International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.9, No.10, Oct. 2017
Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System
Full Text (PDF, 1060KB), PP.1-11
One of the major challenges for researchers and governments across the world is reducing resources-waste or loss. Resources loss can happen if there is not a capable control system that contributes to environmental change. The specific aim is to create user-friendly control and monitoring system to reduce the waste in resources. New Artificial intelligence techniques have been introduced to play an important part in developing such systems.
In oilfields, the oil is extracted then distributed via oil pipes until it reaches the end consumer. This operation will occur without a full and complete monitoring for the oil in the pipeline’s journey to the provider. Although, the existing oilfield monitoring systems can communicate locally but they will not send information back to the main provider. That means the provider is not aware of the whole circumstances happened in the transportation process. That gives the provider no control on the process. For example, a sudden decision from the main provider to stop transporting to a specific destination or knowing where the leakage is and which pipe is leaking in the pipelines grid.
This paper, introduces for the first-time oilfield pipeline Neuro-fuzzy (NF) supervision system using Simscape simulation software package. This system can be the first step solution to keep real time communication between the main provider and the oil transportation process in the oilfields and enables the provider to have full supervision on the oil pipes grid. The simulation supervision system illustrates a clear real-time oilfield pipeline grid that gives the provider the ability to control and monitor pipeline grid and prioritise the recovering process. The two parameters selected for control and monitoring were volume and pressure. The results in this paper show full control for the NF supervision system on the transportation process.
Cite This Paper
Nagham H. Saeed, Maysam.F. Abbod, "Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.10, pp.1-11, 2017. DOI: 10.5815/ijisa.2017.10.01
"Oil and environment", Eccos, 2016. [Online]. Available: http://www.eccos.us/al-gore-global-warming. [Accessed: 18- Jun- 2017].
C. Carter. (2015 Nov). Brent Crude Oil performance 2016. [Online]. Available: http://moneyweek.com/prices-news-charts/oil. [Accessed: 18- Jun- 2017].
B. Titlow and M. Tinger, Protecting the Planet Environmental Champions from Conservation to climate change, Prometheus Books, 2016.
W. Burroughs, Climate change. Cambridge: Cambridge University Press, 2011.
R. J. Kopp. (2006, November 17) "Replacing Oil: Alternative Fuels and Technologies | Resources for the Future", Rff.org, p.15-18. 2016. [Online]. Available: http://www.rff.org/research/publications/replacing-oil-alternative-fuels-and-technologies. [Accessed: 18- Jun- 2017].
"Reliable and innovative solutions for the oil and gas industry," Oil & Gas -Siemens, W3.siemens.com, 2016. [Online]. Available: http://w3.siemens.com/markets/global/en/oil-gas/Pages/oil-gas.aspx. [Accessed: 18- Jun- 2017].
“Safety Monitoring: Advanced Gas, Flame, and Particle Detection Solutions”, Emerson Process Management, UK, September 2014. 2016. [Online]. Available: http://www2.emersonprocess.com/siteadmincenter/PM%20Rosemount%20Analytical%20Documents /FGD_BRO_Safety_Monitoring.pdf. [Accessed: 18- Jun- 2017].
Y. Yu, CC. Wu, XK. Xing, YC. Shi, YP. Li and LL. Zuo, “Correcting rated pump performance improves heavy oil pipeline efficiency,” Oil & Gas Journal, China University of Petroleum, Beijing, Peoples R China, vol. 112, no.7, pp. 108, 2014.
Je. Bernard. “Security Technology in the Oil and Gas Industry: Managing Risk and Enhancing Operational Efficiency” Oil & Gas Network Magazine, Osprey Informatics, October 2014. 2016. [Online]. Available: http://www.ospreyinformatics.com/osprey-gives-expert-insight-security-technologies-october-2014-oil-gas-network-magazine/ [Accessed: 18- Jun- 2017].
K. Ross and G. Vogler, “Iraqis Mending Own Pipelines,” Oil & Gas Journal, Tulsa, Oklahoma, U.S.A. Vol. 107, no.7, pp. 50-53, 2009.
iDirect - Oil & Gas", Idirect.net, 2016. [Online]. Available: http://www.idirect.net/Applications/Oil-and-Gas.aspx. [Accessed: 18- Jun- 2017].
iDirect, "Connecting the Digital Oil Field White Paper" Idirect.net, 13865 Sunrise Valley Drive Herndon, VA 20171, USA 2016. [Online]. Available: http://file:///D:/Smart%20Grid/New/Supporting%20Doucment/idirect%20oil%20gas.pdf. [Accessed: 18- Jun- 2017]
A. Parwal , G. Parwal, A. Sharma and M. Khan “ Implementation of Fuzzy Technique Based on LabVIEW for Control Gas System Using USB 6009” International Journal of Control and Automation, Vol. 6, No. 3, June, 2013
E. Rich, K. Knight and S. B. Nair, Artificial Intelligence, McGraw-Hill ,1st edition, 2008.
N. Arora and J. Saini, "Estimation and Approximation Using Neuro-Fuzzy Systems", International Journal of Intelligent Systems and Applications (IJISA), vol.8, no.6, pp.9-18, 2016.
J. Ghiasi-Freez, A. Hatampour, P. Parvasi, “Application of Optimized Neural Network Models for Prediction of Nuclear Magnetic Resonance Parameters in Carbonate Reservoir Rocks”, International Journal of Intelligent Systems and Applications (IJISA), vol.7, no.6, pp.21-32, 2015
A. K. Kori, A.K. Sharma and A.K.S. Bhadoriya, “Neuro Fuzzy System Based Condition Monitoring of Power Transformer,” International Journal of Computer Science Issues (IJCSI), vol. 9, issue 2, no 1, March 2012.
M. Egorovna and V. Nikolaevna, “Artificial intelligence in problems of leak definition from the oil pipeline,” International Conference on Mechanical Engineering, Automation and Control Systems (MEACS), 2014.
A. Mohamed, M. Hamdi and S. Tahar, “Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey,” CHAPTER, Data Science and Big Data: An Environment of Computational Intelligence, vol. 24, pp 189-207.
Matlab & Simulink: Simcscap TM User's Guide, R201 5B, The Math Works, Inc., 3 Apple Hill Drive Natick, 2007-2015.
S. N. Danilin, S. A. Shchanikov and A. I. Galushkin, "The research of memristor-based neural network components operation accuracy in control and communication systems," 2015 International Siberian Conference on Control and Communications (SIBCON), pp. 1-6, May 2015.
A. Kusagur, S. F. Kodad and S. B V Ram, "Modeling, Design & Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor," International Journal of Computer Applications, Foundation of Computer Science, vol. 6, no. 12, pp. 29–45, September 2010.
"Piezo Motion and Positioning systems." Physik Instrumente (PI) UK: Piezo, Physikinstrumente.co.uk, 2016. [Online]. Available: http://www.physikinstrumente.co.uk/technologies/piezo/?gclid=CLjmxoObrssCFarpwgodd3QI0w. [Accessed: 18- Jun- 2017]
"Hydraulic Equipment Components Suppliers | Engineers Edge | www.engineersedge.com", Engineersedge.com, 2016. [Online]. Available: http://www.engineersedge.com/engineering/Products_Directory/Fluid_Power_Equipment/Hydraulic_ Equipment_Components/. [Accessed: 18- Jun- 2017]
“Hydraulic Valves: Directional Control Valve, Pressure Control Valve, Flow Control Valve,” Parker Distributor Hydraulics. Damen Technical Agencies (DTA), Dta.eu, 2016. [Online]. Available: http://dta.eu/hydraulics/hydraulic-valves. [Accessed: 18- Jun- 2017]
J. S. R. Jang and Chuen-Tsai Sun, "Neuro-fuzzy modeling and control," in Proceedings of the IEEE, vol. 83, no. 3, pp. 378-406, Mar 1995.
L. Zadeh, “Fuzzy Logic and Softcomputing”, Plenary Speaker, Proceedings of IEEE International Workshop on Neuro Fuzzy Control, Muroran, Japan, 1993.
J. R. Jang, “ANFIS: Adaptive-network, based fuzzy inference system”, IEEE transaction on Systems, Man and Cybernetics, 1993, vol. 23, no. 3, pp. 665-685.
"Gaussian curve membership function - MATLAB gaussmf - MathWorks United Kingdom", Uk.mathworks.com, 2017. [Online]. Available: https://uk.mathworks.com/help/fuzzy/gaussmf.html. [Accessed: 18- Jun- 2017].