with complex tunnels and structures had built up on Day 130 (Figure 5–1c).
Batch Feeding Continuous Feeding Batch Feeding Continuous Feeding d 680 e 600 Sulfide Sulfate
630 400 Lactate Acetate Propionate
580 200
80 Concentration (mg/L)
Concentration (mg-S/L) 40
0 0 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Time (day) Time (day) Figure 5–1 SEM images of biofilms on coupons taken on Day 30 (a), Day 60 (b), and Day 130 (c); sulfate and sulfide in reactor effluent (d), and lactate, propionate and acetate in reactor effluent (e) during the course of the experiment. All data reported are from R1.
Sulfide concentration increased and sulfate decreased gradually in the same period, which indicates SRB growth and activities. The sulfate concentration in the synthetic seawater was 676 mg-S/L, and it decreased to and stabilized at around 585 mg-S/L after Day 114, so the reduction of sulfate was approximately 90 mg-S/L. The sulfide concentration in the reactor liquid gradually increased to about 60 mg-S/L after Day 114. The sulfite and thiosulfate concentrations were negligible. The sulfide concentration was 30 mg-S/L lower than its theoretical concentration calculated from sulfur balance. This could be attributed to the precipitation of ferrous sulfides as a corrosion product, of which the formation was indicated by the black particles observed both in the medium and on the coupons 38 during the experiments. After Day 60, acetate became the main carbon compound in the reactor effluent (Figure 5–1e). The acetate concentration in steady state was 54.3 mg/L, which was substantially lower than the sulfate concentration (585.6 mg/L).
5.3.2 Electrochemical measurements
5.3.2.1 Open circuit potential
Batch Feeding Continuous Feeding -0.50
-0.55
-0.60
-0.65
-0.70
OCP (V vs Ag/AgCl) -0.75
-0.80 0 20 40 60 80 100 120 140 Time (day) Figure 5–2 OCP of the carbon steel working electrode in R1.
The change of OCP of carbon steel in R1 is shown in Figure 5–2. The OCP value increased initially from -0.68 V to -0.67 V in the first 7 days, then decreased and remained at a low level of -0.73V until Day 25. The initial increase could be the result of the passivation of the carbon steel with trace oxygen which wasn’t fully replaced by N2 in the environment. From Day 26, the OCP value increased greatly to a relatively high level (around -0.6 V), which was likely caused by the formation of the corrosion product film on the surface. Since the continuous-feeding phase started after Day 36, OCP decreased gradually to around -0.7 V between Day 80 to Day 100. Although experiencing a few fluctuations, the OCP generally remained steady at around -0.67 V after Day 60, which matches the changes of sulfur species (Figure 5–1d) and VFA (Figure 5–1e) and indicates that the system has reached a stable state. OCP is influenced by many factors and it does not provide a direct indication of corrosion rate (Stansbury and Buchanan 2000). So, LPR and EIS were conducted to analyze the corrosion performance more directly and preciously.
5.3.2.2 EIS measurements
The EIS, Nyquist (a, b, c) and Bode plots (d, e, f) of R1 are shown in Figure 5–3.
39 400 800 c D76 D111 b 0.01 Hz D31 D54 a D1 D9 D38 D62
50000 ) D86 D126
D3 D13 Fitting line )
D46 D72 2 ) D5 D20 2 600 300 D142
2 0.013 Hz D94 D7 D27 Fitting line 40000 40 Fitting line cm cm ⋅ ⋅ cm 0.022 Hz 0.071 Hz 30 ⋅ 30000 400 200 Ω Ω
Ω 6000 20 0.755 Hz 20000 0.022 Hz 4000 10 200 0.755 Hz 100 0.015 Hz -Z” (
0.071 Hz -Z” ( -Z” ( 2000 0 10000 0.022 Hz 0 10 20 30 40 50 0 0 2000 4000 6000 0 0 0 0 20000 40000 60000 80000 0 100 200 300 400 500 600 0 100 200 300 Z’ (Ω⋅cm2) Z’ (Ω⋅cm2) Z’ (Ω cm2) ⋅
1000 -100 600 -100 e f D76 D111 D1 D9 -100 D31 D54 d 80000 D3 D13 -80 D86 D126 -80 ) ) 800 D38 D62 2 ) D5 D20 2 D94 D142
2 -80 D7 D27 D46 D72 -60 400 -60 60000 Fitting line 600 Fitting line cm -60 cm Fitting line ⋅ ⋅ cm ⋅ -40 -40 Ω Ω Ω 40000 -40 400 -20 200 -20 -20 |Z| ( |Z| ( |Z| ( 20000 200 0 0 0 Phase (degree) Phase (degree) Phase (degree) 0 20 0 20 0 20 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 log (f, Hz) log (f, Hz) log (f, Hz)
Q Q Q dl Q f h f f g i R RS S RS W W R O R O f CT Rf Rf
Figure 5–3 Nyquist (a, b, and c) and Bode (d, e, and f) plots of the carbon steel working electrode in R1. The electrochemical equivalent circuits (g), (h) and (i) were used to fit the EIS data reported in a & d, b & e, and c & f, respectively.
40 Generally, the diameter of the arcs in the Nyquist plots is proportional to the impedance, so a bigger diameter indicates a lower corrosion rate (Langford and Broomfield 1987, Ribeiro et al. 2015). According to the Nyquist plots in the first 27 days (Figure 5–3a), the diameter of the semicircle increased in the first 5 days then decreased greatly from Day 7. In Bode plots (Figure 5–3d), the phase angle showed a shift towards higher value at low frequencies (10-2 to 1 Hz), which can be regarded as a trend of transformation of the system to diffusion limitation phenomenon (Batmanghelich et al. 2017). The phase angle diagram showed a time constant in medium frequencies (1 to 103 Hz), which was attributed to the unstable layer of a mixture of inorganic/organic compounds (Castaneda and Benetton 2008). Low-frequency impedance in Bode plots reduced quickly after Day 7, of which the decreasing rate slowed down after Day 13 and remained at a relatively low value (less than 1000 W×cm2).
In Figure 5–3b, the Nyquist plots changed to a more complex shape from Day 31. Instead of a pure semicircle, a linear line (from 0.071 to 0.755 Hz) with a big arc (from 0.01 to 0.071 Hz) appeared after a small arc (red box area). From Day 46 to Day 62, the diameter of the big arc reduced gradually, which is an indication of an increasing corrosion rate. As shown in the Bode plots (Figure 5–3e), the big phase angle continued to shift to even lower frequencies, and another time constant appeared between 1 and 10 Hz.
The linear line with the big arc changed to a straight line after Day 76 (Figure 5–3c), of which the gradient slowly reduced with time. The Bode plots showed that the time constant between 1 and 10 Hz gradually shrank and disappeared on Day 111. The impedance decreased gradually to a low level around 150 W×cm2.
The EIS were fitted with the suitable equivalent circuit models to get a quantitative measurement of the electrochemical parameters at the metal/electrolyte interface (Figure 5–3 g, h and i). The fitting results are shown in Table 5–1. The fitting errors were less than 10% for all parameters, indicating adequacy of the equivalent circuit models. Since the surface of the coupon was unlikely smooth but with microscopic roughness, the constant phase element (Q), which is a non-ideal capacitor, was used to consider distributed capacitance instead of an ideal capacitor. The impedance of Q (ZQ) is calculated by the following equation: $ Z" = + (5-1) %&(())
where Y0 and n are frequency independent parameters indicating the deviation of the specimen from an ideal capacity, and w is the angular frequency of the alternating voltage in rad s-1. In the circuits
41 (Figure 5–3 g, h, and i), Rs is the solution resistance, Rf and Qf are the resistance and capacitance of the biofilm, respectively. RCT, Qdl and Wo denote the charge transfer resistance, a double layer capacitance, and the finite length Warburg (FLW) element, respectively.
Table 5–1 Fitting results of EIS tests of carbon steel working electrode in R1 during 142-day experimental study.
Qf nf Rf WR WP Qdl ndl RCT Day RS WT (s) (Ω·cm2) (0 Three electrochemical equivalent circuits shown in Figure 5–3 g, h and i were used to model the EIS results from Day 1 to Day 27 (Figure 5–3 a and d), Day 31 to Day 72 (Figure 5–3 b and e), and Day 76 to Day 142 (Figure 5–3 c and f), respectively. As the precipitation of corrosion products and biofilm on the metal surface can be considered a macromolecular coating with pores, a resistance (Rf) and a constant phase element (Qf) were used in the one-time constant modeling circuit (Figure 5–3g) (Liu et al. 2015b). Before Day 27, the Nyquist plots (Figure 5–3a) showed one arc and there was only 42 one time constant in the Bode plots (Figure 5–3d). The impedance spectra in this period was fitted with the one-time constant circuit (Figure 5–3g). Generally, the corrosion rate is inversely correlated with the resistance value. According to the fitted results (Table 5–1), Rf increased tremendously for the first 5 days from 8416 2 2 to 81146 Ω·cm , which indicated a decreasing corrosion rate. Then Rf started to drop to 15855 Ω·cm on Day 13 and bounced back gradually to 70870 Ω·cm2 on Day 27, indicating that the corrosion rate first increased and then decreased to a low level. From Day 31 to Day 72, the EIS data (Figure 5–3 b and e) was modelled by a two-time constant circuit, with Qdl and Rct denoting the double-layer capacitance and the charge transfer resistance, respectively. A FLW (Wo) element was included to describe the diffusional influence (0.071 to 0.755 Hz) between the two arcs. FLW is the solution of the one-dimensional diffusion equation of a particle, which is completely analogous to wave transmission in a finite-length RC transmission line (Sun et al. 2018). The deviation of the double-layer capacitance from an ideal capacity (ndl) dropped gradually from 0.8994 (Day 38) to 0.6695 (Day 72), which indicated that the electrode surface was getting more rough and porous (Sheng et al. 2007). A one-time constant circuit was used to model the spectra (Figure 5–3 c and f) from Day 76 to Day 142. The big arc had changed to a linear shape completely, which was simulated with the Warburg element (Wo). 5.3.2.3 LPR results Batch Feeding Continuous Feeding 100000 80000 ) 2 60000 cm Ω⋅ ( P 40000 R 20000 0 0 20 40 60 80 100 120 140 Time (day) Figure 5–4 Rp value calculated from linear polarization plots of the carbon steel working electrode in R1. As shown in Figure 5–4, the Rp value calculated from linear polarization plots increases tremendously in the first 5 days from 8031 Ω·cm2 to a large value (82796 Ω·cm2), which then decreases quickly to 12234 Ω·cm2 at Day 12. It bounces back to increase at a lower rate until Day 27. This shows the same 43 trend as the EIS results (Table 5–1). After Day 27, Rp starts to decrease and gradually reaches a relatively steady state at Day 71. Generally, the corrosion rate is inversely correlated with Rp, so the corrosion rate experiences a few fluctuations and ends up at a relatively high level. 5.3.3 Weight-loss measurements Batch Feeding Continuous Feeding 1.5 1.0 0.5 Corrosion rate (mm/y) 0.0 0 20 40 60 80 100 120 140 Time (day) Figure 5–5 Corrosion rates of the coupons calculated from the weight-loss measurements in R1. Error bars represent standard errors (n = 3). The corrosion rate of the coupons calculated from weight-loss measurements along with the immersion time is shown in Figure 5–5. The corrosion rate increased fast from Day 8 (0.42 ± 0.09 mm/y) to Day 46 (0.95 ± 0.16 mm/y) and relatively stabilized at around 0.95 mm/y until Day 90. Then the corrosion rate experienced a drop and finally reached about 0.61 mm/y at Day 130, which kept at the same level until Day 142. It is noticed that the results of the weight-loss measurements were not entirely consistent with the electrochemical measurements including EIS and LPR, as also reported in some other previous studies (Liu et al. 2018). The electrochemical measurements provide instant information of the corrosion processes, while weight-loss measurements represent average corrosion rate during a given period (Liu et al. 2018). 5.3.4 Surface morphologies after removing corrosion products After 130 day reactor operation, one coupon was taken out from R1 and washed with Clark’s solution to remove the biofilm and corrosion products on the surface. Pits dispersed on the coupon surface (Figure S5–1), and the zoom-in picture showed honeycomb micromesh feature of the pit, which indicated that MIC was taking place (Bryant et al. 1991, Mudali et al. 2004). 44 25 a 120 Day 46 b Day 90 Day 46 Day 130 20 Day 90 ) 90 3 Day 130 10 15 60 10 Depth (µm) Count ( 30 5 0 0 0 200 400 600 800 0 20 40 60 80 100 120 140 Distance (µm) Depth (µm) Figure 5–6 Pit depth profile of randomly selected lines (a), and the depth distribution (b) of the coupon surfaces (Figure S5–2) in R1. As MIC process causes localized corrosion instead of the general corrosion, surface roughness and pit depth are important parameters for the assessment (Jacobson 2007). Figure 5–6 shows the pit depth profile of randomly selected lines (a), and the depth distribution (b) of the coupon surfaces (Figure S5–2), which have been immersed in the reactor for 46 days, 90 days and 130 days, respectively. The lowest point of the surface is set as height 0 in the depth distribution diagram (Figure 5–6b). Table 5–2 Surface roughness values of carbon steel coupons on Day 46 (a), Day 90 (b) and Day 130 (c) in R1. Sampling time Ra (µm) Rq (µm) Day 46 5.43 7.37 Day 90 7.22 10.07 Day 130 12.79 17.41 From Figure 5–6b, the highest depths of the coupons shown were 73.3 µm, 103.6 µm, 144.7 µm on Day 46, Day 90 and Day 130 respectively. So, the depth of the deepest pits increased at an average rate of 0.69 µm/d from Day 46 to Day 90, while the rate was much higher at 1.03 µm/d from Day 90 to Day 130. Also, the depth distribution curves get wider gradually from Day 46 to Day 130 (Figure 5–6b), which indicates an increasing surface roughness. The surface roughness of the 3D image was calculated and summarized in Table 5–2. Ra and Rq value increased by 1.78 µm and 2.37 µm, respectively, from Day 46 to Day 90, while the figures have grown by 5.6 µm (Ra) and 7.4 µm (Rq) for the next 40 days until Day 130. Overall, the risk of forming a deep pitting hole was much higher after Day 90 than that before Day 90. 45 5.4 Discussion 5.4.1 Three stages of the corrosion development Based on the EIS results (Figure 5–3 & Table 5–1), corrosion development on coupon surface in the simulated water injection system can be divided into 3 phases: Initialization (I), Transition (II) and Stabilization (III), which is also in accordance with biofilm development (Figure 5–1), OCP (Figure 5–2), and LPR (Figure 5–4) results. Phase Initialization (I) included the first 27 days, which involved the formation of a corrosion products layer and the initial attachment of the sessile microbes on coupon surface. At the beginning, the number of microbes was low and the corrosion behavior was similar to that in abiotic conditions (Castaneda and Benetton 2008). In Figure 5–3a, the diameter of the semicircle increased from Day 1 to Day 5, and from Figure 5–4, the corrosion rate increases greatly. This could be due to the precipitation, mostly iron phosphide (Glindemann et al. 1998), which is homogeneously distributed on the coupon surface with the ferrous ion produced by the steel dissolution. The decreasing semicircle diameter and Rp from Day 5 to Day 13 indicated an increasing corrosion rate and vulnerability of the working electrode (Liu et al. 2017a), which is different from that in abiotic environment, because the planktonic SRB started to grow and releases H2S in the medium. H2S could form corrosion products of iron sulfides on coupon surfaces (Fe + HS- ® FeS + H+ + 2e-), and gradually a thick layer of black precipitation formed as visualized on Day 24 (Figure S5–3). Also, a small number of microbes started to colonize on the coupon surface as shown in Figure 5–1a. This process could have slowed down the corrosion rate by blocking the sulfide produced in medium reaching the metal surface, as indicated by the Rp increase from Day 13 to Day 27 (Figure 5–4). Thus, Stage Initialization (I) involved the formation of the corrosion products layer and the initial attachment of the sessile microbes on coupon surface in the simulated water injection system. Iron sulfides formed in the absence of oxygen are normally unstable (Walker 2001). When a breakdown occurs, which exposes some part of the metal surface, active corrosion cells will build up between iron sulfide (cathode) and the exposed-steel (anode) (Hamilton 2003). Thus, as shown in Figure 5–4, Rp decreased greatly from Day 31 to Day 46, indicating an increasing corrosion rate. The appearance of the diffusional influence (Figure 5–3e) is probably due to the formation of biofilms with a large amount of EPS on the surface, which are negatively charged and could repel corrosive anions (Dunne 2002, Zuo et al. 2005) (Figure 5–1b). But still, as the biofilm is patchy and thin (Figure 5–1b), the substrates could be gradually transferred to the metal/biofilm interface by the mixing shear force, which was likely the reason that the diffusional influence disappeared and the charge transfer 46 resistance affects the corrosion process mostly. While the time constant in medium frequencies might be due to the formation of the outer-layer biofilm, the one at the lowest frequencies was probably a result of the active pits, which had a typical feature in the pitting model (Zuo et al. 2005). From Day 46 to Day 62, the impedance’s gradient reduced gradually, so although the increasing speed slowed down, the corrosion rate kept rising gradually (Figure 5–4). From Day 31 to Day 72, the biofilm grew fast on the coupon surface and transformed the corrosion behavior, which was generalized as Phase Transition (II). In this period, corrosion rate first increased greatly and then continued to increase at a lower speed. The corrosion behaviour on the coupon surface started to be dominated by diffusion control after Day 76 (Figure 5–3 c and f). As shown in the SEM image of the coupon surface at Day 130 (Figure 5– 1c), thick and compact biofilms with a large amount of EPS had covered most of the coupons (over 85%), which likely isolated the coupon surface from the electrolyte. This isolation effect could protect the coupons working as a coherent coating film in some degree (Dunne 2002, Sun et al. 2011, Zuo 2007). However, unlike the inert coating, the biofilm could influence the formation of a gradient concentration of the ions, which could alter the micro-environment at the metal/biofilm interface (Castaneda and Benetton 2008). After Day 111, the biofilm structure and thickness reached pseudo- steady state as they were governed by the substrate concentration, which kept stable after Day 111, and the shear force (Liu and Tay 2002). In Phase Stabilization (III), the corrosive biofilm on the coupon surface reached a steady state and corroded the coupons at a relatively stable rate. 5.4.2 Effect of diffusional influence on corrosion It was reported that SRB biofilms enhanced the corrosion rate and shifted the active charge transfer reactions to a diffusion-limited process in a static two-electrode electrochemical cell (Castaneda and Benetton 2008), but it had not been found previously that systems with mixing suffered diffusion limitations. As it was found that high shear force promoted the formation of high-density biofilm (Van Loosdrecht et al. 1995), the biofilm’s density formed on the coupon surface with a certain shear force in simulated water injection system could be larger than that in stagnant systems. With fewer voids and tunnels than in static conditions, a dense and compact biofilm formed on the coupon surface in the mixed system should considerably slow down the substrates’ transmission across the biofilm. Also, although the system is mixed homogenously, the hydrodynamic or velocity boundary layer, which referred to the region where the velocity of the fluid changes from the mixing rate to zero at the biofilm surface (Bishop et al. 1997), was likely non-neglectable. 47 The diffusion limitation effect didn’t appear in other studies in static conditions, which could be due to the relatively short duration of the experiments. Some work on the MIC study in enriched cultures were conducted in less than 1 month (Liu et al. 2017a, Liu et al. 2015b, Sun et al. 2011), with a few for 7 days only (Batmanghelich et al. 2017, Chen et al. 2015). As well, since the systems in their studies were enclosed, the microbes’ metabolites accumulated along with the nutrients decreasing, which could be detrimental to the biofilm growth. Thus, the biofilm might be restricted and couldn’t affect the mass diffusion as much as that in the simulated water injection system used in this study. As aforementioned, the weight-loss measurements showed the uniform corrosion rate on the coupon surface. Due to the diffusion limitation, the concentration of the detrimental ions, such as H+ and HS-, at the metal/biofilm surface were expected to decrease, which could be the reason for the lower general corrosion rate after Day 90 (Figure 5–5). However, the pitting corrosion rate increased in Phase (III) (Figure 5–6 and Table 5–2). The conventional SRB metabolism involves sulfate reduction coupled to organic carbon oxidation. When organic carbons are not available in the environment, both the anabolism (cell synthesis) and the catabolism (energy metabolism) should be at a low level. However, previous studies have shown that SRB might be capable to take electrons directly from Fe0 when organic carbons are not available, which accelerates the pitting corrosion rate (Chen et al. 2015, Xu and Gu 2014). Thus, low carbon source level could enhance the corrosion process. The acetate concentration (54.3 mg/L) in the liquid is relatively scarce compared to sulfate concentration (591.5 mg/L) after Day 114. So, acetate could have been completely consumed by SRB in the out layer, leading to carbon deficiency at the metal surface, while sulfate managed to pass through the biofilm and reached the biofilm/metal interface. SRB embedded on the metal surface could have turned to Fe0 for electrons to maintain their viability, which largely enhanced the pitting depth. 5.5 Conclusions The MIC development in a simulated water injection system was evaluated with a combination of EIS for electrical properties, SEM for surface morphology, 3D optical profiler for pits characterization and direct measurement of weight loss. The main conclusions are: • The development of MIC of metal in a water injection system comprises 3 phases: Initialization (I), including the formation of the corrosion products layer and the initial attachment of the sessile microbes; Transition (II), when biofilm develops on the metal surface; and Stabilization (III) with mature and stable biofilm on coupon surface. 48 • Along with the formation of the biofilm on the metal surface, the MIC process gradually shifts from charge transfer resistance to a diffusional-limitation when a compact biofilm formed. • The diffusional-limitation effect slows down the general corrosion, but in the meantime enhances the pitting corrosion in Phase (III), which supports the mechanism of direct electron uptake from the metal surface by SRB. 5.6 Supplemental information Figure S5–1 Carbon steel coupon surface after 130 days’ immersion in R1. Figure S5–2 3D images of the coupon surfaces on Day 46 (a), Day 90 (b) and Day 130 (c) in R1. Figure S5–3 Photo of the coupon surface after 24 days’ immersion in R1. 49 600 800 Sulfide 700 Sulfate 400 Propionic 600 Acetic 500 Lactic 60 200 40 20 Concentration (mg/L) Concentration (mg-S/L) 0 0 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Time (day) Time (day) Figure S5–4 Volatile fatty acid (VFA) and sulfur species concentration in R2. 600 800 Sulfate 700 Sulfide Propionic 600 400 Acetic Lactic 500 60 200 40 20 Concentration (mg/L) Concentration (mg-S/L) 0 0 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Time (day) Time (day) Figure S5–5 Volatile fatty acid (VFA) and sulfur species concentration in R3. 50 60000 50000 40000 ) ) 40000 2 2 30000 cm cm Day 1 Day 12 Ω⋅ Ω⋅ Day 2 Day 14 Day 4 20000 Day 17 -Z”( -Z”( 20000 Day 6 Day 21 Day 25 Day 8 10000 Day 27 Day 10 Day 12 0 0 0 20000 40000 60000 80000 0 10000 20000 30000 40000 50000 Z’( cm2) Z’( cm2) Ω⋅ Ω⋅ 1500 200 5000 Day 90 Day 96 Day 102 4000 150 Day 107 Day 84 1000 ) Day 114 ) ) 2 2 2 3000 Day 80 Day 122 cm cm Day 130 cm Day 74 100 Ω⋅ Ω⋅ Ω⋅ Day 30 Day 70 Day 142 2000 Day 34 Day 64 -Z”( Day 37 -Z”( -Z”( 500 Day 41 Day 60 Day 45 Day 56 50 Day 48 1000 Day 52 Day 48 0 0 0 0 500 1000 1500 0 200 400 600 800 1000 0 50 100 150 2 2 2 Z’(Ω⋅cm ) Z’(Ω⋅cm ) Z’(Ω⋅cm ) Figure S5–6 Nyquist plots of the carbon steel working electrode in R2. 51 50000 60000 40000 ) ) 2 2 40000 Day 1 30000 Day 12 cm cm Day 2 Day 14 Ω⋅ Day 4 Ω⋅ Day 17 20000 Day 21 -Z”( Day 6 -Z”( 20000 Day 25 Day 8 Day 27 Day 10 10000 Day 12 0 0 0 20000 40000 60000 80000 0 10000 20000 30000 40000 Z’( cm2) Z’( cm2) Ω⋅ Ω⋅ 100 3000 1000 80 Day 84 ) 2 ) 2000 ) 2 2 60 Day 80 Day 90 cm cm cm Day 74 Day 96 500 Ω⋅ Ω⋅ Day 30 Ω⋅ Day 70 Day 102 Day 34 40 Day 64 -Z”( Day 107 -Z”( 1000 Day 37 -Z”( Day 60 Day 114 Day 43 Day 122 Day 45 Day 56 20 Day 130 Day 48 Day 52 Day 48 Day 142 0 0 0 0 500 1000 1500 0 200 400 600 800 1000 0 20 40 60 80 100 Z’(Ω⋅cm2) Z’(Ω⋅cm2) Z’(Ω⋅cm2) Figure S5–7 Nyquist plots of the carbon steel working electrode in R3. 52 Table S5–1 Fitting results of EIS tests of carbon steel working electrode in R2 during 142-day experimental study. RS Qf nf Rf WR WP Qdl ndl RCT Day WT (s) (Ω·cm2) (0 53 Table S5–2 Fitting results of EIS tests of carbon steel working electrode in R3 during 142-day experimental study. Qf nf Rf WR WP Qdl ndl RCT Day RS WT (s) (Ω·cm2) (0 36 11.49 0.003047 0.646 8.707 10.96 0.4974 0.5 6.567×10-3 0.8943 67185 39 14.51 0.003387 0.6363 10.52 10.26 0.3811 0.5 7.441×10-3 0.8976 18217 43 14.2 0.003965 0.6444 9.408 9.47 0.1805 0.5 9.211×10-3 0.8567 3954 46 13.62 0.004441 0.6411 9.246 9.77 0.1874 0.5 8.975×10-3 0.8518 1336 49 14.41 0.005555 0.6322 10.44 8.47 0.2462 0.5 8.85×10-3 0.8601 1032 52 13.54 0.005993 0.6402 11.49 10.64 0.3509 0.5 1.02×10-2 0.8783 446.5 56 14.1 0.005768 0.6711 12.13 10.49 0.4319 0.5 1.19×10-2 0.8763 327.7 60 14.04 0.006105 0.6747 12.27 7.12 0.3092 0.5 1.27×10-2 0.8676 385.9 64 15.01 0.007567 0.6769 11.34 8.39 0.3371 0.5 1.341×10-2 0.8401 370.3 70 14.39 0.007418 0.7235 9.655 8.546 0.4906 0.5 1.739×10-2 0.7789 473.5 74 14.23 0.01062 0.6535 13.06 6.66 0.2644 0.5 1.803×10-2 0.823 191.9 80 14.57 0.01794 0.5514 70.1 185.8 0.2902 0.5712 84 14.27 0.009359 0.6943 9.553 707.6 73.41 0.6395 88 14.01 0.01494 0.6172 10.49 188.4 19.93 0.4436 92 13.88 0.008579 0.7636 6.313 508.8 225 0.5523 98 14.31 0.03457 0.5186 21.76 163.7 14.61 0.5288 102 13.28 0.03324 0.585 19.13 16.64 1.528 0.4156 104 13.25 0.03524 0.5877 18.45 17.64 1.719 0.4212 109 12.36 0.04114 0.5751 25.17 18.16 1.296 0.4266 112 11.93 0.03449 0.6219 11.56 16.34 1.958 0.4154 116 11.64 0.03235 0.6397 8.568 16.7 2.046 0.4035 120 12.54 0.0288 0.6516 8.648 20.9 2.456 0.395 130 11.87 0.02579 0.6765 9.872 19.45 2.365 0.4052 142 12.85 0.02318 0.6612 9.672 18.53 2.487 0.4188 54 Chapter 6 Decreasing microbially influenced metal corrosion using free nitrous acid in a simulated water injection system 6.1 Introduction As reviewed in section 2.4, FNA has strong biocidal effects on sewer biofilms, based on which we hypothesized that FNA would also be able to deactivate the biofilm in a water injection system. However, as discussed in section 2.6, the effect of FNA on steel corrosion cannot be directly inferred from previous sewer work due to the more complex nature of metal corrosion. This study aims to experimentally investigate the effectiveness and the detailed effects of FNA dosing in reducing metal corrosion in water injection systems. Two laboratory-scale water injection systems were used in this study, one as the experimental system receiving intermittent FNA dosing, and the other as a control. Weight-loss and 3D optical profiling were used to determine the general and pitting corrosion, respectively. Electrochemical measurements, including OCP, EIS, and LPR, and ATP measurements were carried out to reveal the mechanisms involved in corrosion reduction. This study potentially delivers a cost-effective and environment-friendly technology for corrosion control in water injection systems. 6.2 Materials and methods 6.2.1 Experimental procedure The three simulated water injection systems set up in Chapter 5 continued to operate in continuous mode with same monitoring procedure. To determine the FNA concentration to be applied to the simulated water system, a pre-test was conducted. At Day 238, 13 coupons in one of the reactors were sampled aseptically, one serving as control, and the rest 12 were treated with nitrite concentrations of 50, 100, and 200 mg-N/L for 3 h, - pH 6 h, 12 h, and 24 h, at pH 6. The calculated FNA concentrations (FNA as HNO2-N=NO2 -N/(Ka´10 ), where Ka=e-2300/(270+T(°C))) were 0.12, 0.24, and 0.49 mg-N/L, respectively (Anthonisen et al. 1976). The viability of the coupons before and after the treatment was assessed by LIVE/DEAD staining. Subsequently, 0.49 mg-N/L FNA with 24-hour treatment was selected to be the dosing strategy for this study (Figure S6–1 & Figure S6–2). This reactor was shut down thereafter, with the remaining two reactors serving as the experimental and control reactor, respectively. 55 Thereafter, one of the other 2 reactors was treated with 0.49 mg-N/L FNA (Experimental), and the remaining one served as the control without FNA addition (Control). At Day 244, the pH of the Experimental reactor was adjusted to 6 by 1 mol/L HCl, and 5.00 mL NaNO2 stock solution (394.286 - g/L) was added to reach a concentration of 200 mg-N/L NO2 within the reactor. The pH was maintained at 6 during the 24-hour treatment. The feeding pump was stopped before the 1st FNA dosing, and was restarted after 24 hours. The Experimental reactor was allowed to recover for 82 days, and the 2nd FNA treatment was applied at Day 326. It was the same procedure as the 1st treatment, except that the reactor was fed with 10 L fresh medium to remove the FNA and fully replenish the reactor. 6.2.2 Analysis Dissolved sulfur compounds, and lactate and acetate were monitored regularly in reactor medium (section 4.9). Electrochemical measurements were conducted every 24 hours during the experiment (section 4.3). During the experimental period, coupons were removed from each of the reactors in an anaerobic chamber every 15 ± 2 days for analysis. The cellular ATP levels of the biofilm on the coupons were analyzed immediately after removed (section 4.4). Then, all the coupons were carefully washed, dried and analyzed with weight loss measurement (section 4.5) and 3D profiling (section 4.6). 6.3 Results and discussion 6.3.1 Reactor performance a 1st FNA 2nd FNA b 1st FNA 2nd FNA 200 400 Experimental Experimental Control 150 Control 300 100 200 Lactate (mg/L) Acetate (mg/L) 100 50 0 0 210 240 270 300 330 360 390 420 450 480 210 240 270 300 330 360 390 420 450 480 Time (day) Time (day) 56 c 1st FNA 2nd FNA d 1st FNA 2nd FNA 700 80 Experimental Experimental Control Control 650 60 600 40 Sulfide (mg S/L) Sulfate (mg S/L) 550 20 500 0 210 240 270 300 330 360 390 420 450 480 210 240 270 300 330 360 390 420 450 480 Time (day) Time (day) e 1st FNA 2nd FNA 8 ) 6 2 4 Experimental Control ATP (nmol/cm ATP 2 0 210 240 270 300 330 360 390 420 450 480 Time (day) Figure 6–1 Lactate (a), acetate (b), sulfate (c), sulfide (d), and biofilm ATP (e) concentrations in Experimental and Control reactors, respectively. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment (black arrows) were conducted respectively. Before the 1st FNA treatment to the Experimental reactor at Day 244, Experimental and Control reactors were both operated under the same conditions, and reached relatively stable states. To illustrate the effects of the FNA treatment, Figure 6–1 shows concentrations of relevant parameters in the reactors from Day 198 to Day 487. As Figure 6–1a shows, the concentration of lactate was low (0 to 10 mg/L) both in Experimental and Control reactors before Day 244. Three days after the 1st FNA treatment, the lactate concentration peaked around 190 mg/L (Day 247), after which it progressively decreased to < 10 mg/L at Day 256. Similarly, after the 2nd FNA treatment, the lactate concentration peaked to 432 mg/L after the medium was replaced with 10 L fresh synthetic seawater at Day 327. It took 10 days for the lactate to decrease to the same low level prior to FNA treatment. In Figure 6–1b, the acetate concentration in the Experimental reactor decreased immediately after both FNA treatments, which was likely due to the low microbial viability of the biofilm and the suspended cells in the medium following FNA treatment. Acetate then recovered and peaked after approximately 10 days, indicating microbial recovery. Gradually, acetate in the Experimental reactor 57 decreased to close to the baseline level after around 60 days. In contrast, acetate in the Control remained stable. Similar to the lactate trend in the Experimental reactor, the sulfate concentration shown in Figure 6– 1c increased immediately after FNA treatment. Sulfate concentration began to decrease 10 days after the 1st FNA treatment, and 2 days after the 2nd treatment. Notably, sulfate concentration kept decreasing below the baseline of ca. 571 mg/L to around 515 mg/L prior to the 2nd FNA treatment. As shown in Figure 6–1d, the sulfide concentration in the Experimental reactor dropped to 0 during both FNA treatments, which would be due to the H2S emission along with the HCl addition to adjust pH for FNA treatment (Figure S6–4). The sulfide level gradually increased after the treatments, inversely to the sulfate concentration in Figure 6–1c. From Day 300 to Day 330, the levels of both acetate (Figure 6–1b) and sulfate (Figure 6–1c) were lower, and the level of sulfide (Figure 6–1d) was higher than those after 2nd FNA treatment. This would be attributed to the different initial states after the FNA treatments. The slow dilution of the medium after 1st FNA treatment allowed the inhibited and removed microbes stayed in the reactor, but the microbes were completely removed from the reactors after 2nd FNA treatment. ATP assessment (Figure 6–1e) showed that the viability of the biofilm on the coupon surface suddenly decreased to <20% of the previous level, within 24 h after FNA treatment. This result was in accordance with the LIVE/DEAD staining results (Figure S6–1 and Figure S6–2). The loss in viability could have shifted the reaction on the biofilm/metal interface from biocatalytic to chemical (Hernández Gayosso et al. 2005, Raman et al. 2008). The biofilm recovered to 90% of baseline activity in approximately one month after each FNA treatment. 6.3.2 General corrosion by weight-loss measurement 1st FNA 2nd FNA 1.0 0.8 0.6 Experimental 0.4 Control 0.2 Corrosion rate (mm/y) 0.0 180 210 240 270 300 330 360 390 420 450 480 Time (day) Figure 6–2 General corrosion rate calculated by the weight-loss measurement for coupons taken from Experimental and Control reactors, respectively. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment (black arrows) were conducted respectively. 58 Weight-loss measurement provides an accurate and reliable indication of the general corrosion rate (Afolabi et al. 2014, Lebrini et al. 2006). Figure 6–2 shows that the 1st FNA treatment decreased the general corrosion rate by 18.6% on average compared to that before FNA treatment, i.e. from ca. 0.86 mm/y to 0.70 mm/y. The general corrosion rate was kept stable at approx. 0.70 mm/y during the 82- day interval between the FNA treatments. After the 2nd FNA treatment, the general corrosion rate decreased to the lowest level at ca. 0.59 mm/y (15 days post-treatment), and it was inhibited by 31.4% compared with the baseline level (prior to any FNA dosing). The corrosion rate reached the previous level (prior to the 2nd FNA treatment) at Day 408, and it reached the baseline level at Day 459. The general corrosion rate in the Control remained relatively stable at around 0.85 to 0.95 mm/y, which indicated that the changes in the Experimental reactor were due to the FNA treatments. Overall, both of the FNA treatments slowed down the general corrosion rate immediately. However, the corrosive activity recovered more quickly after the 2nd FNA treatment than after the 1st treatment. From the above weight-loss measurements, FNA showed moderate inhibition effects on general corrosion. 6.3.3 3D optical profiling of corroded coupon surface a b 120 120 Day 183 Day 244 90 90 Experimental Experimental 60 Control 60 Control 30 30 Cumulative distribution (%) 0 Cumulative distribution (%) 0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Depth (µm) Depth (µm) c d 120 120 Day 326 Day 370 90 90 Experimental 60 Experimental 60 Control Control 30 30 Cumulative distribution (%) Cumulative distribution (%) 0 0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Depth (µm) Depth (µm) 59 e f 120 120 Day 451 Day 487 Experimental Control 90 90 Experimental 60 60 Control 30 30 Cumulative distribution (%) 0 Cumulative distribution (%) 0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Depth (µm) Depth (µm) g 1st FNA 2nd FNA h 1st FNA 2nd FNA 300 200 250 Experimental m/y) µ Control 150 200 150 100 Depth (µm) 100 50 Experimental 50 Control Pitting corrosion rate ( 0 0 180 210 240 270 300 330 360 390 420 450 480 180 210 240 270 300 330 360 390 420 450 480 Time (day) Time (day) Figure 6–3 Pitting corrosion generated from 3D profiling. The cumulative distribution of the pitting depth in Experimental and Control reactors, respectively at Day 183 (a), Day 244 (right before 1st FNA treatment) (b), Day 326 (right before 2nd FNA treatment) (c), Day 370 (d), Day 451 (e), and Day 487 (f). The dashed lines in (a) to (f) show the depth when the cumulative distribution reaches 90%. The pitting depth at 90% cumulative distribution is summarized in (g). The pitting corrosion rates calculated from (g) are shown in (h). The cumulative distribution of the corrosion depth in Experimental and Control reactors (Figure 6–3 a-f) was generated based on 3D images, with calculation details shown in Figure S6–6 and Figure S6–7. Prior to FNA treatment, at Day 183 and Day 244 (Figure 6–3 a & b), the cumulative depth distribution increased smoothly vs. the depth in both Experimental and Control reactors, which indicated that no sharp pits were formed on the coupon surfaces. At Day 244, the corrosion depth in Experimental was slightly higher (ca. 13 µm) than that in Control (Figure 6–3 b & g), which could be regarded as normal variability between the individual reactors under same operating conditions. At Day 326 (82 days post 1st FNA treatment and right before the 2nd FNA treatment), the cumulative distribution started to show uneven increase in both reactors, but it was more obvious in Control reactor (Figure 6–3c). The pitting depth in Control started to surpass that in Experimental (Figure 6– 3 c & g), and the pitting depth at 90% cumulative distribution increased 50.6 µm and 8.0 µm from 60 Day 244 to Day 326 in Control and Experimental, respectively. It implied that the 1st FNA treatment inhibited the pitting corrosion in Experimental, and the inhibition efficiency was approx. 84.2% after the 1st FNA treatment (during the 82-day interval between the FNA treatments). From then onwards, the cumulative distribution increased more unevenly in Control than that in Experimental (Figure 6– 3 d-f), while no deep pits formed in Experimental. The averaged pitting corrosion rate (Figure 6–3h) calculated according to the depth at 90% cumulative distribution (Figure 6–3g) shows that the pitting corrosion rate was generally stable at ca. 70 – 100 µm/y throughout the experimental period in the Experimental reactor. In contrast, starting from a similar level, the pitting corrosion rate in the Control reactor increased approximately linearly from Day 244, doubling that in the Experimental reactor towards the end of the experimental period. The results indicated that 2 intermittent FNA treatments at an 82-day interval over 304 days effectively prevented severe pitting from developing in the Experimental reactor. Both pitting depth plots (Figure 6–3g) and pitting corrosion rate diagrams (Figure 6–3h) showed that the pitting corrosion accelerated from Day 326 in Control, which indicated that microbes tended to attack subtle areas that were previously corroded on the metal surface. As shown in our previous study, a dense and compact biofilm formed on the coupon surface could be a barrier for the transfer of carbon substrates from the bulk liquid phase to deep layers of the biofilm (Zhong et al. 2019). The diffusion limitation likely caused low carbon concentrations at the bottom of the biofilm (i.e. the metal surface), where SRB would turn to Fe0 for electrons for substitution, triggering more pitting corrosion (Gu et al. 2009, Venzlaff et al. 2013). In the Experimental reactor, biofilm was interrupted twice by the FNA treatments, with much lowered biofilm activity and possibly thinner biofilm following each treatment (Jiang and Yuan 2013). This likely alleviated the diffusion limitation of carbon substrates, thus reduced pitting corrosion. The results also showed that it is desirable to control and mitigate corrosion at early stages of the corrosion development. Pitting corrosion is known to be more detrimental to pipeline integrity than general corrosion in the oil recovery operation (Muthukumar et al. 2003). As compared with general corrosion (Figure 6–2), pitting corrosion was more effectively controlled by FNA treatment (Figure 6–3). Interestingly, the pitting corrosion rates measured in this study were lower than the general corrosion rates measured based on weight loss (Figure 6–2). The measuring reference (depth = 0) on coupon surface was set as the highest point on the surface, which varied on different coupons and normally became lower along with immersion. Thus, the pitting results shown were not compared to the initial uncorroded surface level, which led to the deviation with the general corrosion results. 61 6.3.4 FNA’s effect on electrochemical properties of the carbon steel coupons a 1st FNA 2nd FNA b 1st FNA 2nd FNA -0.45 1000 Experimental Experimental Control -0.50 Control 800 ) -0.55 2 600 cm Ω⋅ ( -0.60 P 400 R OCP (V vs Ag/AgCl) -0.65 200 -0.70 0 210 240 270 300 330 360 390 420 450 480 210 240 270 300 330 360 390 420 450 480 Time (day) Time (day) Figure 6–4 OCP (a), and polarization resistance (Rp) calculated from LPR measurements (b) of the carbon steel working electrode in Experimental and Control reactors. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment (black arrows) were conducted respectively. Figure 6–4a shows that the OCP increased immediately in the Experimental reactor to around -0.49 V after both FNA dosages. This may have been due to the FNA dosing, with sudden pH drop to 6 - - and ions addition including Cl and NO2 (Figure S6–4). It was also found in a study of pipeline steel in simulated soil solutions that OCP increased along with pH decrease from neutral to acidic levels (Gadala and Alfantazi 2014). The OCP of the Experimental reactor decreased gradually to around - 0.65 V 50 days after the 1st FNA dosage. After the 2nd FNA treatment, the OCP decreased even further to around -0.68 V. In contrast, the OCP level in the Control was steady at around -0.56 V over the course of the experiment. A more positive potential usually means that the working electrode is less affected by the electrolyte. However, OCP is influenced by many factors and it does not provide a direct indication of corrosion rate (Stansbury and Buchanan 2000). In Figure 6–4b, the linear polarization resistance (Rp) increased sharply after both FNA treatments in the Experimental reactor. Because the corrosion rate is generally inversely correlated with Rp, it can be deduced that the corrosion rate decreased following the FNA treatments, supporting the results based on weight-loss measurement. Based on the Rp values, the corrosion rate should have decreased by 53.1% following the 1st FNA treatment, and by 55.4% after the 2nd FNA treatment. This indirect measurement of corrosion inhibition was much higher than that calculated by direct weight-loss measurement at 31.4%. This difference in result is not uncommon because electrochemical measurements are affected by a number of factors/errors, including simplification of corrosion reaction processes, activation vs. concentration difference polarization, and selection of conversion factors (Liu and Cheng 2018). The Rp in the Experimental reactor decreased to the baseline level (ca. 240 Ω⋅cm2) 4 weeks after the 1st FNA treatment, and then gradually increased to reach a steady state at 360 Ω⋅cm2 at Day 301. However, the Rp value rapidly increased and remained at a high level (ca. 62 700 Ω⋅cm2) for more than 4 months after the 2nd FNA treatment before it showed signs of decreasing after Day 459. The Rp in the Control slightly increased over the 300 days from 200 Ω⋅cm2 to 300 Ω⋅cm2, but was otherwise stable as compared with that in the Experimental reactor. 120 120 160 a D244.5 D258 b c D244.125 D252 Fitting line D329 D487 D244 D248 D453 90 D327.5 D237 D246 90 120 D426 D326.5 Fitting line D245 D392 ) ) ) 2 D326.125 2 2 D372 D326 cm cm cm ⋅ ⋅ ⋅ D365 60 80 Ω 60 Ω Ω D320 D353 D307 D347 -Z” ( -Z” ( -Z” ( D294 D339 D270 Fitting line 30 30 40 0 0 0 0 20 40 60 0 20 40 60 0 20 40 60 Z’ (Ω⋅cm2) Z’ (Ω⋅cm2) Z’ (Ω⋅cm2) 200 -90 200 -90 200 -90 d e D244.5 D258 D326 Fitting line f Fitting line D372 D244.125 D252 D329 D487 150 -60 150 D320 -60 150 D265 -60 ) ) D244 D248 D327.5 ) 2 D307 D453 D253 2 2 D237 D246 D326.5 D426 cm D294 D347 cm cm ⋅ ⋅ ⋅ Ω 100 100 100 Ω D245 D326.125 Ω Fitting line -30 D270 -30 D392 D339 -30 |Z| ( |Z| ( |Z| ( Phase (degree) Phase (degree) 50 50 50 Phase (degree) 0 0 0 0 0 0 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 log (f, Hz) log (f, Hz) log (f, Hz) Q g f h W RS O RS Rf Figure 6–5 Nyquist (a, b, & c) and Bode (d, e & f) plots of the carbon steel working electrode in the Experimental reactor. Z’ and Z” represent the real and imaginary parts of the impedance, respectively. |Z| is the absolute impedance indicated by the red arrow, while the phase angle is indicated by the black arrow in the Bode plots (d, e & f). (a) and (d), (b) and (e), and (c) and (f) show the results from Day 237 to Day 258 (D237-D258), Day 270 to Day 329 (D270-D329), and Day 339 to Day 487, respectively. At Day 244 and Day 326, the FNA treatments were applied respectively (legends marked as red), and the plots show the results right before the FNA dosages (D244 & D326). (g) and (h) are the equivalent circuit models used to fit the measured EIS plots (a-f). Rs is the solution resistance, Rf and Qf are the resistance and capacitance of the biofilm, respectively. Wo denotes the finite length Warburg (FLW) element. 63 The Nyquist plots of the 1st FNA treatment are presented in Figure 6–5a. The plots at Day 237 before FNA treatment were linear, with an imaginary impedance (-Z”) of 40.9 Ω·cm2 and a real impedance (Z’) of 19.6 Ω·cm2 at 0.01 Hz. Twelve hours after the 1st FNA dosage, the Nyquist plots morphed from a linear-shape to an arc-shape (Day 244.5) (Figure 6–5a). The shape change in the Nyquist plots indicated the interface reactions and variations on the coupon surface. EIS is a powerful analytical technique to describe the physicochemical changes at the interface between the medium and electrode, which provides a rapid, sensitive and nondestructive platform based on measuring impedance variations (Ansari et al. 2017, Su et al. 2013). The diameters of the arc-shaped plots showed that the plots started to fluctuate from Day 244.5 to Day 258, and the plot shapes morphed back to linear- shaped at Day 307 (Figure 6–5 a & b). This indicated that the layers composed of biofilm and corrosion products gradually formed on the surface after the 1st FNA treatment until Day 307, and started to limit the diffusion of the substrates in the medium to the metal surface. The impedance stayed relatively steady, as the Nyquist plots at Day 307, Day 320, and Day 326 were linear-shaped with comparable lengths and slopes (Figure 6–5b). The Bode plots in Figure 6–5e also showed obvious changes in phase angle at frequency of 10-2 Hz to 10-1 Hz from Day 294 to Day 307, indicating that the dominant simulating electrical component in the testing system has changed. After the 2nd FNA treatment applied at Day 326 (Figure 6–5b), the shape of the Nyquist plots remained linear, which indicated that although the microbes were likely killed by the 2nd FNA treatment (Figure 6–1e), the structure of the layers on working electrode surface did not change as much as after the 1st FNA treatment. At Day 426, the imaginary part of the impedance (-Z”, Figure 6–5b) began to increase gradually, experiencing fluctuations, to 148.5 Ω·cm2 at 0.01 Hz. This impedance level was maintained for 4 weeks, and after Day 453 it decreased slowly. Figure 6–5f shows that the phase angle did not change as per Figure 6–5e, indicating that surface properties were relatively stable with the 2nd FNA treatment. The absolute impedance at 0.01 Hz (|Z|, Figure 6–5f) generally increased and more than doubled from 76.2 at Day 320 to 163.7 at Day 453. Combined, these results indicate that a more compact biofilm was established after the 1st FNA treatment. The impedance of the working electrode in the Control (Figure S6–3) remained stable from Day 249 to Day 486 with an imaginary part of 36.2 Ω·cm2 and a real part of 22.0 Ω·cm2 at 0.01 Hz. Both imaginary and real impedance were lower than that in Experimental (Figure 6–5 a & b). After Day 244 (1st FNA treatment in Experimental), the impedance in Experimental showed a trend of increasing, while it stayed relatively stable in Control, which was in accordance with the LPR results shown in Figure 6–4b. 64 Two equivalent circuits (Figure 6–5g & h) were applied to fit the measured EIS results to gain a quantitative understanding of the electrochemical parameters on the carbon steel working electrode surface in the Experimental reactor. In the circuits, Rs is the solution resistance, and Rf and Qf are the resistance and capacitance of the biofilm, respectively. Wo denotes the FLW element. The adequacy of the equivalent circuit models was maintained with fitting errors less than 10% for all fitted parameters. The working electrode surface was unlikely to be smooth but with microscopic roughness, so the constant phase element (Q), a non-ideal capacitor, was used to fit the distributed capacitance instead of an ideal capacitor. The impedance of Q (ZQ) was calculated using equation 6-1: $ Z" = + (6-1) %&(()) where Y0 and n are frequency independent parameters indicating the deviation of the specimen from an ideal capacity, and w is the angular frequency of the alternating voltage in rad s-1. The fitting results are shown in Table 6–1. WR represents the diffusion impedance, while WP is an exponential factor. WT is the time taken for the substrates to diffuse throughout the system. WT is calculated using the following equation 6-2: 1 - = 0 (6-2) . 2 where L (cm2) is the diffusion layer thickness and D (cm2 s-1) is the diffusion coefficient. Table 6–1 Fitting results for the EIS measurements of the Experimental reactor. At Day 244 and Day 326, the 1st FNA and 2nd FNA treatment were conducted respectively, and the data show the results right before the FNA dosages. RS Qf Rf WR Day nf (0 65 307 13.24 5.671 0.6530 0.4414 320 16.31 5.58 0.6262 0.4423 322 16.16 6.04 0.6871 0.4427 326 17.98 5.462 0.5373 0.4251 326.125 12.13 4.891 0.4286 0.4383 326.5 11.50 7.982 0.7718 0.4325 327.5 11.08 7.956 0.8753 0.4175 329 11.63 7.348 1.0170 0.4368 333 12.20 7.405 1.2450 0.4460 339 13.12 7.85 1.3850 0.4525 343 12.47 7.14 1.0500 0.4446 347 12.67 7.018 1.0010 0.4457 353 12.95 7.192 0.9888 0.4447 365 12.96 6.831 0.6754 0.4471 372 12.74 6.945 0.6156 0.4450 379 12.43 6.466 0.5608 0.4458 385 12.20 6.483 0.5561 0.4477 392 12.39 7.398 0.6457 0.4493 400 14.92 6.547 0.4915 0.4504 408 12.90 6.369 0.4736 0.4500 426 12.89 6.92 0.5291 0.4493 453 13.09 6.648 0.4844 0.4409 459 21.88 6.551 0.4645 0.4487 487 15.87 6.094 0.4212 0.4297 As shown in Table 6–1, before the 1st FNA treatment (Day 237 and Day 244) and up to 3 hours after the treatment (Day 244.125), the reactions on the working electrode surface were dominated by the diffusion limitation as shown in Figure 6–5a, so the EIS results were fitted by the circuit with the Warburg element (Wo) shown in Figure 6–5h. Three hours after the 1st treatment, WR and WT both decreased, indicating a decrease in diffusional transport on the working electrode surface. The diffusional effect disappeared 12 hours after the 1st FNA treatment (Day 244.5), so the results were fitted with the circuit shown in Figure 6–5g, where the Warburg element (Wo) is replaced with a constant phase element (Qf) and a paralleled resistance (Rf). After the FNA treatments, the dead cells on the surface of the biofilm were likely sloughed from the coupon surface with vortexing, leading to a decrement in biofilm thickness, also eliminating the diffusion control effect (Hernández Gayosso et al. 2005). The deviation of the double-layer capacitance from an ideal capacity (nf) was in a relatively steady state of approximately 0.82 up to Day 248, after which nf dropped gradually to 0.5094 at Day 270, indicating that the electrode surface was becoming rough and porous (Sheng et al. 2007). It was also a sign that the biofilm was re-developing on the electrode surface. The diffusion limitation effect returned from Day 307, which was a result of both the re-developing biofilm (as 66 indicated by increasing biofilm ATP levels in Figure 6–1e) and the low concentration of available 2 carbon in the medium (Figure 6–1a, b). From Day 307 to Day 322, WR (around 5.5 to 6.0 Ω·cm ) was higher than that (4.1 Ω·cm2) before the 1st FNA treatment, which indicated a higher resistance in the electrode surface layer. Unlike the 1st FNA treatment, the EIS results after the 2nd FNA treatment still fitted the circuit shown in Figure 6–5h, as the shape of the Nyquist plots did not change after Day 326 (Figure 6–5 b & c). Both WR and WT increased 12 hours after the 2nd FNA treatment (Table 6–1, Day 326.5), indicating an increasing resistance and diffusion time. According to the Pourbaix Diagram (EH-pH) for H2S- H2O-Fe, iron sulfide precipitations are usually stable at pH 6 (Ning et al. 2015), indicating that the corrosion products remain on the coupon surface instead of dissolve into the reactor medium. While the microbial metabolites and the corrosion products remain on the coupon surface, the removal of cells forms voids and tunnels that favor adherence of new cells (Simões et al. 2011). After the 1st FNA treatment, a new layer of biofilm with corrosion products developed on top of the remaining corrosion products, resulting in a much thicker layer on the electrode surface before the 2nd FNA treatment than that before the 1st FNA treatment. This may be a contributing factor to the remaining biofilm’s diffusion limitation effect after the 2nd FNA treatment. It is likely that some of the dead cells were decomposed to biomacromolecules, such as polysaccharide (Nakas and Klein 1979), attached to the coupon surfaces, enhancing the diffusion limitation effect. WT and WR stayed at relatively high values up to Day 353, and from Day 365 the diffusional effect decreased to a level with a lower WT but a higher WR compared with that before the 1st FNA treatment at Day 237. Overall, the EIS results were consistent with LPR measurements. The working electrode surface properties and the dominant reactions revealed by EIS analysis supported the general (Figure 6–2) and pitting (Figure 6–3) corrosion results. The electrical components in the equivalent circuits interpreted the mechanisms of the MIC process on the steel coupon surface. 6.3.5 Potential for FNA dosing as a corrosion control technology In this study, we demonstrated that intermitted dosing of FNA to inject water could significantly reduce the metal corrosion rate in water injection systems. With 2 intermittent FNA treatments at an 82-day interval over 304 days, FNA dosing decreased the averaged pitting corrosion rate by a maximum of 58.5% as compared with the control without FNA. General corrosion was also reduced by up to 31%. Pitting corrosion is known to be more detrimental to pipeline integrity than general corrosion in the oil recovery operation (Muthukumar et al. 2003). To mitigate corrosion in oilfield infrastructure, an intermittent, monthly FNA dosage strategy would be an effective alternative to the 67 currently used organic biocides which are usually applied continuously or by batch treatment every 2 to 4 weeks (OilfieldWiki 2016, Papavinasam 2014). The dosing of nitrite to injected water was previously investigated (Gardner and Stewart 2002). Without simultaneously lowering pH of the injected water, the addition of nitrite at 110 mg-N/L for 24 hours to a continuous-flow annular reactor with SRB biofilms developed on polycarbonate slides only caused a temporary reduction in the SBR activity, which recovered to 90% of the baseline level only 32 hours after the dosing (Gardner and Stewart 2002). This was likely due to the fact that FNA rather than nitrite was the actual biocidal agent. At pH of 7.2, the FNA level applied in Gardner and Stewart (2002) is estimated to be only 0.019 mg-N/L, which is an order of magnitude lower that the FNA level used in this study. Also, Gardner et al. (Gardner and Stewart 2002) only focused on SRB activities and did not evaluate the metal corrosion process. The FNA dosing in this study was achieved through the simultaneous addition of sodium nitrite (approximately 450 USD per ton of sodium nitrite powder) and hydrochloric acid (approximately 150 USD per ton of 32% solution), both are commonly available chemicals with relatively low costs. In our case, the estimated cost for dosing sodium nitrite at 200 mg-N/L for 24 hours, applied once a month, would be 0.015 USD/m3 (averaged to the total flow over a year, see section 6.5 for calculation details). The in-situ pH of oil reservoirs typically ranges from 3 to 7 (Magot et al. 2000, Pannekens et al. 2019). When pH is lower than 6, the amount of sodium nitrite needed to reach 0.49 mg HNO2- N/L would be lower than 200 mg-N/L, and hence the cost for sodium nitrite would be lower than what calculated above. If the pH is above 6, hydrochloric acid needs to be added. For the upper end of the pH range, e.g. 7, the cost of hydrochloric acid could be up to 0.050 USD/m3 (yearly averaged, see section 6.5 for calculation details), assuming that carbonic acid is saturated. The commonly used glutaraldehyde (approximately 2000 USD per ton of 50% solution) is typically dosed daily or weekly at 50 to 2,500 ppm for up to 4 hours in water injection systems (Company T.D.C. 2012), for which the cost would be 5 to 100 times higher compared to FNA-based treatment. THPS (approximately 3000 USD per ton of 75% solution) is normally dosed continuously at 14 to 67 ppm in injection water (Company T.D.C. 2009), 15 times more expensive than FNA treatment. Intermittent FNA treatment is more economical compared with glutaraldehyde and THPS strategies. At the concentration added (0.49 mg HNO2-N/L), FNA has been shown to be biocidal to microorganisms in both this study and previous studies (Jiang et al. 2011b). However, FNA would be substantially diluted when the injected water reaches reservoirs. Previous studies have shown that, at low concentrations, nitrite is biodegradable, and would be easily removed by denitrifying organisms with N2 as the final product. Therefore, the FNA dosing strategy will not lead to pollution of the injected water. This represents a significant advantage of the proposed strategy over some of the 68 commonly used biocides. Under anaerobic conditions, glutaraldehyde was reported to be degraded to 1,5-pentanediol and 3-formyl-6-hydroxy-2-cyclohexene-1-propanal over 123 days, instead of mineralized to CO2 (Kahrilas et al. 2015, Leung 2001). The residue glutaraldehyde and its yields in the environment have toxicity to many freshwater and seawater organisms (McGinley et al. 2009). This intermittent FNA dosing strategy has strong potential to be applied to other anaerobic environments, such as marine environments (Ramírez et al. 2016) and cooling water utilities (Jia et al. 2017), where metal corrosion is induced by microbial activities. The FNA dosing frequency will depend on both the microbial growth rate and the corrosion rate. 6.4 Conclusions The inhibitory effect of FNA on carbon steel corrosion in a simulated water injection system was investigated in laboratory tests. The key findings are: • FNA has shown strong potential to be an effective and efficient method of controlling pitting corrosion in oil recovery infrastructures. The results from measuring cumulative distribution of the corrosion depth indicated that FNA prevents the formation of deep pits on the steel surface. • FNA exerted a moderate effect on slowing the general corrosion rate. • Intermittent FNA dosage is a promising strategy for MIC control in anaerobic environments. This technology could be cost-effective and environment-friendly, as nitrite is cheap, the application is relatively easy compared to coating, and the residual nitrite is biodegradable in the system. 69 6.5 Supplemental information Figure S6–1 Fluorescence images of stained live (green) and dead (red) cells on the surface of the coupons in reactor 1 exposed to nitrite concentrations of 50, 100, and 200 mg-N/L at pH 6, leading to FNA concentrations of 0.12, 0.24, and 0.49 mg-N/L, respectively. The exposure was for 3, 6, 12, and 24 h. 100 80 60 40 0 50 20 100 Viable biofilm cells (%) cells biofilm Viable 200 0 0 5 10 15 20 25 Time (hour) Figure S6–2 The viable fraction of biofilm cells assessed by live and dead staining after FNA treatment at concentrations of 0.12, 0.24, and 0.49 mg-N/L, for 3, 6, 12, and 24 h, respectively. 70 150 D486 120 D441 D425 ) 2 D393 90 cm D348 Ω⋅ D310 60 D288 -Z”( D249 D236 30 D190 0 0 20 40 60 Z’( cm2) Ω⋅ Figure S6–3 Nyquist plot of the carbon steel working electrode in the Control reactor from Day 190 to Day 486 (D190 to D486). Z’ and Z” represent the real and imaginary parts of the impedance, respectively. 1st FNA 2nd FNA 8.0 7.5 7.0 6.5 Experimental pH Control 6.0 5.5 5.0 210 240 270 300 330 360 390 420 450 480 Time (day) Figure S6–4 The pH levels in the Control and Experimental reactors. 1st FNA 2nd FNA 250 200 150 100 50 Concentration (mgN/L) 0 242 244 246 248 250 322 324 326 328 330 Time (day) Figure S6–5 The nitrite concentrations in the Experimental reactor. 71 40 6000 D183 30 m) 4000 µ D183 20 Count Height ( 2000 10 0 0 0 100 200 300 400 0 20 40 60 80 Distance (µm) Height (µm) 100 5000 80 D244 4000 D244 m) µ 60 3000 40 Count 2000 Height ( 20 1000 0 0 0 100 200 300 400 0 20 40 60 80 100 Distance (µm) Height (µm) 100 3000 80 D326 D326 2000 m) µ 60 40 Count 1000 Height ( 20 0 0 0 100 200 300 400 0 20 40 60 80 100 120 Distance (µm) Height (µm) 72 100 3000 80 D370 D370 2000 m) µ 60 40 Count 1000 Height ( 20 0 0 0 100 200 300 400 0 20 40 60 80 100 120 Distance (µm) Height (µm) 8000 D451 120 D451 6000 m) 90 µ 60 4000 Count Height ( 30 2000 0 0 0 200 400 600 0 30 60 90 120 Distance (µm) Height (µm) 12000 120 D487 D487 8000 m) 90 µ 60 Count 4000 Height ( 30 0 0 0 200 400 600 0 30 60 90 120 Distance (µm) Height (µm) Figure S6–6 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Experimental reactor. 73 60 D183 30000 D183 40 m) µ 20000 Count 20 Height ( 10000 0 0 0 100 200 300 400 0 20 40 60 80 Distance (µm) Height (µm) 80 5000 D244 D244 60 4000 m) µ 3000 40 Count 2000 Height ( 20 1000 0 0 0 100 200 300 400 0 20 40 60 80 100 Distance (µm) Height (µm) 160 4000 D326 120 3000 m) µ 80 2000 D326 Count Height ( 40 1000 0 0 0 100 200 300 400 0 40 80 120 160 Distance (µm) Height (µm) 74 25000 200 D370 D370 20000 160 m) µ 15000 120 Count 10000 80 Height ( 40 5000 0 0 0 200 400 600 0 40 80 120 160 200 Distance (µm) Height (µm) 8000 240 D451 D451 200 6000 m) µ 160 4000 120 Count Height ( 80 2000 40 0 0 0 500 1000 0 40 80 120 160 200 240 Distance (µm) Height (µm) 300 D487 250 D487 10000 200 m) µ 150 Count 5000 100 Height ( 50 0 0 0 300 600 900 0 40 80 120 160 200 240 280 Distance (µm) Height (µm) Figure S6–7 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control reactor. 75 Estimating FNA treatment costs To calculate the yearly averaged cost of the FNA treatment strategy (200 mg-N/L sodium nitrite at pH 6 for 24 hours monthly) proposed in our study, we assumed that the water flow through the pipe is Q (m3/d). The price of sodium nitrite powder is approximately 450 USD per ton. The concentration of sodium nitrite needed is 0.986 kg/m3. The dosing frequency is 1 d per month, i.e. 12 days in total over a year. The yearly averaged cost is calculated as follows: 0.986 kg⁄m3 × Q m3/d × 12 d/y Cost (sodium nitrite, yearly averaged)= × 0.45 USD/kg Q m3/d × 365 d/y = 0.015 USD/m3 The pH of oilfield waters is usually controlled by the CO2/bicarbonate system (PetroWiki 2016). The ionic equilibrium in water is: + - H2CO3 = H + HCO3 L The relation of pH and acid dissociation constant (Ka) is: pH = pKa − lg MNOPQ . L R MOPQ R The pKa is known to be 6.37. When pH is 7, STLUQ = 4.266 STNLUQ . To reduce the pH to 6, the added hydrochloric acid would fit the following equation: ST LU + STLW 6 = pKa − lg N Q R STLUQ − STLW STLW = 2.692 STNLUQ Provided that the carbonic acid is saturated (0.033 mol/L), the highest amount of hydrochloric acid needed is 3.24 kg/m3. So, the amount of HCl solution (32%, w/w) needed to add in the produced water is 10.13 kg/m3. 10.13 kg⁄m3 × Q m3/d × 12 d/y Cost (HCl(32%, w/w), yearly averaged)= × 0.15 USD/kg Q m3/d × 365 d/y = 0.050 USD/m3 76 Chapter 7 Combined corrosion inhibitory effects of free nitrous acid and imidazoline derivative in a simulated water injection system 7.1 Introduction Based on the results shown in Chapter 6, FNA has not only shown its strong biocidal effect on corrosive biofilm on steel coupons, but also been proven to be effective to inhibit MIC. In practical oil production process, the pipelines are usually maintained with mixed chemicals, including corrosion inhibitors and biocides (Heidersbach 2018, Kermani and Chevrot 2012). FNA has restrained inhibition effect on general corrosion, which could be enhanced by a combined dosage with corrosion inhibitors, which are known to have better ability to inhibit general corrosion. Research on the combined effect of FNA and corrosion inhibitors will be beneficial in evaluating FNA’s potential as a MIC controlling chemical, and an innovative FNA-based strategy would be developed as a substitute to control and mitigate MIC in real oil production process. As a typical imidazoline derivative, N-b-hydroxyethyl oleyl imidazoline (HEI-17) was selected to be applied with FNA in this study to investigate the combined effect on the corrosion behaviour of carbon steel. Because FNA’s effect on MIC has been thoroughly evaluated in Chapter 6, we focused on comparison of sole HEI-17 application, and both HEI-17 and FNA treatment in this chapter. The corrosion properties were investigated by electrochemical measurements, including OCP, EIS, and LPR tests. The general corrosion rate was calculated by weight-loss measurements, while the pitting corrosion was analysed by 3D optical profiling. 7.2 Materials and methods 7.2.1 Experimental procedure The structure of HEI-17 is shown in Figure 7–1. Figure 7–1 The molecular structure of HEI-17. 77 Three simulated water injection systems (Figure 4–1) were set up, one running as Control, with neither HEI-17 nor FNA dosed. Another two were operated with constant 20 ppm HEI-17 treatment both in the initial medium and the feeding medium, with one marked as Control + HEI-17 with no FNA treatment, and the other as Experimental, which was treated once with 200 mg N/L FNA at pH 6 (chosen based on Chapter 6 results) on Day 47 for 24 h. The concentration of the HEI-17 used in this study was selected according to previously published results (Ortega-Toledo et al. 2010, Villamizar et al. 2007). From Day 1 to Day 30, the reactors were operated batch-wisely, with medium replaced by 200 mL fresh synthetic medium every 4 days from Day 9, to supply nutrients. On Day 30, the reactors were switched to continuous feeding mode with an HRT of 4 days. 7.2.2 Analysis Water samples were taken before and after medium change during the batch mode before Day 30, and every 4 days during the continuous feeding mode (section 4.9). Electrochemical measurements were conducted every 24 hours during the experiment (section 4.3). From Day 15 to Day 120, two coupons were removed from each of the reactors in an anaerobic chamber every 15 ± 2 days for analysis. The cellular ATP levels of the biofilm on the coupons were analyzed immediately after removed (section 4.4). Then, all the coupons were carefully washed, dried and analyzed with weight loss measurement (section 4.5) and 3D profiling (section 4.6). 7.3 Results and discussion 7.3.1 Reactor performance Batch Batch feeding FNA to Exp feeding FNA to Exp a 500 b 300 400 Experimental Experimental Control + HEI-17 200 Control + HEI-17 300 Control Control 200 100 Lactate (mg/L) Acetate (mg/L) 100 0 0 0 30 60 90 120 0 30 60 90 120 Time(day) Time(day) 78 Batch Batch feeding FNA to Exp feeding FNA to Exp c 800 d 80 Experimental 600 60 Control + HEI-17 Control 400 40 Experimental Control + HEI-17 20 200 Sulfide (mg S/L) Sulfate (mg S/L) Control 0 0 0 30 60 90 120 0 30 60 90 120 Time(day) Time(day) Batch feeding FNA to Exp e 8 Experimental Control + HEI-17 ) 6 2 Control 4 ATP (nmol/cm ATP 2 0 0 30 60 90 120 Time(day) Figure 7–2 Lactate (a), acetate (b), sulfate (c), sulfide (d) concentration, and cellular ATP level (e) in biofilms in Control, Control + HEI-17, and Experimental reactors, respectively. On Day 47, the FNA treatment (black arrow) was conducted in the Experimental reactor. The organic carbon (lactate and acetate) concentrations were shown in Figure 7–2 a & b. Generally, the changes of the lactate and acetate concentrations were similar in Control + HEI-17 and Experimental before Day 47. The lactate concentration (Figure 7–2a) decreased more quickly between Day 0 and Day 9 in Control than those in the other two reactors. In the meantime (Figure 7– 2b), the acetate concentration increased to around 240 mg/L in the Control + HEI-17 and Experimental reactors in the first 13 days, while it stayed at a low level (< 13 mg/L) in Control. This indicated that the microbes grew more slowly in the Experimental and Control + HEI-17 reactors, likely due to the bacterial inhibitory effect of the imidazoline derivative (Liu et al. 2016b). The compound contains an amino group, which could inhibit growth of planktonic microbes. The acetate concentration increased immediately after all the three reactors were switched to the continuous feeding mode, reaching their respective peak on Day 43 before starting to decrease. The acetate concentration gradually stabilized at around 22 mg/L in Control and around 42 mg/L in Control + HEI-17 from Day 60 to Day 120. However, the FNA treatment at Day 47 changed the trend of both 79 lactate and acetate concentration in the Experimental reactor. The lactate concentration in the Experimental reactor increased from Day 48 to Day 52 (192 mg/L), indicating that the microbial activities were inhibited by the FNA treatment. Thereafter, the microbes started to recover, with the lactate concentration decreasing back to 0 again on Day 74, and acetate concentration reaching a relatively stable level (ca. 53 mg/L) on Day 79. Overall, although the sulfate concentration was higher in Control + HEI-17, it showed similar trends with that in Control (Figure 7–2c). It decreased generally in all the three reactors till Day 30, though experienced a slight increase from Day 9 to Day 13. Then it started to increase after the reactors were switched to continuous feeding mode on Day 30, and began to decrease after 8 days. It reached a steady state around 410 mg-S/L gradually after Day 65 in Control, which was about 70 mg-S/L higher than that in Control + HEI-17. The sulfate concentration in Experimental was almost the same with that in Control + HEI-17 before Day 47. After the FNA treatment on Day 47, sulfate concentration gradually increased before Day 60 and then decreased and stabilized at around 500 mg-S/L. In accordance to the sulfate concentration changes, sulfide concentration generally showed an increasing trend in both Control and Control + HEI-17, and it gradually stabilized at around 60 mg-S/L in Control and about 40 mg-S/L in Control + HEI-17. Sulfide concentration in Experimental dropped to 0 immediately after FNA was dosed, as also found in Figure 6–1d in Chapter 6, which could be due to the H2S emission along with the HCl addition in order to adjust pH to 6 for FNA treatment. Then it gradually increased to reach a relatively steady level at approximately around 32 mg-S/L after Day 90. In Figure 7–2e, the ATP level in Control was generally higher than that in Control + HEI-17 over the 120 days, supporting that HEI-17 had an inhibitory effect on microbes. It was previously found that at low concentrations around 10 ppm, imidazoline derivatives could inhibit bacterial growth (Ghasemi et al. 2015, Liu et al. 2016b). In this study, HEI-17 showed moderate inhibitory effect (approx. 14.7%) after 75 days’ immersion in Control + HEI-17 compared with Control. In Experimental, the ATP level decreased immediately to around 11% of the baseline level (before FNA treatment) after FNA treatment. With FNA addition, the microbial activity, as measured as ATP, was inhibited by up to 94.7% compared with Control. Then it gradually recovered after the medium feeding resumed to the previous level 28 days after the FNA treatment, and gradually reached to almost the same level as in Control + HEI-17 (approx. 81.0% of that in Control) on Day 120 (2.5 months after the FNA treatment). 80 7.3.2 General corrosion by weight-loss measurements Batch feeding FNA to Exp 1.5 Experimental Control + HEI-17 Control 1.0 0.5 Corrosion rate(mm/y) 0.0 0 30 60 90 120 Time(day) Figure 7–3 Corrosion rate calculated by the weight - loss measurements in Control, Control + HEI- 17, and Experimental reactors, respectively. On Day 47, FNA treatment (black arrow) was conducted in the Experimental reactor. In Figure 7–3, before Day 47, the corrosion rate of the coupons in Control + HEI-17 and Experimental were almost the same, which was around 70% lower than that in Control. This indicated the corrosion inhibition effect of the HEI-17, as reported previously in other studies (Villamizar et al. 2007, Wang et al. 2011). The corrosion inhibition efficiency of a selected imidazoline derivative on the MIC in a CO2-containing oilfield produced water was found to be 72% and 68% after 21 days’ and 43 days’ incubation, respectively (Liu et al. 2016b). Imidazoline derivatives at 50 ppm could inhibit the corrosion rate by 50% to 90% in 5% HCL acid after 48 hours’ immersion (Wang et al. 1999). After Day 60, the corrosion rate in Control and Control + HEI-17 stayed at a relatively stable level around 1.05 mm/y and 0.55 mm/y, respectively. The general corrosion rate in the Control reactor reached the highest level on Day 47, and remained at this level (approximately 1.05 mm/y) for the remaining period of the experiment. In comparison, the highest corrosion rate in the Control + HEI-17 reactor was reached on Day 60, and remained at this level (approximately 0.55 mm/y) for the remaining period of the experiment. The results indicate that HEI-17 not only reduced the steady state corrosion rate, but also slowed down the development of the corrosion. For the Experimental reactor, after the FNA treatment on Day 47, the general corrosion rate did not follow the trend of the Control + HEI-17 reactor, but rose much more slowly, reaching 93.4% of the general corrosion rate in the Control + HEI-17 reactor in 2.5 months. This should have been because of the biofilm removal and deactivation by FNA, which reduced the MIC of the coupon surfaces. The trends of the corrosion rate in the Control + HEI-17 and Experimental reactors reassembled the trends of ATP (Figure 7–2e). 81 7.3.3 3D optical profiling of corroded coupon surface a b c 120 120 120 Day 30 Day 47 Day 61 90 90 90 Experimental Experimental 60 60 Control + HEI-17 60 Experimental Control + HEI-17 Control Control + HEI-17 Control Control 30 30 30 Cumulative distribution (%) Cumulative distribution (%) 0 Cumulative distribution (%) 0 0 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 Depth (µm) Depth (µm) Depth (µm) d e f 120 120 120 Day 75 Day 90 Day 106 90 90 90 Experimental 60 60 Experimental 60 Experimental Control + HEI-17 Control + HEI-17 Control + HEI-17 Control Control Control 30 30 30 Cumulative distribution (%) Cumulative distribution (%) 0 0 Cumulative distribution (%) 0 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 Depth (µm) Depth (µm) Depth (µm) Batch feeding FNA to Exp i 600 g 100 120 h Day 120 m/y) Control Experimental µ 80 Control + HEI-17 90 Control + HEI-17 400 Experimental Control m) 60 µ Regression 60 Experimental 40 200 Control + HEI-17 Depth ( 30 Control 20 Pitting corrosion rate ( Cumulative distribution (%) 0 0 0 0 50 100 150 200 0 30 60 90 120 D47-D90 D90-D120 Depth (µm) Time(day) Time(day) Figure 7–4 Pitting corrosion estimated from 3D profiling. The cumulative distribution of the corrosion depth in Control, Control + HEI-17, and Experimental reactors on Day 30 (a), Day 47 (FNA treatment in Experimental) (b), Day 61 (c), Day 75 (d), Day 90 (e), Day 106 (f), and Day 120 (g). The dashed lines in (a) to (g) show the depth when the cumulative distribution reaches 90%. The 90%-ile pitting corrosion depths are summarized in (h). The averaged pitting corrosion rates after Day 47 (between Day 47 and Day 90, and between Day 90 and Day 120) calculated by the built-in function LINEST in Microsoft Excel from (h) (see regression lines shown in (h)) are shown in (i). Error bars represent the standard deviation of the pitting corrosion rates. The cumulative distribution of the corrosion depth in Experimental and Control reactors (Figure 7–4 a-g) was generated based on 3D images, with calculation details shown in Figure S7–1, Figure S7–2, and Figure S7–3 (referring to section 4.6 for detailed methods). Before the FNA treatment on Day 47, the cumulative distribution of the corrosion depth in Experimental was generally the same with that in Control + HEI-17 (Figure 7–4 a, b, h). The corrosion depth in Control was higher than those in the other 2 reactors, which indicated that HEI-17 slowed down the pitting corrosion. This could have been due to the lower biofilm viability in Control + HEI-17 and Experimental (Figure 7–2e). 82 On Day 61, 14 days after the FNA treatment in Experimental (Figure 7–4c), the corrosion depth was approx. 5 µm lower than that in Control + HEI-17, with comparable slope of cumulative distribution vs. depth. On Day 75 (Figure 7–4d), the difference of the pitting depth distribution between the three reactors became more apparent. The FNA addition to Experimental slowed down the pitting corrosion rate compared with that in Control + HEI-17, which could be attributed to the biocidal effect of FNA that deactivated the microbial metabolism. This inhibitory effect on pitting corrosion in Experimental continued for the following 31 days (Figure 7–4 e & f), which remained towards the end of the experiments at Day 120 (Figure 7–4g) with an obvious gap between Experimental and Control + HEI- 17. The slope of cumulative distribution vs. depth in Control was the lowest from Day 61 to Day 120 (Figure 7–4 c-g), which showed that the range of the pitting depth was the widest, indicating that the surfaces of the steel coupons in Control were the roughest and had the highest risk of deep pit formation. The 90%-ile corrosion-depth profile (Figure 7–4h) in the Experimental reactor following FNA treatment (i.e. between Day 47 and Day 120) displayed two distinct slopes (Figure 7–4h), as a rate of 0.12 ± 0.079 mm/y between Day 47 and Day 90, and 0.25 ± 0.006 mm/y between Day 90 and Day 120 (Figure 7–4i). The statistical significance of the results was analyzed with t-test and the difference was regarded as statistically significant when p < 0.05. The pitting corrosion rates of Day 47 – Day 90 in the Experimental reactor were significantly reduced compared with the Control + HEI-17 reactor (p < 0.01) and the Control reactor (p < 0.01). The pitting corrosion rate in the period of Day 47 – Day 90 was 64.6% lower than the rate in the Control + HEI-17 reactor, strongly suggesting that FNA treatment is effective in reducing pitting corrosion. However, the rate in the period of Day 90 – Day 120 was comparable to that in the Control + HEI-17 reactor (p > 0.05, Figure 7–4i), suggesting the effect of FNA treatment last for several weeks, after which FNA treatment should be re-applied. The observed inhibitory effects of FNA treatment on pitting corrosion are in line with the microbial measurement. As shown in Figure 7–2e, the biofilm ATP levels in the Experimental reactor was substantially lower than those in the Control + HEI-17 reactor between Day 47 and Day 90, but reached similar levels in the remaining period. 83 7.3.4 Electrochemical measurements Batch Batch FNA to Exp feeding feeding FNA to Exp -0.3 a b 40000 Experimental -0.4 Control + HEI-17 Experimental 30000 Control ) Control + HEI-17 -0.5 2 Control cm 20000 Ω⋅ -0.6 ( P R 10000 -0.7 OCP (V vs Ag/AgCl) -0.8 0 0 30 60 90 120 0 30 60 90 120 Time(day) Time(day) Figure 7–5 OCP (a) and polarization resistance (Rp) calculated by the LPR measurements (b) of the carbon steel working electrode in Experimental and Control. On Day 47, the FNA treatment (black arrow) was conducted in Experimental. Figure 7–5a shows that the OCP in Experimental and Control + HEI-17 was comparable (ca. -0.64 V), which was about 0.07 V higher than that in Control. Usually, OCP could be regarded as a thermodynamic parameter which shows the tendency of the metal to be involved in the electrochemical corrosion reactions (Silverman 2011). The lower OCP in Control indicated a higher tendency of the working electrode to corrode, which implied that HEI-17 could slow down the steel coupons’ corrosion. The FNA treatment on Day 47 in Experimental increased the OCP immediately, which was in accordance with the results shown in Chapter 6. Then the OCP started to decrease after the FNA treatment, which gradually matched with the other 2 reactors around Day 79, though experienced a few fluctuations. To be noticed, the OCP in all the 3 reactors eventually reached similar levels at around -0.55 V, which was also comparable to the OCP levels shown in Figure 6–4 before the 1st FNA treatment at Day 244. In Figure 7–5b, Rp started at the same level around 1500 to 2000 Ω⋅cm2 on Day 1 in all the 3 reactors and increased thereafter. It reached the highest level (approximately 37000 Ω⋅cm2) in Experimental and Control + HEI-17 on Day 6, while Rp in Control started to decrease after Day 3 and the peaked at around 11000 Ω⋅cm2. The inhibition efficiency of HEI-17 calculated by LPR measurements was 70.3%, which was comparable with the efficiency calculated by weight-loss measurements in 7.3.2. After Day 13, Rp in Experimental and Control + HEI-17 started to decrease to a level nearly 2000 Ω⋅cm2 higher than that in Control (around 4300 Ω⋅cm2) on Day 33. The FNA dosage on Day 47 in Experimental increased the Rp by almost 2000 Ω⋅cm2, which reduced the corrosion rate by 31.3% compared with the previous level. The inhibition efficiency was lower than that in 6.3.4 after both 84 FNA treatments (53.1% to 55.4%), but the increase of the absolute resistance was almost 10 times of that in 6.3.4. Although Rp in all the 3 reactors continued to reduce after Day 66, it was approximately 2000 Ω⋅cm2 higher in Experimental than that in Control + HEI-17 and around 3000 Ω⋅cm2 higher than that in Control at Day 118. 25000 b 1200 1000 a D1 D13 c D3 D19 20000 D6 D24 800 D28 900 D9 ) ) ) 2 2 Fitting line 2 15000 600 cm cm cm ⋅ ⋅ 600 ⋅ Ω Ω Ω D75 D96 4000 10000 D33 D49 400 D79 D102 -Z” ( -Z” ( 3000 D39 D57 -Z” ( D83 D109 2000 300 D47 D66 5000 D48 D70 200 1000 D90 D118 Fitting line 0 Fitting line 0 1000 2000 0 0 0 0 10000 20000 30000 40000 0 100 200 300 0 100 200 300 400 2 Z’ (Ω⋅cm ) Z’ (Ω⋅cm2) Z’ (Ω⋅cm2) d 40000 e 1400 -90 f 1200 -90 D1 D13 -90 D33 D49 D75 D96 D3 D19 1200 D79 D102 D6 D24 D39 D57 1000 30000 D9 D28 D47 D66 D83 D109 1000 -60 D48 D70 D90 D118 -60 Fitting line ) ) -60 ) 800 2 2 Fitting line 2 800 Fitting line cm cm cm ⋅ ⋅ 20000 ⋅ 600 Ω Ω -30 Ω -30 -30 600 |Z| ( |Z| ( |Z| ( 400 Phase (degree) Phase (degree) 400 10000 Phase (degree) 0 0 0 200 200 0 0 0 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 log (f, Hz) log (f, Hz) log (f, Hz) Figure 7–6 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Experimental. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 70 (D33 to D70), and Day 75 to Day 118 (D75 to D118), respectively. The embedded diagram shows the details of the Nyquist plots on Day 1, Day 24, and Day 28. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz). At Day 47, the FNA treatment (black arrow) was conducted. 25000 a D1 D13 b 1000 c 800 D3 D19 20000 D6 D24 800 D79 D9 D28 600 ) ) ) D83 2 2 2 15000 600 D90 cm cm cm ⋅ ⋅ ⋅ Ω 400 D96 Ω D33 D57 Ω 10000 400 D39 D66 D102 -Z” ( -Z” ( D44 D70 -Z” ( D109 200 5000 200 D49 D75 D118 Fitting line Fitting line Fitting line 0 0 0 0 10000 20000 30000 40000 0 100 200 300 0 100 200 300 400 2 2 2 Z’ (Ω⋅cm ) Z’ (Ω⋅cm ) Z’ (Ω⋅cm ) 85 1400 -90 1200 -90 40000 e d D1 D33 f D79 Fitting line -90 D39 D3 1200 1000 D83 D44 D6 D90 30000 -60 -60 1000 D49 D96 ) D9 ) 800 ) -60 2 2 D57 2 D102 D13 800 D66 cm cm ⋅ cm ⋅ D109 ⋅ D19 600 20000 D70 Ω Ω Ω -30 D118 -30 D24 -30 600 D75 |Z| ( |Z| ( Fitting line |Z| ( D28 Fitting line 400 Phase (degree) 400 Phase (degree) 10000 Phase (degree) 0 200 0 0 200 0 0 0 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 log (f, Hz) log (f, Hz) log (f, Hz) Figure 7–7 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Control + HEI-17. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 75 (D33 to D75), and Day 79 to Day 118 (D79 to D118), respectively. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz). a 5000 D1 D13 b 800 c 300 D3 D19 4000 D6 D24 D9 D28 600 ) ) 2 Fitting line ) 200 2 2 3000 1500 cm ⋅ cm cm ⋅ ⋅ Ω 1200 400 Ω Ω D79 D102 2000 900 D33 D57 -Z” ( D83 -Z” ( -Z” ( 100 D109 600 D39 D66 D90 200 1000 D44 D70 300 D96 D118 D49 D75 0 Fitting line 0 300 600 900 1200 Fitting line 0 0 0 0 2000 4000 6000 0 100 200 300 400 0 50 100 150 200 250 Z’ (Ω cm2) 2 2 ⋅ Z’ (Ω⋅cm ) Z’ (Ω⋅cm ) 8000 -90 1200 -90 d D1 D13 e f 600 -90 D3 D19 D33 D57 D79 D102 D24 1000 D6 D39 D66 D83 6000 D9 D28 D109 -60 D44 D70 -60 D90 -60 ) ) 800 ) 400 2 2 Fitting line D49 D75 2 D96 D118 cm cm Fitting line cm ⋅ ⋅ Fitting line 4000 600 ⋅ Ω Ω -30 -30 Ω -30 |Z| ( |Z| ( 400 |Z| ( 200 Phase (degree) Phase (degree) 2000 Phase (degree) 0 200 0 0 0 0 0 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 -2 -1 0 1 2 3 4 5 log (f, Hz) log (f, Hz) log (f, Hz) Figure 7–8 Nyquist (a, b and c) and Bode (d, e and f) plots of the carbon steel working electrode in Control. All lines are fitting lines. (a) and (d), (b) and (e), and (c) and (f) show the results from Day 1 to Day 28 (D1 to D28), Day 33 to Day 75 (D33 to D75), and Day 79 to Day 118 (D79 to D118), respectively. Red arrows in (d), (e) and (f) indicate that the lines are |Z| vs. log(f, Hz), while black arrows denote lines of phase angle vs. log(f, Hz). 86 Figure 7–6, Figure 7–7, and Figure 7–8 show the Nyquist and Bode plots of the working electrodes in Experimental, Control + HEI-17, and Control from Day 1 to Day 118, respectively. From Day 1 to Day 24, the Nyquist plots in Experimental (Figure 7–6a) were arc-shaped, of which the diameter reached the biggest at Day 3, indicating the highest impedance. After Day 3, the diameter of the Nyquist plots gradually decreased. This was in accordance with the LPR results in Figure 7–5b. In the zoom-in plot in Figure 7–6a, the Nyquist plot changed from arc-shaped at Day 24 to linear-shaped at Day 28. In Figure 7–6d, the bode plots showed only one time constant, of which the phase angle shifted towards higher values at low frequencies (10-2 to 10-1 Hz), which can be regarded as a trend of transformation of the system to diffusion limitation phenomenon (Batmanghelich et al. 2017). The Nyquist and Bode diagrams in Control + HEI-17 showed similar trends (Figure 7–7 a & d) compared with Experimental, indicating that HEI-17 had repeatable influence on carbon steel coupon corrosion behavior. In Control, the highest diameter of the Nyquist plots (Figure 7–8a) was about 4 times lower than that in Experimental and Control + HEI-17, which was comparable to the LPR results (Figure 7–5b). The shape of the Nyquist plots changed to linear at Day 6, indicating the diffusional effect on the working electrode surface (Liu and Cheng 2018), which was also indicated by the phase angle shift in the Bode plots (Figure 7–5d). This would be due to the low carbon source concentration in Control (Figure 7–2 a & b) (Venzlaff et al. 2013, Xu and Gu 2014). From Day 33 to Day 47 (right before FNA treatment), the Nyquist plots in Experimental were linear- shaped, which were changed to arc-shaped right after the FNA treatment at Day 48, with a larger diameter than that at Day 47 (Figure 7–6b). This shape change was also observed after the 1st FNA treatment in Figure 6–5a in Chapter 6. This was a sign of the surface reactions’ variation due to the deactivation of the biofilm on the coupon surface and the mediation of the electrolyte by the dosage of FNA. Then, it changed back to linear-shape at Day 49, of which the slope was the highest among the plots after Day 49, indicating the biggest impedance (Figure 7–6b). This was in accordance with the LPR results shown in Figure 7–5b. At Day 70, the Nyquist plot shifted to arc-shaped, which would be due to the re-development of biofilm on the electrode surface according to the cellular ATP level measurements (Figure 7–2e). The phase data in Bode plots show one time constant from Day 33 to Day 70 (Figure 7–6e). The |Z| reached the highest at Day 48, which indicated that the FNA treatment enlarged the impedance of the system. In Control + HEI-17, the Nyquist plots were linear-shaped from Day 33 to Day 70 (Figure 7–7b), which confirmed that the shape change in Experimental was due to the FNA application (Figure 7–6b). In Control, the Nyquist plots continued as linear-shaped from Day 33 to Day 57, which, however, switched to arc-shaped at Day 66 (Figure 7–8b). This change would be due to the breakdown of the unstable iron sulfides formed in the absence of oxygen, and 87 the diffusion limitation influence was eliminated by the surface change (Hamilton 2003, Walker 2001). The Nyquist plots in Experimental were relatively stable from Day 75 to Day 118, except Day 79 and Day 83, which were arc-shaped instead of linear-shaped lines (Figure 7–6c). However, the shape of the Nyquist plots in Control + HEI-17 changed from straight lines to arcs at Day 96, which indicated the breakdown or removal of biofilm or corrosion products layers on the electrode surface. This would be due to the instability of the precipitations or the detachment of the microbe clusters during the biofilm development. This led to the reduce of |Z| (Figure 7–7f), which was, however, still higher than that in Control (Figure 7–8f). In contrast, the Nyquist plots in Control changed from arc-shaped to linear-shaped at Day 90 (Figure 7–8c), which would be due to the formation of a compact biofilm on the electrode surface. Also, the carbon source level (acetate, Figure 7–2b) in Control was the lowest among all the three reactors, implying that the reactions on the working electrode in Control were influenced more by the diffusion limitation than the other 2 reactors. |Z| continued to decrease to around 70 Ω·cm2 at Day 118 (Figure 7–8f), which was almost 5 times lower than that in Control + HEI-17 (Figure 7–7f), and only 8.3% of that in Experimental (Figure 7–6f). To quantify the EIS results, two equivalent circuits (Figure 7–9) were used to fit the results shown in Figure 7–6, Figure 7–7, and Figure 7–8. The adequacy of the equivalent circuit models was maintained with fitting errors less than 10% for all fitted parameters. Q a f b R S W RS O Rf Figure 7–9 Equivalent circuits used to fit the EIS results in Figure 7–6, Figure 7–7, and Figure 7–8. Rs is the solution resistance, Rf and Qf are the resistance and capacitance of the biofilm, respectively. Wo denotes the finite length Warburg (FLW) element. The working electrode surface was impossibly smooth but with microscopic roughness, so the constant phase element (Q), a non-ideal capacitor, was used to fit the distributed capacitance instead of an ideal capacitor in circuit shown as Figure 7–9a. The impedance of Q (ZQ) is calculated by the following equation 7-1: ] Z\ = b (7-1) ^_(`a) 88 where Y0 and n are frequency independent parameters indicating the deviation of the specimen from an ideal capacity, and w is the angular frequency of the alternating voltage in rad s-1. The fitting results of the EIS plots in Experimental (Figure 7–6), Control + HEI-17 (Figure 7–7), and Control (Figure 7–8) are shown in Table 7–1, Table 7–2, and Table 7–3, respectively. WR represents the diffusion impedance, while WP is an exponential factor. WT denotes diffusion interpretation, which shows the time for the substrates to diffuse the system. It could be calculated by equation 7-2: N c = e (7-2) d f Where L (cm2) is the diffusion layer thickness and D (cm2 s-1) is the diffusion coefficient. Table 7–1 Fitting results for the EIS measurements of the Experimental (Figure 7–6). FNA treatment was applied on Day 47 (bold). Q W W Day R (Ω·cm2) f n (0 1 14.95 1.303 × 10-4 0.8597 1,938 3 11.00 2.377 × 10-4 0.8311 60,631 6 5.74 4.072 × 10-4 0.8394 39,325 9 6.93 3.285 × 10-4 0.8793 40,089 13 5.19 3.343 × 10-4 0.8577 45,427 19 7.10 1.107 × 10-4 0.8189 34,115 24 5.20 1.562 × 10-3 0.7728 10,238 28 15.92 18.74 0.1979 0.4486 33 13.78 15.38 0.1622 0.4491 39 10.19 12.58 0.1432 0.4043 47 10.86 13.82 0.1480 0.4329 48 8.35 1.444 × 10-2 0.9753 9,297 49 6.13 7.22 0.0685 0.4723 57 11.98 7.08 0.0534 0.4498 66 7.78 9.27 0.0388 0.4564 70 6.26 2.093 × 10-2 0.9077 5,092 75 11.10 16.13 0.1894 0.4489 79 7.77 2.658 × 10-2 0.8914 5,142 83 7.28 2.393 × 10-2 0.8909 4,239 90 13.12 15.53 0.1745 0.4416 96 12.44 14.40 0.1364 0.4381 102 10.19 12.90 0.1196 0.4290 109 17.41 14.90 0.1499 0.4366 118 10.36 13.34 0.1419 0.4314 89 Before Day 24, the arc-shaped Nyquist plots (Figure 7–6a) were fitted with the one-time constant circuit in Figure 7–9a. As the fitting results show in Table 7–1, the resistance of the biofilm on the working electrode surface (Rf) increased sharply in the starting 3 days, which then gradually decreased till Day 24. This trend was in accordance with the LPR results shown in Figure 7–5b. The deviation of the double-layer capacitance from an ideal capacity (nf) was in a relatively steady state of approximately 0.85 up to Day 19, after which it dropped to 0.7728 at Day 24, indicating that the electrode surface was becoming rough and porous (Sheng et al. 2007). As the Nyquist plots changed to linear-shaped at Day 28, in Figure 7–9b, a Warburg element (Wo) was used to replace the parallel resistance (Rf) and the constant phase element (Qf) to simulate the diffusional limitation effect indicated by the linear-shaped Nyquist plots. The presence of Wo was associated with the formation of a biofilm on the working electrode surface (Liu and Cheng 2018). As the Nyquist plot at Day 48 (right after the FNA treatment) changed to arc-shaped (Figure 7–6b), the EIS data of Day 48 was simulated with the circuit in Figure 7–9a. The shape of the Nyquist plot then changed back to linear- shaped at Day 49. Compared with the fitting result of Day 47 (right before FNA treatment), both the diffusion impedance (WR) and the diffusion time (WT) decreased after the FNA treatment (results from Day 49 to Day 66), which indicated that the biofilm on the working electrode surface was becoming thinner and less compact after the FNA treatment. According to the EIS data (Figure 7–6), the diffusion limitation effect of the biofilm did not show dominant influence on the working electrode surface from Day 70 to Day 83, so the equivalent circuit with parallel Rf and Qf was used (Figure 7–9b). From then onwards till Day 118, the EIS data in Experimental were fitted with the circuit with Wo, as the Nyquist plots were linear-shaped, implying that the electrode was diffusion limited by the biofilm. Table 7–2 Fitting results for the EIS measurements of the Control +HEI-17 (Figure 7–7). Q W W Day R (Ω·cm2) f n (0 1 11.24 1.941 × 10-4 0.8540 1,166 3 12.63 2.978 × 10-4 0.8912 31,518 6 10.23 8.271 × 10-5 0.9472 37,360 9 8.84 3.173 × 10-4 0.8642 45,925 13 5.10 2.827 × 10-4 0.9006 44,251 19 8.31 1.581 × 10-4 0.8645 16,804 24 9.39 2.115 × 10-4 0.7543 16,062 28 10.59 15.87 0.1864 0.4525 33 10.90 14.27 0.1328 0.4366 39 13.78 15.38 0.1622 0.4491 44 10.86 13.82 0.1480 0.4329 49 13.57 14.14 0.4711 0.4438 57 13.17 17.06 0.1837 0.4482 90 66 12.96 15.71 0.1724 0.4379 70 10.86 13.82 0.1480 0.4329 75 10.07 14.37 0.1511 0.4329 79 11.32 14.00 0.1391 0.4270 83 9.42 13.86 0.1390 0.4394 90 7.15 1.448 × 10-2 0.8761 3,619 96 5.75 2.145 × 10-2 0.8114 1,511 102 6.46 2.535 × 10-2 0.7205 1,180 109 13.71 2.462 × 10-2 0.7411 1,606 118 7.49 2.463 × 10-2 0.7531 1,151 Table 7–2 shows that before the FNA treatment in Experimental at Day 47, the surface property and impedance behaviour of the working electrodes in Control + HEI-17 and Experimental were fairly comparable. This indicated that HEI-17 showed repeatable influence on the electrochemical parameters of the working electrode. The fitting results showed that the diffusion limitation of the biofilm continued to dominate the reactions on the working electrode surface up to Day 83 in Control + HEI-17. From Day 90, the biofilm’s diffusion limitation effect failed to control the reactions on the electrode surface (Figure 7–7c), so the EIS data were fitted with the circuit in Figure 7–9a. Table 7–3 Fitting results for the EIS measurements of the Control (Figure 7–8). Q W W Day R (Ω·cm2) f n (0 1 10.91 2.580× 10-4 0.7377 1,189 3 17.92 8.908× 10-4 0.7212 19,239 6 6.35 19.26 0.0634 0.3952 9 10.37 14.08 0.1475 0.4465 13 8.35 12.84 0.1840 0.4169 19 11.10 16.13 0.1894 0.4489 24 10.91 15.37 0.1683 0.4504 28 8.74 19.80 0.4001 0.5071 33 12.71 26.29 0.4035 0.4441 39 11.92 24.45 0.4104 0.4438 44 13.55 24.83 0.4334 0.4467 49 15.71 30.98 0.5597 0.4492 57 11.98 27.05 0.4881 0.4461 66 18.74 1.725 × 10-2 0.8262 7,897 70 18.41 1.363 × 10-2 0.7897 8,062 75 13.17 1.290 × 10-2 0.7581 6,349 79 12.79 2.213 × 10-2 0.7224 2,593 83 11.59 4.272 × 10-2 0.7532 1,263 90 10.67 5.903 0.2879 0.4141 96 10.77 8.623 0.7356 0.3946 102 10.51 9.841 1.0810 0.3903 91 109 9.917 8.944 1.0380 0.3812 118 8.747 15.29 2.4500 0.3709 The fitting results in Control (Table 7–3) showed that the diffusion limitation effect of the biofilm started to dominate the system at an early stage (Day 6) of the immersion. This added to the evidence along with the ATP measurements (Figure 7–2e) that HEI-17 had inhibitory effect on the microbial activity and could eliminate the attachment of the microbes to the metal surface as a surfactant (Ghasemi et al. 2015, Liu et al. 2016b). The diffusion limitation effect disappeared at Day 66, which was similar to that in Control + HEI-17 at Day 90. However, the biofilm re-developed to form a compact and thick structure, hindering the diffusion of the substrates at Day 90 in Control. Compared with Experimental, WR was lower in Control, which indicated that the resistance of the diffusion film was higher in Experimental, implying a lower corrosion rate. However, WT was much higher in Control, which showed that it consumed more time for the substrates to diffuse to the working electrode surface, indicating a much thicker biofilm than in Experimental. Combined with the pitting results in Figure 7–4, a thicker biofilm in Control caused much more severe pitting corrosion than in Experimental. Overall, the EIS results were consistent with LPR measurements. The working electrode surface properties and the dominant reactions revealed by EIS analysis supported the general (Figure 7–3) and pitting (Figure 7–4) corrosion results. The electrical components in the equivalent circuits interpreted the mechanisms of the MIC process on the steel coupon surface. 7.3.5 Combined imidazoline derivative and FNA treatment as a potential technology for corrosion control in water injection systems Compared with the Control reactor, continuous HEI-17 treatment showed strong general corrosion inhibition efficiency of approx. 70% according to the weight-loss measurements and LPR tests. Moderate pitting corrosion inhibition effect (up to 27%) was achieved with HEI-17 based on the measured cumulative distribution of the depth, which disappeared along with the biofilm development on the carbon steel coupons. These results are consistent with previous studies (Villamizar et al. 2007, Wang et al. 2011). In Chapter 6, we demonstrated that intermittent dosing of FNA could significantly reduce the metal corrosion rate in water injection systems. With 2 intermittent FNA treatments at an 82-day interval over 304 days, FNA dosing decreased the averaged pitting corrosion rate by a maximum of 58.5% as compared with the control without FNA. General corrosion was also reduced by up to 31%. The intermittent dosing of FNA appeared to be complementary to the commonly used corrosion inhibitors. 92 In this study, the effect of pulse dosing of FNA to further reduce corrosion in a simulated water injection system, continuously-dosed with HEI-17 as a corrosion inhibitor, was investigated. The combination of HEI-17 and FNA showed complementary effects with higher inhibitory efficiency in both pitting and general corrosion. FNA showed high compatibility by moderately enhancing HEI- 17’s strong general corrosion inhibition and complementing its deficient pitting corrosion inhibition. Compared with sole HEI-17 treatment, the dosage of FNA enhanced the general corrosion inhibition efficiency moderately by up to 49% and improved the pitting corrosion inhibition efficiency by up to 69%. However, the pitting corrosion enhancement by FNA gradually diminished and disappeared around 1.5 month after the dosage, indicating that in practice, to achieve successful pitting corrosion inhibition, the frequency of FNA dosage should be once per month. In practice, corrosion inhibitors are usually dosed continuously in the range of 10 to 1000 ppm in oilfields (Kermani 2017). At a price of 2 – 3 USD/kg, the cost for the use imidazoline derivatives would be in the range 0.02 to 3 USD/m3 of inject water treated. The range of the in-situ pH of oil reservoirs is from 3 to 7 (Magot et al. 2000, Pannekens et al. 2019). When the pH is lower than 6, the estimated cost for dosing sodium nitrite to reach the FNA level of 0.49 mg HNO2-N/L for 24 hours (the level applied in this study), applied monthly, would be up to 0.015 USD/m3 (averaged to the total flow over the month), which is far lower than the costs for imidazoline, unless the latter is used at very low concentrations (e.g. 10 ppm). For the upper end of the pH range, e.g. 7, the cost of hydrochloric acid could be up to 0.050 USD/m3 (yearly averaged). Considering the significant enhancement to corrosion inhibition achieved, the use of intermittent FNA dosing to complement continuous imidazoline dosing is likely an economically attractive approach. FNA is biodegradable after being diluted to low concentrations in the same, which means it would not cause secondary pollution, unlike most of the commonly used organic biocides. 7.4 Conclusions The combined effect of imidazoline derivative (HEI-17) and FNA on carbon steel corrosion in a simulated water injection system was investigated in laboratory tests. The key findings are: • Intermittent dosing of FNA produces corrosion inhibition effects complementary to those by continuous dosing of corrosion inhibitors such as imidazoline derivatives. When applied alone, imidazoline derivative HEI-17 has a strong inhibition effect on general corrosion and a moderate inhibition effect on pitting corrosion. The intermittent dosing of FNA substantially enhances the inhibition on pitting corrosion, and also moderately enhances the inhibition on general corrosion. 93 • FNA dosing appears to inactivate/remove biofilms on steel surfaces, and hence its application frequency is determined by the speed of biofilm recovery. This study suggests that the FNA dosing frequency should be once a month. 94 7.5 Supplemental information 40 120000 30 D30 m) 80000 µ 20 Count Height ( 40000 10 D30 0 0 0 500 1000 1500 0 10 20 30 40 50 Distance (µm) Height (µm) 50 120000 40 D47 m) 80000 µ 30 20 Count Height ( 40000 10 D47 0 0 0 500 1000 1500 0 20 40 60 80 Distance (µm) Height (µm) 95 80 8000 D61 60 6000 m) µ D61 40 4000 Count Height ( 20 2000 0 0 0 100 200 300 400 0 20 40 60 80 100 Distance (µm) Height (µm) 100 30000 D75 80 20000 m) D75 µ 60 40 Count 10000 Height ( 20 0 0 0 200 400 600 800 0 50 100 150 Distance (µm) Height (µm) 150 20000 D90 15000 100 m) D90 µ 10000 Count 50 Height ( 5000 0 0 0 200 400 600 800 0 50 100 150 Distance (µm) Height (µm) 96 150 20000 D106 15000 100 m) D106 µ 10000 Count 50 Height ( 5000 0 0 0 200 400 600 800 0 50 100 150 200 Distance (µm) Height (µm) 200 25000 D120 20000 150 m) D120 µ 15000 100 Count 10000 Height ( 50 5000 0 0 0 200 400 600 800 0 50 100 150 200 Distance (µm) Height (µm) Figure S7–1 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control reactor. 97 25 80000 D30 20 D30 60000 m) µ 15 40000 10 Count Height ( 20000 5 0 0 0 500 1000 1500 0 10 20 30 Distance (µm) Height (µm) 30 80000 D47 60000 D47 20 m) µ 40000 Count 10 Height ( 20000 0 0 0 500 1000 1500 0 10 20 30 40 Distance (µm) Height (µm) 50 100000 D61 40 80000 m) µ 30 60000 D61 20 Count 40000 Height ( 10 20000 0 0 0 500 1000 1500 0 20 40 60 Distance (µm) Height (µm) 98 80 80000 D75 60 60000 m) D75 µ 40 40000 Count Height ( 20 20000 0 0 0 500 1000 1500 0 20 40 60 80 Distance (µm) Height (µm) 100 25000 D90 80 20000 m) µ 60 15000 D90 40 Count 10000 Height ( 20 5000 0 0 0 200 400 600 800 0 20 40 60 80 100 Distance (µm) Height (µm) 60 25000 D106 20000 40 D106 m) µ 15000 Count 10000 20 Height ( 5000 0 0 0 200 400 600 800 0 50 100 150 Distance (µm) Height (µm) 99 150 25000 D120 20000 100 m) D120 µ 15000 Count 10000 50 Height ( 5000 0 0 0 200 400 600 800 0 50 100 150 Distance (µm) Height (µm) Figure S7–2 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Control + HEI-17 reactor. 100 25 80000 20 D30 60000 m) µ 15 40000 10 Count Height ( 20000 5 D30 0 0 0 500 1000 1500 0 10 20 30 Distance (µm) Height (µm) 30 80000 D47 60000 D47 20 m) µ 40000 Count 10 Height ( 20000 0 0 0 500 1000 1500 0 10 20 30 Distance (µm) Height (µm) 40 80000 D61 30 60000 D61 m) µ 20 40000 Count Height ( 10 20000 0 0 0 500 1000 1500 0 10 20 30 40 50 Distance (µm) Height (µm) 101 40 40000 D75 30 30000 m) µ D75 20 20000 Count Height ( 10 10000 0 0 0 200 400 600 800 0 10 20 30 40 50 Distance (µm) Height (µm) 60 20000 D90 15000 40 m) D90 µ 10000 Count 20 Height ( 5000 0 0 0 200 400 600 800 0 20 40 60 Distance (µm) Height (µm) 80 40000 D106 60 30000 m) µ Count 40 20000 Count Height ( 20 10000 0 0 0 200 400 600 800 0 20 40 60 80 100 Distance (µm) Height (µm) 102 150 25000 D120 20000 100 m) µ 15000 D120 Count 10000 50 Height ( 5000 0 0 0 200 400 600 800 0 50 100 150 Distance (µm) Height (µm) Figure S7–3 The 3D images, pit depth profile of randomly selected lines, and the depth distribution of the coupon surfaces in the Experimental reactor. 103 Figure S7–4 The theoretical transition of carbon steel surface by FNA treatment in the Experimental reactor. (a) demonstrates the MIC mechanism by SRB. (b) denotes the surface change after FNA treatment. FNA removes the biofilm on the steel surface, and HEI-17 forms a protective layer on the exposed surface (blue line). 104 Chapter 8 Conclusions and future work 8.1 Main conclusions of the thesis To date, petroleum is still the most important fuel to people’s daily lives, so the sustainable oil and gas production is crucial in the oilfields. Corrosion is an inevitable problem that threats the pipeline integrity, and MIC contributes significantly to pitting corrosion, causing pipeline system failures and leakages. Researchers have been working on uncovering the mystery of MIC for decades, and the study in Chapter 5 went a further step based on the previous research outcomes. This study started with real produced water sampled from oilfield as inoculum and fresh steel coupons in simulated water injection systems, to investigate the MIC development in the early stage of water injection in secondary oil production. These findings not only illustrate the MIC development in more detail and more thoroughly, but also provide important theoretical knowledge for the MIC monitoring and controlling in practical oil production infrastructure. The main conclusions are: • The development of MIC of metal in a water injection system comprises 3 phases: Initialization (I), including the formation of the corrosion products layer and the initial attachment of the sessile microbes; Transition (II), when biofilm develops on the metal surface; and Stabilization (III) with mature and stable biofilm on coupon surface. • Along with the formation of the biofilm on the metal surface, the MIC process gradually shifts from charge transfer resistance to a diffusional-limitation when a compact biofilm formed. • The diffusional-limitation effect slows down the general corrosion, but in the meantime enhances the pitting corrosion in Phase (III), which supports the mechanism of direct electron uptake from the metal surface by SRB. In practical oil production, biocides are usually added to the injection water to kill or remove the biofilm formed on the pipeline surfaces. Following the MIC development investigation in Chapter 5, the effect of FNA, a strong biocide, was evaluated with 2 intermittent dosages to one of the simulated water injection systems in Chapter 6. This laboratory investigation has opened a new vision for MIC control and mitigation with FNA application, which is promising to be an alternative to the current MIC control procedures in oilfields with organic biocides. The key findings are: • FNA has shown strong potential to be an effective and efficient method of controlling pitting corrosion in oil recovery infrastructures. The results from measuring cumulative 105 distribution of the corrosion depth indicated that FNA prevents the formation of deep pits on the steel surface. • FNA exerted a moderate effect on slowing the general corrosion rate. • Intermittent FNA dosage is a promising strategy for MIC control in anaerobic environments. This technology could be cost-effective and environment-friendly, as nitrite is cheap, the application is relatively easy compared to coating, and the residual nitrite is biodegradable in the system. In oilfields, the pipelines are usually maintained with mixed chemicals, including corrosion inhibitors and biocides. Based on the results in Chapter 6, to further understand FNA’s potential as a MIC treating chemical in oilfields, a widely used corrosion inhibitor, the imidazoline derivative (HEI-17 as a represent in this study), was applied continuously along with intermittent FNA dosage in Chapter 7. FNA shows high compatibility by moderately enhancing HEI-17’s strong general corrosion inhibition and remedying the deficient pitting corrosion inhibition. This study further reveals FNA’s potential to be applied in the practical oil recovery infrastructure. The key findings are: • Continuous HEI-17 treatment has shown strong general corrosion inhibition efficiency according to the weight-loss measurements and LPR tests. The dosage of FNA enhanced the general corrosion inhibition efficiency moderately. • HEI-17 treatment alone showed moderate pitting corrosion inhibition effect from measuring the cumulative distribution of the depth, and the FNA treatment inhibited the formation of deep pits effectively. • The combined application of HEI-17 and FNA has shown synergetic effect and high efficiency in mitigating MIC in the simulated water injection system. This treating strategy has strong potential to be applied in the practical oil recovery infrastructure. Overall, this thesis focuses on MIC, the most common and detrimental problem in oilfield operations, and supplies future MIC understanding and combating with comprehensive outcomes from thorough laboratory experiments. To control and mitigate MIC, understanding the current MIC developing phase and corrosion reaction is helpful for the proper treating strategy selection. The intermittent FNA treating strategy could be an economical and green substitute for the current traditional treating procedures. Future tests in real oil production infrastructure will be essential in evaluating FNA’s feasibility to be applied in oilfields. 106 8.2 Recommendations for future research In addition to the research objectives investigated so far, many research challenges were identified that require further research during the whole period of my PhD. Some of them are summarized as follows: (1) Our study revealed that the MIC process was influenced by the biofilm development on the metal surface. However, neither the exact mechanisms nor the structure of biofilms is fully understood. The microbial community compositions of injection and produced water both in onshore and offshore oil production processes have been determined by previous studies (Gittel et al. 2009, Okoro et al. 2014, Ren et al. 2011). Also, some research on biofilm community in situ have been reported (Neria-González et al. 2006, Schwermer et al. 2008). However, the stratified microbial structure of the biofilm has never been verified. Microelectrodes can be used to determine the spatial distribution of the in situ concentrations of the key ions, such as sulfide, and the pH level. Cryosectioning, commonly known as frozen sections, was first developed for medical applications for the rapid diagnosis of pathological tissue lesions since the 1980s, and it has been applied in biofilm studies since the 1990s (Yu et al. 1994). The stratified distribution of the microbes involved in MIC, particularly SRB, and their abundance at different depths could be analysed by FISH and 16s rRNA amplicon pyrosequencing after the cryosectioning. Based on this, the roles of different microbes in the corrosion process can be illustrated, which will help to understand the MIC mechanisms. (2) Biofilm formation is a complicated dynamical process of which the development depends mostly on the environmental conditions and types and properties of the bacteria inside the biofilm (Garrett et al. 2008). Mathematical modeling of biofilms is crucial to understand this complex microbial activity more deeply, not only by verifying experimental findings, but also by making qualitative and quantitative predictions which might provide guidelines for experimental designs. There is little report on the mathematical modeling of biofilm on carbon steel surface, let alone the anaerobic corrosive biofilm in seawater injection process in the oilfield. A biofilm model would help to build a mathematical model for potential corrosive biofilm prediction in practical operations. This biofilm model can be employed on AQUASIM V2.1d software and should consist of 4 types of microbial processes: hydrolysis, fermentation, sulfate reductions, and methanogenesis. It can be modified to evaluate the experimental results in the above section (1). Based on these, 107 the microbial distribution and profiles within the biofilms will be predicted. This study will help to build a mathematical model for potential corrosive biofilm prediction in practical operations. (3) In Chapter 5, the diffusional-limitation effect slows down the general corrosion, but in the meantime enhances the pitting corrosion, which supports the mechanism of direct electron uptake from the metal surface by SRB under carbon source starvation. It indirectly indicates that the best nutrient level for SRB biofilm growth is likely not the one causing worst pitting corrosion, which is against previous perspective. The relationship of carbon source level and corrosion rate needs further investigation, which would help to understand the MIC mechanism. For mechanism study, pure Desulfavibrio vulgaris strains can be used to grow mature biofilm on the coupon surfaces before transferred to vials with different lactate concentrations. LIVE/DEAD staining can be used to visualize the gradient change of the biofilm viability. Microelectrodes can be used to determine the spatial distribution of the in situ concentrations of the key ions, such as sulfide, and the pH level. The pitting corrosion could be evaluated by the 3D optical profiling. RT-qPCR could be applied to evaluate SRB’s catabolism by analysing the expression of dsrA and apsA genes, which encode the dissimilatory sulfate reductase and APS reductase, respectively. From the industry point of view, research can be conducted with the enriched oilfield consortia to investigate carbon source levels’ impact on MIC in the simulated water injection system. The biofilm will be allowed to develop on the coupons in full organic carbons, and then the reactors will be changed to different lower lactate levels for a relatively long-term cultivation. Similar monitoring and analysing procedures in Chapter 5 could be followed. (4) The experiments were carried out in simulated water injection systems under laboratory conditions. However, the real oilfield condition is more complex than laboratory environment. Therefore, further tests are essential to fully understand the MIC process and evaluate the FNA- based strategies under real oil recovery conditions. The experiments were done at 25 °C under laboratory conditions. However, the temperature in the oil reservoir varies from lower than 10 °C to higher than 80 °C due to regional and seasonal differences. Microbes’ activities vary under different temperature, which would influence the metal corrosion differently. 25 °C is a relatively moderate condition, and lower and higher temperatures should be taken into consideration in future research. Similar simulated water injection systems with temperature controlled by thermostats could be set up to investigate the impact of different temperature levels. 108 Besides biocides and corrosion inhibitors added in the injection water to control and mitigate MIC, usually some other chemicals may be added along, such as surfactants to help extract oil and scale inhibitors. To fully evaluate FNA’s compatibility with other chemicals, normal surfactants and scale inhibitors could be selected to be applied along with FNA. There are some other factors to be considered before apply FNA treatment in oilfields, including oil reservoir pressure and flow conditions in the pipelines, etc. These conditions vary from case to case, so a comprehensive understanding of the oilfield’s conditions and the chemicals are essential for the proper treating procedures’ selection. (5) This study showed that FNA was effective on mitigating MIC in laboratory tests. This was regarded to be mainly due to FNA’s biocidal effect on the corrosive biofilm. However, as metal acts as an electrode submerged in the electrolyte, the corrosion process is also influenced by the medium conditions, including pH and ion concentrations. The dosage of FNA lowered the pH and raised chloride and sodium concentrations in the medium. Therefore, a more detailed evaluation of FNA’s MIC controlling mechanism is necessary. To investigate the influence of pH, lower pH can be adjusted with HCl (in high concentration to eliminate Cl- addition). And according to the calculating equation in 6.2.1, sodium nitrite concentrations could be determined to reach 0.49 mg-N/L FNA at different pH levels. The addition of Na+ is negligible due to the high Na+ concentration in the seawater synthetic medium. The important ions’ influence could be evaluated by adding selected amount of the testing ion and controlling the other ions identical in different simulated water injection systems. 109 List of References Afolabi, A., Muhirwa, A., Abdulkareem, A. and Muzenda, E., 2014. Weight loss and microstructural studies of stressed mild steel in apple juice. Ahmad, Z., 2006. Principles of corrosion engineering and corrosion control, Elsevier. AlAbbas, F.M., 2013. 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