Advertisement

Proposed Risk Management Decision Support Methodology for Oil and Gas Construction Projects

  • Mohammed K. Al MhdawiEmail author
Conference paper
  • 68 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Oil and gas construction projects are complex and risky due to their dynamic nature and environment, as they involve a considerable number of stakeholders. The contracting companies in Iraq find many challenges when managing the risky events in an environment that categorized with poor supplies, security threats, unskilled workforce and logistics difficulties. Evidence in the literature indicates that there is a lack of unified methodology for project management and especially for risk management in Iraq. Also, the rise in the global energy demand increases the need for a workable, effective and efficient risk management methodology for such projects. Thus, the purpose of this research is to develop an integrated decision support methodology for managing the risk factors in oil and gas construction projects in Iraq. The proposed methodology has been developed to support the local and international contracting companies working in Iraqi oil and gas fields when making decisions regarding the risk factors during the project life cycle. The proposed methodology consists of the following phases: risk planning, risk identification using documentation review and expert interviews, risk analysis using a multi-criteria risk analysis model based on fuzzy set theory, risk effect prediction on project time and cost using artificial neural network (ANN), selection of risk response actions using Gravitational Search Algorithm (GSA) optimization technique and finally, designing an integrated web-based risk management decision support platform. The adopted methodology will enable decision makers to assess the oil and gas projects risky events, support their decision during planning and work implementation stages, gain experience in risk management through exercising and implementing risk management on scientific and documented bases; organize and document the knowledge-based information for decision makers.

Keywords

Oil and gas Project management Risk management Decision support system 

References

  1. Abdulsattar A (2017) Performance in construction industry in post-conflict situations—Iraq as a case study. Am J Eng Res (AJER) 6(9):188–195Google Scholar
  2. Accenture (2012) Developing strategies for the effective delivery of capital projects: accenture global survey of the utilities industry. Accenture, IrelandGoogle Scholar
  3. Adam G, Humphreys P (2008) Encyclpedia of decision making and decision support system technologies. Information Science Reference, UKCrossRefGoogle Scholar
  4. AL-Zwainy S, Mohammed A, Raheem H (2016) Investigation and assessment of the project management methodology in Iraqi construction sector. Int J Appl Eng Res 1(4):2494–2507Google Scholar
  5. Ana-Maria D, Doina M (2012) The necessity of risk management programme in organizations. Ovidius University Annals, Economic sciences series, issue 2, Ovidius University Press, pp 961–963Google Scholar
  6. Arthur W, Richard H (1995) Risk management and insurance. McGraw-Hill, USAGoogle Scholar
  7. Aven T, Vinnem J, Wiencke H (2007) Decision framework for risk management, with application to the offshore oil and gas industry. Reliab Eng Syst 92(4):433–448CrossRefGoogle Scholar
  8. Balanchard M (2018) Iraq in brief. Congressional Research Service, USAGoogle Scholar
  9. Berg P (2010) Risk management: procedures, methods and experiences. RT&A 2(17):79–95Google Scholar
  10. British Petroleum (PB) (2017) Statistical review of world energy: oil production. British Petroleum, UKGoogle Scholar
  11. Burek P (2007) Collaborative tools and techniques to build the project risk plan. PMI, USAGoogle Scholar
  12. Carvalho M, Rabechini R (2015) Impact of risk management on project performance: the importance of soft skills. Int J Prod Res 53(2):321–340CrossRefGoogle Scholar
  13. Cheraghi E, Khalilzadeh M, Shojaei S, Zohrehvand S (2017) A mathematical model to select the risk response strategies of the construction projects: case study of Saba Tower. In: Proceeding of the CENTERIS—international conference on ENTERprise information systems/projMAN—international conference on project management/HCist—international conference on health and social care information systems and technologies, CENTERIS/ProjMAN/HCist, 8–10 Nov 2017, Elsevier, Barcelona, Spain, pp 609–619Google Scholar
  14. Crockford N (1982) The bibliography and history of risk management: some preliminary observations. Geneva Pap Risk Insur 7(2):169–179CrossRefGoogle Scholar
  15. Dehdasht G, Zin M, Keyvanfar A (2015) Risk classification and barrier of implementing risk management in oil and gas construction companies. Dehdasht, Rosli & Keyvanfar/Jurnal Teknologi (Sciences & Engineering), 77(16):161–169Google Scholar
  16. De Maere d’Aertrycke G, Ehrenmann A, Smeers Y (2017) Investment with incomplete markets for risk: the need for long-term contracts. Energ Policy 105:571–583CrossRefGoogle Scholar
  17. Dinu A (2012) Modern methods of risk identification in risk management. Int J Acad Res Econ Manage Sci 1(6):67–71Google Scholar
  18. Donovan W (2013) Iraq’s petroleum industry: unsettled issues. USA: Middle East Institute Eia., 2017. Monthly energy review. US energy information administration. Department of Energy, Washington, D.C., USAGoogle Scholar
  19. Elhoush R, Kulatunga U (2017) The effectiveness of project risk management: a study within the libyan oil and gas industry. In: Proceedings of the 13th international postgraduate research conference (IPGRC), 14–15 Sept 2017, University of Salford, UK, pp 680–691Google Scholar
  20. EL Khalek H, Aziz R, Kamel H (2016) Risk and uncertainty assessment model in construction projects using fuzzy logic. Am J Civ Eng 4(1):24–39CrossRefGoogle Scholar
  21. El-Sayegh S, Mansour M (2015) Risk assessment and allocation in highway construction projects in the UAE. J Manage Eng 26(4):431–438Google Scholar
  22. Giannakis M, Louis M (2011) A multi-agent-based framework for supply chain risk management. J Purchasing Suppl Manag 17(1):23–31CrossRefGoogle Scholar
  23. Haykin S (2018) Neural networks and learning machines, 3rd edn. Pearson India, IndiaGoogle Scholar
  24. Hong J, Shen Q, Xue A (2016) Multi-regional structural path analysis of the energy supply chain in China’s construction industry. Energ Policy 92:56–68CrossRefGoogle Scholar
  25. Hutchins G (2016) ISO: risk-based thinking, 1st edn. CERM Academy Series on Enterprise Risk Management, USAGoogle Scholar
  26. Iraqi Extractive Industries Transparency Initiative (IEITI) (2015) Iraqi extractive industries transparency initiative (IEITI) oil export, local consumption and field development. PWC, USAGoogle Scholar
  27. Jayaraman R, Colapinto C, La Torre D, Malik T (2015) Multi-criteria model for sustainable development using goal programming applied to the United Arab Emirates. Energ Policy 87:447–454CrossRefGoogle Scholar
  28. Jin X, Zhang G, Liu J, Feng Y, Zuo J (2017) Major participants in the construction industry and their approaches to risks: a theoretical framework. In: Proceeding of the 7th international conference on engineering, project, and production management, 21–23 Sept 2016, Elsevier, Bialystok, Poland, pp 314–320Google Scholar
  29. Kendrick T (2015) Identifying and managing project risk: essential tools for failure—proofing your project, 3rd edn. AMACOM, USAGoogle Scholar
  30. Knight A, Ruddock L (2008) Advanced research methods in the built environment. Wiley-Blackwell, USAGoogle Scholar
  31. Lam K, Wang D, Lee P, Tsang Y (2007) Modelling risk allocation decision in construction contracts. Int J Proj Manag 25(5):485–493CrossRefGoogle Scholar
  32. Laryea S (2008) Risk pricing practices in finance, insurance and construction. In: Proceedings of the construction and building research conference of the royal institution of chartered surveyors, 4–5 Sept 2008, COBRA, Dublin, Ireland, pp 1–16Google Scholar
  33. Lazarevska M, Knezevic M, Cvetkovska M (2012) Application of artificial neural networks in civil engineering. Tehnicki Vjensnik 21(6):1353–1359Google Scholar
  34. Maniruzzaman F, AL-Saleem K (2017) The energy and environment dilemma: sustainably developing iraqi oil and gas in international law and policy—prospects and challenges. Oil, Gas & Energy Law Intelligence (OGEL), UKGoogle Scholar
  35. Mehr R, Hedges A (1963) Risk management in the business enterprise. Irwin, USAGoogle Scholar
  36. Motaleb O, Kishk M (2012) Risk response plan framework for housing construction project delay in the UAE. In: Proceedings of RICS, USAGoogle Scholar
  37. Mubin S, Ghaffar M (2008) Risk analysis for construction and operation of gas pipeline projects in Pakistan. Pak J Eng Appl Sci 2:22–37Google Scholar
  38. Mubin S, Mannan A (2013) Innovative approach to risk analysis and management of oil and gas sector EPC contracts from a contractor’s perspective. J Bus Econ 5(2):149–170Google Scholar
  39. Muhammad S, Mhdhav N (2016) A fuzzy-bayesian model for risk assessment in power plant projects. In: Proceeding of the MANagement/conference on health and social care information system and technologies, CENTERIS/ProjMAN/HCist, 5–7 Oct 2016, Procedia Computer Science, Porto, Portugal, pp 963–970Google Scholar
  40. Myers M (2009) Qualitative research in business and management. Sage, UKGoogle Scholar
  41. Nicholas M, Steyn H (2011) Project management for business, engineering, and technology: principles and practice, 3rd edn. Elsevier, UKGoogle Scholar
  42. Nieto-Morote A, Ruz-Vila F (2011) A fuzzy approach to construction project risk assessment. Int J Proj Manage 29(2):220–231zbMATHCrossRefGoogle Scholar
  43. Project Management Institute (PMI) (2008) A guide to the project management body of knowledge, 4th edn. PMI, USAGoogle Scholar
  44. Rezakhani P (2012) Classifying key risk factors in construction projects. Bull Inst Polytech Inst Jassy Constr Archit Sect 58(2):27–38Google Scholar
  45. Roghanian E, Mojibian F (2015) Using fuzzy FMEA and fuzzy logic in project risk management. Iran J Manag Stud (IJMS) 8(3):373–395Google Scholar
  46. Salas R (2014) Managing risks at the early phases of oil and gas major capital projects. In: Proceedings of the SPE E&P health, safety, security and environmental conference—Americas, 16–18 Mar 2015. Curran Associates, Inc., USA, Colorado, pp 1–6Google Scholar
  47. Salazar-Aramayo L, Rodrigues-da-Silveira R, Rodrigues-de-Almeida M, de Castro-Dantas N (2013) A conceptual model for project management of exploration and production in the oil and gas. Int J Project Manage 31(4):589–601CrossRefGoogle Scholar
  48. Sang L, Ian F, Raja I (2014) Development of a two-step neural network-based model to predict construction cost contingency. J Inf Technol Constr (ITcon) 19:399–411Google Scholar
  49. Scott H, Gregory N (1964) Risk management and insurance. Irwin/McGraq-Hill, USAGoogle Scholar
  50. Scott H, Gregory N (2003) Risk management and insurance. Irwin/McGraq-Hill, USAGoogle Scholar
  51. Seddeeq A, Assaf S, Abdallah A, Hassanain M (2019) Time and cost overrun in the Saudi Arabian oil and gas construction industry. Buildings 9(41):2–19Google Scholar
  52. Shad K, Lain W (2015) A conceptual framework for enterprise risk management performance measure through economic value added. Glob Bus Manag Res Int J 7(2):1–11Google Scholar
  53. Siang C, Ali S (2012) Implementation of risk management in the Malaysian construction industry. J Surv Constr Prop 3(1):1–15Google Scholar
  54. Smith N, Merna T, Jobling P (2013) Managing risk in construction projects, 2nd edn. Black Well Publishing Inc., UKGoogle Scholar
  55. Smith N, Merna T, Jobling P (2014) Managing risk in construction projects, 3rd edn. Black Well Publishing Inc., UKGoogle Scholar
  56. Subramanyan H, Sawant P, Bhatt V (2012) Construction project risk assessment: development of model based on investigation of opinion of construction project experts from India. J Constr Eng Manag 138(3):409–421CrossRefGoogle Scholar
  57. Syedhosini S, Noori S, Hatefi M (2009) An integrated methodology for assessment and selection of the project risk response action. Risk Anal 29(5):752–763CrossRefGoogle Scholar
  58. Tamak J, Bindal D (2013) An empirical study of risk management and control. Int J Adv Res Comput Sci Softw Eng 3(12):279–282Google Scholar
  59. Taroun A (2008) Towards a better modelling and assessment of construction risk: insights from a literature review. Int J Proj Manag 32(1):101–115CrossRefGoogle Scholar
  60. Washington State Department of Transportation (WSDOT) (2014) Project risk management guide. Engineering and Regional Operations Development Division, Design Office, USAGoogle Scholar
  61. Waziri B, Bala K, Bustani S (2017) Artificial neural networks in construction engineering and management. Int J Archit Eng Constr 6(1):50–60Google Scholar
  62. World Bank (2017) Iraq: systematic country diagnostic. Report No. 112333-IQ. World bank, USAGoogle Scholar
  63. World Bank Group (2018) Iraq economic monitor from war to reconstruction and economic recovery. World Bank Group, USAGoogle Scholar
  64. Zhan Y (2016) Selecting risk response strategies considering project risk interdependence. Int J Proj Manage 34(5):819–830CrossRefGoogle Scholar
  65. Zhao X, Luo D, Xia L (2012) Modelling optimal production rate with contract effects for international oil development projects. Energy 45(1):662–668CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of StrathclydeGlasgowUK

Personalised recommendations