Managing Microbial Corrosion inCanadian Offshore & Onshore

Oil Production Operations:Project Overview

John Wolodko, Ph.D., P.EngAI Strategic Chair in Bio & Industrial Materials

University of AlbertaEdmonton, AB, Canada

Presentation Outline

• Introduction• Areas of Focus• Participating Organizations• Project Management and Administration• Overview of Work Scope:

• Activity 1 – Knowledge Generation• Activity 2 – Assays and Devices• Activity 3 – Predictive Models• Activity 4 – Knowledge Translation

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Introduction• The overall goals of this large-scale applied research project

are:• to utilize novel genomics testing & analysis methods to better

understand and predict the formation and evolution of Microbiologically Influenced Corrosion (MIC), and

• to help develop/validate MIC tools (databases, models & guidelines) and MIC mitigation strategies for the Canadian energy sector.

• $7.85M Project funded by Genome Canada, Provinces of Alberta & Newfoundland, Mitacs and industry (in-kind).

• Collaboration between various research providers (academia and government research labs), government andindustry.

• Co-led by Lisa Gieg (University of Calgary), Faisal Khan(Memorial University of Newfoundland), and John Wolodko(University of Alberta).

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Introduction• 4 year project – initiated Oct 2016• Multi-disciplinary project team:

• Microbiology• Chemistry• Materials Science & Engineering• Genetics/Bioinformatics• Engineering Management/Business

• Pan-Canadian project with International collaborations

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3 Main Areas of Focus• Offshore (top-side platforms)• Onshore upstream operations (production & water

systems)• Onshore liquid transmission pipelines (under deposit

corrosion)

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Industry Involvement:• Upstream Oil & Gas

Operators• Transmission Pipeline

Operators• Service Providers• Suppliers - Chemical and

Analytical Supplies• Engineering Consultants• Industry Associations

Onshore

Offshore

Participating Organizations

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Offshore

Funding Agencies Industry Partners

Research Partners

Project Management & Administration

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Working Group - Activity 2Devices/Assays

Gieg, Bottaro, Ping, Haile, Wolodko

Working Group - Activity 3Models

Khan, Haile, Wolodko, Skovhus, Eckert

Working Group - Activity 1 Knowledge

Hawboldt, Gieg, Bottaro, Beiko, Strous, Haile, Wolodko, Hubert,

Turner

Genome Canada

Genome Alberta &Genome Atlantic

Mercer, Stone

Research Oversight

Committee (ROC)

Project co-leads:Gieg, Wolodko, Khan

Project CoordinatorFragoso

INDUSTRY PARTNERS

Executive Team

Working Group - Activity 4Translation (GE3LS)

Wolodko, Lefsrud, Skovhus, Eckert, Jack

Project Management & Administration• Meetings/Events:

• Annual Industry Workshop/Symposium • Semi-annual MIC Team Meetings• Semi-annual ROC Meetings• Monthly Executive Committee Meetings• Participation in various industry conferences (e.g. NACE,

ISMOS, SPE, others).

• Semi-annual progress reporting (researchers and industry partners)

• Project Coordinator: Nuno Fragoso (U of C)• Project Website: www.geno-mic.ca

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Activity 1: Knowledge Activity 2: Devices/Assays

Activity 3: Models Activity 4: Translation

Main Project Activites

Activity 1: Knowledge

Identify the microbial species/pathways, chemical species, and mechanisms that lead to

MIC in offshore and onshore operations

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Activity 1: Knowledge

1.1 – Field Sampling/Methods SOPs1.2 – Database/ontology1.3 – MIC offshore (abiotic & biological)1.4 – MIC onshore1.5 – Biofilms/biocide resistance

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1.1 – Field Sampling/Methods SOPs

• Activity Leads: L. Gieg (U of C), K. Hawboldt (MUN), C. Bottaro (MUN), T. Haile (Innotech AB)

• Objectives:• To obtain field samples from Canadian oil & gas operators to

evaluate assets which are susceptible to MIC.• To develop Standard Operating Procedures (SOPs) for sampling of

liquids and solids from both offshore and onshore operations.• This provides a standardized/consistent approach across the

project to ensure quality and integrity of samples for characterization.

• Approach:• SOPs are based on assessment of best practices available from

project team members and industry.• SOPs are provided to end users in conjunction with data checklist

and sampling kits.

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1.2 – Database/Ontology

• Activity Lead: R. Beiko (Dalhousie)• Objectives:

• To develop a comprehensive project database to allow project members to store and retrieve large data sets generated from the various research activities.

• This includes various datasets from analyses and supporting meta-data (e.g. genomic data, chemical data, corrosion data, physical parameters of the assets, operating conditions, etc.).

• Will bridge the various activities in the project, and be accessible to the project team, end-users, and (ultimately) to the wider research community.

• Approach:• A MIC ontology is being created which formally names and defines data

types, their properties and interrelationships.• Development of formal ontology and database is being done in

consultation with project stakeholders.• Database to be accessible with user-friendly, web based interface.

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Ontology development:

• Structured approach to describing data• Defined terms, controlled vocabularies• Draw on existing resources; develop new terms where

necessary

1.2 Database/Ontology

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1.3 – MIC offshore (abiotic & biological)

• Activity Leads: K. Hawboldt (MUN), L. Gieg (U of C), C. Bottaro (MUN)

• Objectives:• To investigate mechanisms which lead to MIC in offshore (topside)

facilities both from a chemistry (abiotic) and microbiological perspective.

• Primary focus is on S and N transformations.• Approach:

• Identify compounds of importance through literature and thermodynamic analysis.

• Conduct experiments to determine solution chemistry/behavior as a function of temperature, pH and composition.

• Evaluate genomics testing protocols (primer sets, different polymerases, and data analysis pipelines).

• Conduct microbiological analysis.

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1.3 Understanding N & S transformations that lead to MIC in offshore facilities

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Seawaterinjection

Crude oil/gasproduction + H2S/other

S species

Sulfate + treatment agents

Potential reservoir souringH2S

H2S + other N, S& microbial species

Topside Corrosion

- Nitrate treatment may lead to the production of corrosive S species:

From PDF S. Lahme & C. Hubert/I. Head, Sulfurimonas CVONewcastle University

1.3 Abiotic Processes – Compounds in MIC

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1.4 – MIC onshore

• Activity Leads: T. Haile (Innotech AB), J. Wolodko (U of A), L. Gieg (U of C)

• Objectives:• To conduct biological assessments and corrosion tests on solids

(sludges) samples from upstream & transmission pipelines.• To compare and catalog MIC corrosion rates for a variety of

pipeline operating conditions and locations.

• Approach:• Conduct corrosion tests under both static (bench top) and

dynamic (flow loop) conditions with actual and simulated field conditions (water chemistry & operating conditions).

• Perform microbiological and chemical characterization, monitor corrosion rates using weight loss and electrochemical methods, and assess corrosion pit morphology after testing.

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Location1: Leak point

Location 2: Adjacent to Leak point

Location 3: Non-corroded section

Location 4: sediments collected away from corroded area

1.4 Biofilms and UDC associated with MIC

4 samples fromdead-leg of a heavy oil transporting pipeline

Pinhole leak, due toMIC?

Onshore Pipeline Samples:

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geno-MIC

geno

1. Preservative vs non-preservative

Non-preserved samples

Preserved samples (isopropanol)

Pseudo-preserved (cotton gauze))

2. Primer sets

A: 926F, 1392R (V6-V8)

B: 515F, 926R(V4-V5)

C: 341F, 785R (V3-V4)

MIC

1. Water chemistry

Organic acids

Anions

Salt (NaCl eq.)

pH

Total Iron

2. Corrosion assays

Optical measurements

Pitting profile

Scanning electron microscopy (SEM)

1.4 Biofilms and UDC associated with MIC

Also, XRD analysis performed

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Location 1, leak site - Genus level

1.4 Biofilms and UDC associated with MIC

A: 926F, 1392RB: 515F, 926RC: 341F, 785R

- All primer sets yielded similar taxa; 515F, 926R seemed to have best coverage - though analysis still being done

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1.4 Biofilms and UDC associated with MICBiofilm on the coupon’s

surface After cleaning

Non-pitted area

pitted area

Location 1Leak site

Couponobservations after 1 month incubation:

Location 3Non-corroded area

Biofilms on couponsbeing sequenced to help identify taxa involved in pitting

1.5 – Biofilms/biocide resistance

• Activity Leads: R. Turner (U of C), L. Gieg (U of C)• Objectives:

• To study biocide and corrosion inhibitor tolerance/resistance in microbial biofilms, and to better understand the mechanisms for biocide resistance.

• Approach:• Conduct exposure experiments under controlled conditions

for pure cultures and model microbial communities.• Bioinformatics profiling of various genes that may contribute

to resistance. • Findings from this work may provide guidance on future

biocide selection to prevent potential conditions for resistance, and may assist in the development and testing of new corrosion control chemistries.

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Activity 2: Devices & Assays

Develop -omics and chemical-based monitoring tools to detect & measure MIC and associated

chemical end-products

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Activity 2: Devices & Assays

2.1 – MIC Diagnostic genes2.2 – In-line monitoring tools2.3 – Chemical & biosensors

2.3.1 Molecularly Imprinted Polymers (MIPs)2.3.2 Sulfide sensor/biosensor

2.1 – MIC Diagnostic genes

• Activity Leads: L. Gieg (U of C)• Objectives:

• To evaluate lab-on-a-chip devices for portable genomics analysis of MIC samples.

• Approach:• Evaluate commercial, rapid (point of care) gene-based devices

currently used for pathogen detection (microfluidic devices). • Test whether these isothermal PCR protocols are capable of rapid

detection of MIC organisms or genes.• Determine methods to amplify DNA directly from biological

samples.• Determine appropriate primer design to capture diversity of

‘diagnostic’ genes.

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2.2 – In-line monitoring tools

• Activity Leads: T. Haile (Innotech AB) and J. Wolodko (U of A)

• Objectives:• To develop and assess a novel electrochemical based probe for

detection and continual monitoring of biofilms in a pipeline environment.

• Approach:• Prototypes constructed based on parallel plate electrode

designs and compact Inter-Digitated Electrodes (IDE). • Probe performance evaluated under both abiotic and biotic

test conditions (pure cultures and model microbial communities) for various electrode materials and test fluids.

• Biofilm growth kinetics correlated to measured probe capacitance.

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Activity 2.2 In-line corrosion and biofilmmonitoring system

• Development and coupling of a robust in-line biofilm & corrosion monitoring device

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Corrosion Online Monitoring Spacer (COMS)

Biofilm monitoring probe

2.3.1 Molecularly Imprinted Polymers (MIPs)

• Activity Leads: C. Bottaro (MUN) and L. Gieg (U of C)• Objectives:

• To investigate the potential of using Molecularly Imprinted Polymers (MIPs) to detect MIC through potential chemical signatures.

• MIPs can make inexpensive sensors for chemical detection.

• Approach:• Determine specific chemical species which can indicate the

presence of MIC (information from Activity 1).• Develop and validate a prototype MIP sensor (lab and field).

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2.3.2 Sulfide sensor/biosensor

• Activity Leads: X. Pang (Canmet Materials)• Objectives:

• To develop a highly sensitive and selective biosensor for the detection of sulfide and potential monitoring of MIC.

• This biosensor platform is based on single-walled carbon nanotubes (SWCNTs) in conductive polymers which can be functionalized using biomolecules such as enzymes, mediators, and biomarkers.

• Approach:• Functionalize and test the sensor platform with a biological sulfide

oxidase enzyme. • Several bacterial sources of this enzyme will be examined and purified

for incorporation into the SWCNT-conductive polymer system. • Further, depending on the results of the proposed work, other enzymes

or biomarkers may be examined to enable more widespread application.

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Activity 3 – Modeling

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Devise better predictive models and risk assessment tools to help improve materials

design and maintenance/operating practices

Activity 3 – Modeling

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3.1 - MIC Mechanistic & Predictive models3.2 - MIC Risk Assessment Models

Overview of MIC Models The oil and gas industry uses models for variety of purposes:

• predicting the effects of corrosion on asset design and life• materials selection• establishment of mitigation and monitoring programs• resource prioritization and optimization of risk-based

inspection (RBI)

General groups of models usedin this project:

• Molecular Models

• Mechanistic Models

• Risk-Based Models

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Molecular Mechanistic

Risk-Based

Molecular Modeling• Computational methods used to model the behavior of

processes at the atomistic and molecular levels. • Good approach to better understand complex chemical

and biological behaviors and interactions.• Can be used to investigate MIC processes that are difficult

to evaluate macroscopically (e.g. mechanisms within the biofilm or at the bio-film/substrate interface).

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Interaction of DNA and a Carbon NanotubeSource: https://www.sas.upenn.edu/~robertjo/html-physics/research/

Swimming Behavior of Microorganisms at SurfacesSource: https://phys.org/news/2014-09-cells-biofilms-surfaces.html#jCp

Mechanistic MIC Models• Predict corrosion rates by attempting to simulate actual physical, chemical and/or biological processeswithin an MIC environment, such as a biofilm or under solid deposits.

• These mechanistic models are typically semi-empirical in nature, and require calibration with limited experimental data (laboratory or field).

• The majority of existing models have focused on Sulfate Reducing Bacteria (SRB).

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Mechanistic MIC Models36

Source: https://it.dreamstime.com/illustrazione-di-stock-formazione-di-biofilm-image73998615

Example Parameters• Bulk Fluid Properties:

• Fluid Chemistry• pH, T, P• Flow Rates

• Bio-film Properties:• Biotic factors (counts, growth rates,

community analysis)• Mass Transport (nutrients, metabolites,

other chemistries)• Geometric (biofilm thickness) • Temporal factors (e.g. biofilm

formation, growth and detachment)• Surface Properties:

• Substrate Metallurgy• Electrochemical potential (local

chemistry at surface)• Functional Aspects:

• Mitigation parameters (biocide application, pigging frequency)

Risk-Based MIC Models• Risk-based MIC models are practical approaches used to help predict and identify the potential magnitude and location of MIC threats in oil and gas infrastructure such as production facilities or pipeline systems.

• Currently used by industry for planning inspectionand maintenance activities of assets to ensure optimal safety and reliability in resource-constrained operations.

• Risk-based methods are well established, and are built into a number of existing industry standards and recommended guidelines (e.g. ISO 17776).

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Risk-Based Approaches• Qualitative Methods:

• Uses subject matter experts to rank corrosion threats into categories (e.g. low, medium or high).

• Based on expertise, experience and similar situations in the industry.

• Quantitative Methods:• Uses mathematical relations to quantify the risk as deterministic values

or probabilities as opposed to the general risk ratings used in qualitative approaches.

• Typically uses deterministic or probabilistic equations (i.e. empirical or mechanistic based models) to relate chemical, physical and microbiological activities to MIC susceptibility or risk factors.

• Semi-Quantitative Methods:• Combines the major benefits of qualitative and quantitative

approaches (e.g. speed of the qualitative and rigor of the quantitative).

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Project Modeling Activities

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MolecularModeling

Mechanistic MIC

Model #1

ProbabilisticMIC Risk Model(Quantitative)

Inform

Inform

Mechanistic MIC

Model #2

MIC Risk BasedInspection (RBI)

Model(Semi-Quantitative)

Inform

MUN Innotech/UofA/VIA/DNV-GL

3.1 MIC Mechanistic & Predictive Models

Molecular Modeling• Activity Lead: F. Kahn (MUN)• Objectives:

• To shed light on the MIC process at the molecular level by understanding interactions occurring at the corrosion interface.

• To inform (macroscopic) mechanistic model.• Approach:

• Use of molecular modeling tools (Material Studio)• Conduct molecular dynamics simulations for various

adsorbate/substrate combinations and temperatures involved in MIC; assumed adsorbates present in the biofilm including O2, HS-, H2S and H2O.

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3.1 MIC Mechanistic & Predictive Models

Mechanistic Model #1• Activity Lead: F. Kahn (MUN)• Objectives:

• To investigate the effect of bacteria metabolism on pitting in an electro-active biofilm system, and the effect of biofilm growth/thickness on pitting rate.

• Approach:• Sulphate utilization for bacteria metabolism was assumed. • Sulphate diffusivity through biofilm thickness to metal surface as the

limiting step in cathodic reaction (assumed). • Model predicts pitting rates based on mass and energy transfer

resistivity. • Effect of biofilm thickness on mass transfer (nutrient species transfer) is

considered.• Framework for modeling electro-active microbial biofilms performing

direct electron transfer also adopted.

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3.1 MIC Mechanistic & Predictive Models

Mechanistic Model #2• Activity Leads: T. Haile (Innotech AB) and J. Wolodko (U of A)• Objectives:

• To develop a mechanistic MIC model which can predict localized MIC rates in pipelines based on microbiological growth kinetics.

• Approach:• Predicts sessile cell concentration based on the assumption that

planktonic cells are the initial seeds for eventual biofilm formation.

• The model calculates biofilm thickness, flow induced detachment rate, and biomass production (biofouling) based on pipeline operating conditions including temperature and flow velocity.

• Assumes dual Monod growth kinetics. • Currently developed for SRB only, but being extended to other

microbe types such as APBs, IOBs & IRBs.

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3.1 MIC Risk Assessment Models

Quantitative Risk Model (Probabilistic)• Activity Lead: F. Kahn (MUN)• Objectives:

• To develop a practical MIC Quantitative Risk Model to meet the end users’ requirements by integrating a probabilistic failure rate model with a MIC failure consequence model.

• To share the model with industry for testing and feedback, and to develop recommendations for existing integrity assessment, standards, and guidelines.

• Approach:• Perform a comprehensive statistical analysis on MIC research work to

date. • Develop a network-based probabilistic model to assess the MIC

potential in the oil and gas industry.• Develop MIC Risk Index for process operations model.• Develop Dynamic MIC Failure Analysis model.

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3.1 MIC Risk Assessment Models

Semi-Quantitative Risk based Inspection Model• Activity Lead: T. Shovhus (VIA), R. Eckert (DNV GL), J. Wolodko (U of

A)

• Objectives:• To develop a practical Risk Based Inspection (RBI) approach for

assessing MIC in oil & gas facilities.

• To help inform existing industry standards and guidelines.

• Approach:• Build off existing qualitative and semi-qualitative approaches.

• Incorporate use of molecular microbiological methods (MMM).

• Work with select industry partners to obtain inspection datasets for verification of methods.

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Activity 4 - Translation

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G = Genomics and itsE3 = Ethical, Environmental, EconomicL = LegalandS = Social Aspects

Improve new technology adoption and corrosion control strategies to reduce potential failures

Activity 4 - Translation

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4.1 – Genomics in O&G – Assessing Barriers 4.2 – Recommended Guideline Development4.3 – Dissemination and Industry Engagement

G = Genomics and itsE3 = Ethical, Environmental, EconomicL = LegalandS = Social Aspects

4.1 Genomics in O&G – Assessing Barriers

• Activity Leads: J. Wolodko and L. Lefsrud (U of A)• Objectives:

• To assess MIC knowledge and technology transfer.• To quantify prevalence of MIC failures and methods used to assess

these failures.• To summarize current MIC assessment and management practices

in industry.• To assess how novel genomics tools & methods can best be

adopted and integrated into practice.• Approaches:

• Use of social science/business research methods such as literature reviews, bibliometric analysis and stakeholder surveys.

• Work with regulatory data and reports to assess MIC prevalence.

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4.1 Genomics in O&G – assessing barriers

48Source: Hashemi, Bak, Khan, Hawboldt, Lefsrud, Wolodko (2018) CORROSION, v.74, n.4

• Examples of Bibliometric Analysis of MIC Research:

Legend:

- Corrosion Science

- Materials Science

- Microbiology

- Water/EnvironmentalScience

4.2 Recommended Guideline Development

• Activity Leads: T. Skovhus (VIA University) and R. Eckert (DNV GL)

• Objectives:• To help develop or update industry recommended guidelines

and standards related to MIC detection, mitigation and management.

• Approaches:• Work with existing technical standards organizations and

their committees to inform guideline/standards development (e.g. NACE, DNV GL, ISO, etc).

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4.2 Recommended Guideline Development

• New: NACE Task Group 561 (Molecular Microbiology Methods: Sample Handling and Laboratory Processing) – initiated April 2018.

• New Development of a MIC Failure Analysis & Investigation Guideline – for 2019.

• Review of DNV-RP-G101 (Risk based inspection of offshore topsides static mechanical equipment) – inclusion of new appendix for 2019.

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4.3 Dissemination/Industry Engagement

• Participation: All project teams!• Objectives:

• To disseminate project findings through journal articles, and presentations in conferences/workshops.

• To host project related events (geno-MIC Industry Workshops) and Chair specialty symposiums and conferences (NACE, ISMOS, etc).

• To engage industry partners regarding sharing of results, information gathering/surveys, coordination of samples, etc.

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ISMOS-7 June 18-21, 2019

http://www.ismos-7.org/

Date: 15-21 June, 2019Venue: Halifax, Nova Scotia, Canada

(new conference center)Local Organizer: Genome Atlantic

(Andy Stone and his team)Call for Abstracts: 1 Sept. – 1 Dec., 2018

4.3 Dissemination

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Publications 2018 (to date)• Journal Publications:

• Sharma, M., Voordouw, J., Shen, Y. & Voordouw, G. 2018. Effect of long term application of tetrakis(hydroxymethyl)phosphonium sulfate (THPS) in a light oil producing oilfield. Biofouling. In Press.

• Liu, H., Sharma, M., Wang, J., Cheng, Y. F., & Liu, H. 2018. Microbiologically influenced corrosion of 316L stainless steel in the presence of Chlorella vulgaris. International Biodeterioration & Biodegradation 129:209-216.

• Skovhus, T.S., Andersen, E.S., & Hillier, E. 2018. Management of microbiologically influenced corrosion in risk-based inspection analysis. SPE Production & Operations.

• Eckert, R.B., & Skovhus, T.S. 2018. Advances in the application of molecular microbiological methods in the oil and gas industry and links to microbiologically influenced corrosion. International Biodeterioration & Biodegradation 126: 169-176.

• Frenzel, M., Passman, F., Skovhus, T.S., & Whitby, C. 2018. 5th International Symposium on Applied Microbiology and Molecular Biology in Oil Systems. International Biodeterioration & Biodegradation 126: 167-168.

• Eckert, R.B., & Buckingham, K. 2017. Investigating pipeline corrosion failures. Inspectioneering Journal 23.• Hashemi, S.J., Bak, N., Khan, F., Hawboldt, K., Lefsrud, L., & Wolodko, J. 2018 Bibliometric analysis of

microbiologically influenced corrosion (MIC) of oil and gas engineering systems. Corrosion 74: 468-486.

• NACE CORROSION 2018 Conference Papers:• Wolodko J., Eckert R., Haile T., Hashemi J., Khan F., Marciales Ramirez A., Taylor C., Skovhus T.L. 2018.

Modeling of Microbiologically Influenced Corrosion in the Oil and Gas Industry – Past, Present and Future. Paper No. C2018-11398, NACE International Corrosion Conference 2018, Phoenix AZ.

• Pang, X. 2018. Electrochemical sensor for monitoring microbiologically influenced corrosion. Paper No. C2018-11628, NACE International Corrosion Conference 2018, Phoenix AZ.

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Thank you for your attention!

Questions?

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