A Decision Support Tool for the Conceptual Design of De-oiling Systems

  • Badria Al-Shihi
  • Paul W. H. Chung
  • Richard G. Holdich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)


De-oiling of petroleum wastewater is a major concern in petroleum process engineering. Decision support systems (DSS) have been used in assisting operators in evaluating different disposal options of production water, but not the de-oiling process. Also, no application has been reported in assisting the de-oiling of other petroleum waters such as process, ballast or drainage water. This paper describes a DSS for the COnceptual DEsign of de-oiling Systems (CODES) for handling different types of waste water by supporting the tasks of:
  • assessing the types and magnitudes of waste-water streams

  • exploring the feasibility of mixing different streams.

  • selecting the types of de-oiling equipment at different stream locations

  • considers the need for multi-stage treatment to meet quality requirements stated in standards and regulations.

CODES is implemented in Microsoft Excel and is accessed via a web-based front-end.


Expert System Decision Support System Conceptual Design Activity Model Decision Support Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dharaphop, Jirapong: Expert System for Disposal of Produced Water from Petroleum Exploration and Production in New Mexico, MSc. Thesis, New Mexico Institute of Mining and Technology, New Mexico (1993)Google Scholar
  2. 2.
    Eckles, Wesley W. Jr.: Expert System for Quantitative Log Analysis, Petroleum Engineer International, Vol. 63 (June) 72Google Scholar
  3. 3.
    Miller, Betty A.: Object Oriented Expert Systems and their Applications to Sedimentary Basin Analysis, U.S. G.P.O., Denver (1993)Google Scholar
  4. 4.
    Soto Becerra, Rodolfo.: An Expert System to Select the Appropriate Fracture Treatment Design Model, MSc. Thesis Texas A & M University, Texas (1992)Google Scholar
  5. 5.
    Affleck, Noel; Zamora, Mario: PC-Based Expert System Aids Optimum Mud Selection, Petroleum Engineer International, Vol. 59 (January) 38Google Scholar
  6. 6.
    Courteille, J. M.; Fabre, M.; Hollander, C. R.: An Advanced Solution: The Drilling Advisor, Journal of Petroleum Technology, Vol. 38 (August) 899–904Google Scholar
  7. 7.
    Eckles, Wesley W. Jr.: Expert System for Casing and Tubing Strings, Petroleum Engineer International, Vol. 63 (August) 55–58Google Scholar
  8. 8.
    Onan, D. D.; Kulakofsky, D.; Van Domelen, M. S.: Expert Systems Help Design Cementing and Acidising Jobs, Oil & Gas Journal, Vol. 91 (April) 59–61Google Scholar
  9. 9.
    Kulakofsky, David; Crook, Ronald J.: Knowledge Based Expert System Ease Cement Slurry Design, Offshore, Vol. 52 (June), Oklahoma 43Google Scholar
  10. 10.
    Cadmus, Richard H.; Woosley, Melvin D.: Expert Systems Complement Refinery Information Systems, Oil & Gas Journal, Vol. 87 (January) 50–54Google Scholar
  11. 11.
    Takahashi, Kimikazu.; Kateeshock, Tom.: Expert System for Refinery Off-Site Facility Management, ISA Transactions, Vol. 31, No.2, 67–75Google Scholar
  12. 12.
    Damaron, E. Bruce.; Schulze, Randall T.; Bochsler, Daniel C.: Well Control Becomes Target for Expert Systems, Oil and Gas Journal, Vol. 87 (February) 35–40Google Scholar
  13. 13.
    Xiong, Hongjie: STIMEX-An Expert System Approach to Well Stimulation Design, PhD. Thesis Texas A & M University, Texas (1992)Google Scholar
  14. 14.
    Khan, Sameer Ali: An Expert System to Aid in Compositional Simulation of Miscible Gas Flooding, PhD. Thesis University of Texas at Austin (1992)Google Scholar
  15. 15.
    Ayral, T. E.: On Line Expert System for Process Control, Hydrocarbon Processing, Vol. 68 (June) 61–63Google Scholar
  16. 16.
    Heywood, C. H.: Pipeline SCADA Systems: Yesterday, Today, Tomorrow, Pipeline Industry, Vol. 67 (August) 46Google Scholar
  17. 17.
    Kobyakov, A. I.: Include Heuristics in Protection Systems, Hydrocarbon Processing, Vol. 72, No.2, 79Google Scholar
  18. 18.
    Ramanathan, Prasad.; Kannan, Suresh.; Davis, James F.: Use Knowledge Based System Programming Toolkits to Improve Plant Troubleshooting, Chemical Engineering Progress, Vol. 89 (June) 75–84Google Scholar
  19. 19.
    Spriggs, Kevin V.: The Uses of Rule Based Programming in a Unit Level Programmable Controller at Auburn University’s Waste Oil Reprocessing Facility, MSc. Thesis Auburn University (1986)Google Scholar
  20. 20.
    Touchstone, Terrel.; Blackwell, Derek E.; Carter, Grady E.: Expert Systems Trains, Advises Process Operators, Oil and Gas Journal, Vol. 88 (February) 41–44Google Scholar
  21. 21.
    Fowler, Martin; Scott, Kendall: UML Distilled: Applying the Standard Object Modelling Language, Addison-Wesley, USA (1997)Google Scholar
  22. 22.
    Giarratano, J.: CLIPS User’s Guide, NASA, Lyndon B. Johnson Center Information Systems Directorate, Software Technology Branch (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Badria Al-Shihi
    • 1
  • Paul W. H. Chung
    • 2
  • Richard G. Holdich
    • 1
  1. 1.Department of Chemical EngineeringLoughborough UniversityLoughboroughUK
  2. 2.Department of Computer ScienceLoughborough UniversityLoughboroughUK

Personalised recommendations