Analysis of gas production data via an intelligent model: application to natural gas production

References

1. Ahmadi, M.A.
   [2012]. Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilibria, 314, 46–51.
   [Google Scholar]
2. Ahmadi, M.A.
   [2016]. Modeling solubility of carbon dioxide in reservoir brine via smart techniques: application to carbon dioxide storage. International Journal of Low-Carbon Technologies, 11(4), 441–454.
   [Google Scholar]
3. Ahmadi, M.A. and Chen, Z.
   [2018]. Development of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs. Petroleum, doi: 10.1016/j.petlm.2018.06.002.
   https://doi.org/10.1016/j.petlm.2018.06.002 [Google Scholar]
4. Ahmadi, M.A. and Golshadi, M.
   [2012]. Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering, 98, 40–49.
   [Google Scholar]
5. Ahmadi, M.A. and Shadizadeh, S.R.
   [2012]. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel, 102, 716–723.
   [Google Scholar]
6. Ahmadi, M.A., Ahmad, Z., Phung, L.T.K., Kashiwao, T. and Bahadori, A.
   [2016]. Estimation of water content of natural gases using particle swarm optimization method. Petroleum Science and Technology, 34 (7), 595–600.
   [Google Scholar]
7. Ahmadi, M.A., Ebadi, M., Shokrollahi, A. and Majidi, S.M.J.
   [2013]. Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing, 13(2), 1085–1098.
   [Google Scholar]
8. Ahmadi, M.A., Soleimani, R., Lee, M., Kashiwao, T. and Bahadori, A.
   [2015]. Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Petroleum, 1 (2), 118–132.
   [Google Scholar]
9. Ahmed, T.
   [2010]. Reservoir engineering handbook. 4th Edition., Gulf Professional Publishing, 1235–1244.
   [Google Scholar]
10. Arps, J.J.
    [1945]. Analysis of decline curves. Transactions of the AIME, 160(01), 228–247.
    [Google Scholar]
11. Arulmozhi, K. T. and Sheelarani, R.
    [2012]. Prediction of ultrasonic velocities in ternary oxide glasses using microstructural properties of the constituents as predictor variables; Artificial Neural Network (ANN) approach. Scientia Iranica, 19 (1), 127–131.
    [Google Scholar]
12. Baughman, D.R. and Liu, Y.A.
    [2014]. Neural networks in bioprocessing and chemical engineering. Academic press.
    [Google Scholar]
13. Blasingame, T.A. and Lee, W.J.
    [1986]. Properties of homogeneous reservoirs, naturally fractured reservoirs, and hydraulically fractured reservoirs from decline curve analysis. In Permian Basin Oil and Gas Recovery Conference. Society of Petroleum Engineers.
    [Google Scholar]
14. Ebrahimi, M.
    [2010]. Enhanced estimation of reservoir parameters using decline curve analysis. In Trinidad and Tobago Energy Resources Conference. Society of Petroleum Engineers.
    [Google Scholar]
15. Engelbrecht, A.P.
    [2007]. Introduction to computational Intelligence. Computational Intelligence An Introduction. Jonh Wiley & Sons, Ltd, 3–4.
    [Google Scholar]
16. Fetkovich, M.J.
    [1980]. Decline curve analysis using type curves. Journal of Petroleum Technology, 32(06), 1–065.
    [Google Scholar]
17. Freeman, J.A. and Skapura, D.M.
    [1991]. Algorithms, applications, and programming techniques. Addison-Wesley Publishing Company, USA.
    [Google Scholar]
18. Gentry, R.W.
    [1972], Decline-Curve Analysis. Journal of Petroleum Technology, 24, 38–41.
    [Google Scholar]
19. Gentry, R.W. and McCray, A.W.
    [1978]. The effect of reservoir and fluid properties on production decline curves. Journal of Petroleum Technology, 30 (09), 1–327.
    [Google Scholar]
20. Goodall, C.R.
    [1993]. 13 Computation using the QR decomposition. Vol. 9, Elsevier, Amsterdam, Netherlands.
    [Google Scholar]
21. Gramatica, P.
    [2007]. Principles of QSAR models validation: internal and external. QSAR & combinatorial science, 26 (5), 694–701.
    [Google Scholar]
22. Haykin, S.
    [1994]. Neural networks: a comprehensive foundation. Prentice Hall PTR.
    [Google Scholar]
23. Hilal, N., Ogunbiyi, O.O. and Al-Abri, M.
    [2008]. Neural network modeling for separation of bentonite in tubular ceramic membranes. Desalination, 228 (1-3), 175–182.
    [Google Scholar]
24. Hudson, M., Hagan, M. and Demuth, H.
    [2012]. Neural network toolbox user’s guide. Mathworks Inc., Natick, Massachusetts.
    [Google Scholar]
25. Kennedy, J. and Eberhart, R.C.
    [1995]. Particle swarm optimization in Proceedings of IEEE International Conference on Neural Networks. Piscataway December.
    [Google Scholar]
26. Li, K. and Horne, R.N.
    [2005]. Verification of decline curve analysis models for production prediction. In SPE Western Regional Meeting. Society of Petroleum Engineers.
    [Google Scholar]
27. Ling, K. and He, J.
    [2012]. Theoretical bases of Arps empirical decline curves. In Abu Dhabi International Petroleum Conference and Exhibition. Society of Petroleum Engineers.
    [Google Scholar]
28. Mehra, P. and Wah, B.W.
    [1992]. Artificial neural networks: concepts and theory (Vol. 680). Los Alamitos: IEEE Computer Society Press.
    [Google Scholar]
29. Oghena, A.
    [2012]. Pressure Drawdown Decline Curve Analysis. In North Africa Technical Conference and Exhibition. Society of Petroleum Engineers.
    [Google Scholar]
30. Palacio, J.C. and Blasingame, T.A.
    [1993]. Decline curve analysis using type curves—analysis of gas well production data. paper SPE, 25909, 12–14.
    [Google Scholar]
31. Prata, D.M., Schwaab, M., Lima, E.L. and Pinto, J.C.
    [2009]. Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor. Chemical Engineering Science, 64(18), 3953–3967.
    [Google Scholar]
32. Reihanian, M., Asadullahpour, S.R., Hajarpour, S. and Gheisari, K.
    [2011]. Application of neural network and genetic algorithm to powder metallurgy of pure iron. Materials & Design, 32(6), 3183–3188.
    [Google Scholar]
33. Robinson, J. and Rahmat-Samii, Y.
    [2004]. Particle swarm optimization in electromagnetics. IEEE transactions on antennas and propagation, 52(2), 397–407.
    [Google Scholar]
34. Rousseeuw, P.J. and Leroy, A.M.
    [2005]. Robust regression and outlier detection (Vol. 589). John Wiley & sons.
    [Google Scholar]
35. Shi, Y. and Eberhart, R.
    [1998]. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on (pp. 69-73). IEEE.
    [Google Scholar]
36. Shirman, E.I.
    [1999]. Universal approach to the decline curve analysis. Journal of Canadian Petroleum Technology, 38(13).
    [Google Scholar]
37. Zheng, S.Y. and Fei, W.
    [2008]. Application of deconvolution and decline-curve analysis methods for transient pressure analysis. In Europec/EAGE Conference and Exhibition. Society of Petroleum Engineers.
    [Google Scholar]