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Original Article | Open Access

A simulation-based method to determine the coefficient of hyperbolic decline curve for tight oil production

Yanlong Yu, Zhangxin Chen( ), Jinze Xu
Department of Chemical and Petroleum Engineering, University of Calgary, Alberta, T2N 1N4, Canada
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Abstract

Tight oil reservoirs are characterized by the ultra-low porosity and permeability, making it a great challenge to enhance oil production. Owing to the fast development in hydraulic fracturing technology of horizontal wells, tight oil has been widely explored in North America. Individual wells have a long term of low production after a rapid production decline. This causes low cumulative production in tight oil reservoirs. A rate decline curve is the most common method to forecast their production rates. The forecast can provide useful information during decision making on future development of production wells. In this paper, a relationship is developed between the parameters of a hyperbolic decline curve and the reservoir/fracture properties when a reservoir simulation model is used based on the data from a real field. Understanding of this relationship improves the application of the hyperbolic decline curve and provides a useful reference to forecast production performance in a more convenient and efficient way.

Keywords

reservoir simulationTight oildecline curve

References

 

Arps, J.J. Analysis of decline curve. Trans. AIME 1945, 160(1): 228-247.
Crossref Google Scholar
 

Attanasi, E.D., Coburn, T.C., Ran-McDonald, B. Statistical detection of flow regime changes in horizontal hydraulically fractured bakken oil wells. Nat. Resour. Res. 2019, 28(1): 259-272.
Crossref Google Scholar
 
Chen, Z. Reservoir simulation: Mathematical techniques in oil recovery. Philadelphia, USA, SIAM, 2007.https://doi.org/10.1137/1.9780898717075
Crossref
 
Chen, Z., Huan, G., Ma, Y. Computational methods for multiphase flows in porous media. Philadelphia, USA, SIAM, 2006.https://doi.org/10.1137/1.9780898718942
Crossref
 

Cipolla, C.L., Warpinski, N.R., Mayerhofer, M., et al. The relationship between fracture complexity, reservoir properties, and fracture-treatment design. SPE Prod. Oper. 2010, 25(4): 438-452.
Crossref Google Scholar
 
Computer Modelling Group Ltd. GEM User's Guide. 2017.
 
Doublet, L.E., Pande, P.K., McCollum, T.J., et al. Decline curve analysis using type curves–analysis of oil well production data using material balance time: Application to field cases. Paper SPE 28688 Presented at International Petroleum Conference and Exhibition of Mexico, Veracruz, Mexico, 10-13 October, 1994.https://doi.org/10.2118/28688-MS
Crossref
 
Fekete Associates Inc. Decline curve analysis. 2010.
 
Ghaderi, S.M., Clarkson, C.R., Ghanizadeh, A., et al. Improved oil recovery in tight oil formations: Results of water injection operations and gas injection sensitivities in the bakken formation of Southeast Saskatchewan. Paper SPE 185030 Presented at SPE Unconventional Resources Conference, Calgary, Alberta, Canada, 15-16 February, 2017.https://doi.org/10.2118/185030-MS
Crossref
 
Höök, M. Depletion and decline curve analysis in crude oil production. Uppsala, Uppsala University, 2009.
 

Li, L., Jiang, H., Wu, K., et al. An analysis of tracer flowback profiles to reduce uncertainty in fracture-network geometries. J. Pet. Sci. Eng. 2019, 173: 246-257.
Crossref Google Scholar
 

Lin, M., Chen, S., Ding, W., et al. Effects of fracture geometries on well production in hydraulic fractured tight oil reservoirs. J. Can. Pet. Technol. 2015, 54(3): 183-194.
Crossref Google Scholar
 

Liu, Y., Wen, D., Wu, X., et al. Comparison of analytical models for hydraulic fracture conductivity. Arab. J. Geosci. 2019, 12(15): 479.
Crossref Google Scholar
 
Sun, H. Advanced production decline analysis and application. Oxford, United Kingdom, Elsevier Inc., 2015.https://doi.org/10.1016/B978-0-12-802411-9.00026-0
Crossref
 

Sun, R., Yu, W., Xu, F., et al. Compositional simulation of CO2 Huff-n-Puff process in Middle Bakken tight oil reservoirs with hydraulic fractures. Fuel 2019, 236: 1446-1457.
Crossref Google Scholar
 

Wachtmeister, H., Lund, L., Aleklett, K., et al. Production decline curves of tight oil wells in eagle ford shale. Nat. Resour. Res. 2017, 26(3): 365-377.
Crossref Google Scholar
 

Xu, J., Wu, K., Li, R., et al. Real gas transport in shale matrix with fractal structures. Fuel 2018, 219: 353-363.
Crossref Google Scholar
 

Xu, J., Wu, K., Yang, S., et al. Real gas transport in tapered noncircular nanopores of shale rocks. AIChE J. 2017, 63(7): 3224-3242.
Crossref Google Scholar
 

Xu, S., Feng, Q., Wang, S., et al. Optimization of multistage fractured horizontal well in tight oil based on embedded discrete fracture model. Comput. Chem. Eng. 2019, 117: 291-308.
Crossref Google Scholar
 

Yang, S., Yi, Y., Lei, Z., et al. Improving predictability of stimulated reservoir volume from different geological perspectives. Mar. Pet. Geol. 2018, 95: 219-227.
Crossref Google Scholar
 
Yu, S., Jiang, Z., Lee, W.J. Reconciling empirical methods for reliable EUR and production profile forecasts of horizontal wells in tight/shale reservoirs. Paper SPE 189802 Presented at SPE Canada Unconventional Resources Conference, Calgary, Alberta, Canada, 13-14 March, 2018.https://doi.org/10.2118/189802-MS
Crossref
 
Yu, Y., Chen, Z., Xu, J., et al. Improved rate decline prediction formula considering the length of hydraulic fractures in a Jilin Field, China. Paper Presented at Geoconvention, Calgary, Canada, 15-19 May, 2017.
 

Zhang, Y., Hu, J., Zhang, Q. Simulation study of CO2 Huff-n-Puff in tight oil reservoirs considering molecular diffusion and adsorption. Energies 2019, 12(11): 2136.
Crossref Google Scholar
 

Zuloaga-Molero, P., Yu, W., Xu, Y., et al. Simulation study of CO2-EOR in tight oil reservoirs with complex fracture geometries. Sci. Rep. 2016, 6(1): 33445.
Crossref Google Scholar
Advances in Geo-Energy Research
Volume 3 Issue 4,
December 2019
Pages 375-380
DOI: 10.26804/ager.2019.04.04
Cite this article:
Yu Y, Chen Z, Xu J. A simulation-based method to determine the coefficient of hyperbolic decline curve for tight oil production. Advances in Geo-Energy Research, 2019, 3(4): 375-380. https://doi.org/10.26804/ager.2019.04.04

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Received: 02 November 2019
Revised: 20 November 2019
Accepted: 20 November 2019
Published: 22 November 2019
© The Author(s) 2019

This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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