Flow prediction using well log data and intelligent techniques

ABSTRACT

Spatial distribution of petrophysical properties within the reservoirs is important to provide a reliable reservoir model. The most complete reservoir description is generally provided through the identification of flow units. Then, the constructed model can be used for reservoir simulation of different production scenarios within the reservoir. However, before performing modeling and simulation jobs, accurate prediction of flow units is an important task and to reach a reliable petrophysical description first of all one should be able to predict flow units with high precision. Achieving this goal will be discussed in this article.

This paper suggests intelligent techniques using artificial neural network (ANN), fuzzy logic (FL), and neuro-fuzzy (NF) to determine flow units from well log data. For this purpose, available routine core and wireline log data from five wells in Ilam Formation at the Ahwaz super giant oil field are used to find the best intelligent formulation between core-derived flow units and well log data. Validation of the predictive capability of the models was evaluated in one separate cored well (blind test well). The results show that FL-derived flow units had better accuracy with respect to other techniques. This comparison showed that FL can be used as the most reliable intelligent technique for flow unit prediction from well log data in uncored wells.

KEYWORDS:

• Artificial neural network
• flow unit
• flow zone index
• fuzzy logic
• neuro-fuzzy

Acknowledgment

The authors gratefully acknowledge the National Iranian South Oil Company (NISOC) for permission to publish this paper. Behzad Eghdami is thanked for his helpful review comments.

References

• Abbaszadeh, M. D., Fujii, F., and Fujimoto, F. 1996. Permeability prediction by hydraulic flow units-theory and applications. SPE Form. Eval. 11:263–271.
   Google Scholar
• Abdi, Y., Ghane, M., and Haghighi, A. R. 2007. Integrated reservoir characterization and modeling of one Iranian naturally fractured reservoir using laboratory and field data. SPE/EAGE Paper 111120.
   Google Scholar
• Amaefule, J. O., Altunbay, D., Tiab, D., Kersey, D. G., and Keelan, D. K. 1993. Enhanced reservoir description: Using core and log data to identify hydraulic (Flow) units and predict permeability in Uncored intervals/wells. SPE Paper 26436.
   Google Scholar
• Avila, J. C., Archer, R. A., Auckland, U., Mancini, E. A., Alabama, U., and Blasingame, T. A. 2002. A petrophysics and reservoir performance-based reservoir characterization of the Womack Hill (Smackover) Field (Alabama). SPE Paper 77758. SPE Annual Technical Conference and Exhibition, San Antonio, TX, September 29-October 2.
   Google Scholar
• Ebanks, W. J. 1987. Flow unit concept - integrated approach for engineering projects. Abstract, AAPG Annual Convention, 521–522.
   Google Scholar
• Jong-Se, L. 2005. Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J. Pet. Sci. Technol. 49:182–192.
   Web of Science ®Google Scholar
• Khandelwal, M., Singh, T. N. 2010. Artificial neural networks as a valuable tool for well log interpretation. J. Pet. Sci. Technol. 28:1381–1393.
   Web of Science ®Google Scholar
• Orodu, O. D., Tang, Z., and Fei, Q. 2009. Hydraulic (flow) unit determination and permeability prediction: A case study of Block-Shen-95, Liahoe oil field, North east China. J. Appl. Sci. 10: 1801–1816.
   Google Scholar
• Ratchkovski, A., Ogbe, D. O., and Lawal, A. S. 1999. Application of geostatistics and conventional methods to derive hydraulic flow units for improved reservoir description: A case study of endicott field, Alaska. Paper SPE 54587.
   Google Scholar
• Svirsky, D., Ryazanov, A., Pankov, M., Yukos, E. P., and Corbett, P. W. M. 2004. Hydraulic flow units resolve reservoir description challenges in a Siberian oil field. SPE Paper 87056.
   Google Scholar
• Taghavi, A. A. 2005. Improved permeability estimation through use of fuzzy logic in a carbonate reservoir from southwest Iran. SPE Paper 93269.
   Google Scholar
• Lotfizadeh, A. 1965. Fuzzy sets. Inf. Control 8:338–353.
   Google Scholar
• Zhang, F., Shao, C., Li, H., Liu, H., and Li, Y. 2007. Principle of a new flow-unit auto subdividing method and its application in Da Qing Oil field. Paper SPE 105800.
   Google Scholar