Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo: A case study in one of super fields in Iran southwest

Abstract

This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data. The NMR logging data have some highly vital privileges over conventional ones. The measured porosity is independent from bearer pore fluid and is effective porosity not total. Moreover, the permeability achieved by exact measurement and calculation considering clay content and pore fluid type. Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir. Therefore, achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege. The input parameters of model were neutron porosity (NPHI), sonic transit time (DT), bulk density (RHOB) and electrical resistivity (RT). The outputs of model were also permeability and porosity values. The structure developed model was build and trained by using train data. Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data. Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir. Moreover, the considerable accuracy of newly ANN-Cuckoo method also demonstrated. This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.

Keywords

Neural network / ANN-Cuckoo / NMR logging / Permeability modeling / Porosity modeling

Acknowledgements

The authors acknowledge the National Iranian Drilling Company (NIDC) for the support rendered in carrying out this research.

References

[1] K.-J. Dunn, D.J. Bergman, G.A. LaTorraca, Nuclear Magnetic Resonance: Petrophysical and Logging Applications vol. 32, Elsevier, 2002.

[2] B. Kenyon, et al., Nuclear magnetic resonance imaging—technology for the 21st century, Oilfield Rev. 7 (3) (1995) 19-33.

[3] M. Jamialahmadi, F. Javadpour, Relationship of permeability, porosity and depth using an artificial neural network, J. Pet. Sci. Eng. 26 (1) (2000) 235-239.

[4] K. Aminian, et al., Prediction of flow units and permeability using artificial neural networks, SPE Western Regional/AAPG Pacific Section Joint Meeting, Long Beach, California, 2003, pp. 19-24 (May).

[5] J.-S. Lim, Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea, J. Pet. Sci. Eng. 49 (3) (2005) 182-192.

[6] H.A. Malki, J. Baldwin,A neuro-fuzzy based oil/gas producibility estimation method, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN' 02 (Cat. No. 02CH37290), IEEE, 2002.

[7] A. Kadkhodaie-Ilkhchi, et al., Petrophysical data prediction from seismic attributes using committee fuzzy inference system, Comput. Geosci. 35 (12) (2009) 2314-2330.

[8] G. Anderson, Coring and Core Analysis Handbook, (1975).

[9] G.M. Hegazy, et al., Innovative and cost-effective coring technique “extended coring" for long intervals of multiple zones with world record-case histories from the UAE, Abu Dhabi International Petroleum Exhibition and Conference, Society of Petroleum Engineers, 2014.

[10] A. Kadkhodaie-Ilkhchi, H. Rahimpour-Bonab, M. Rezaee, A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: an example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran, Comput. Geosci. 35 (3) (2009) 459-474.

[11] R. Rajabioun, Cuckoo optimization algorithm, Appl. Soft Comput. 11 (8) (2011) 5508-5518.

[12] P. Civicioglu, E. Besdok, A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms, Artif. Intell. Rev. 39 (4) (2013) 315-346.

[13] X.-S. Yang, S. Deb, Cuckoo search: recent advances and applications, Neural Comput. Appl. 24 (1) (2014) 169-174.

[14] M.B. Valentín, et al., Estimation of permeability and effective porosity logs using deep autoencoders in borehole image logs from the brazilian pre-salt carbonate, J. Pet. Sci. Eng. 170 (2018) 315-330.

[15] L. Zhu, et al., Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves, J. Geophys. Eng. 15 (3) (2018) 1050-1061.

[16] S. Mohaghegh, Virtual-intelligence applications in petroleum engineering: Part 1—artificial neural networks, J. Pet. Technol. 52 (2000) 64-73 09.

[17] L. Zhu, et al., Application of Multiboost-KELM algorithm to alleviate the collinearity of log curves for evaluating the abundance of organic matter in marine mud shale reservoirs: a case study in Sichuan Basin, China, Acta Geophys. 66 (5) (2018) 983-1000.

[18] H.A. Malki, J. Baldwin,A neuro-fuzzy based oil/gas producibility estimation method, Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on, IEEE, 2002.

[19] M.A. Ahmadi, Z. Chen, Comparison of Machine Learning Methods for Estimating Permeability and Porosity of Oil Reservoirs via Petro-Physical Logs, Petroleum, 2018.

[20] M.-A. Ahmadi, et al., Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence, J. Pet. Sci. Eng. 123 (2014) 183-200.

[21] M. Ali Ahmadi, et al., Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization, Geophys. Prospect. 61 (3) (2013) 582-598.

[22] L. Aggarwal, K. Aggarwal, R.J. Urbanic, Use of artificial neural networks for the development of an inverse kinematic solution and visual identification of singularity zone (s), Procedia CIRP 17 (2014) 812-817.

[23] M.T. Hagan, et al., Neural Network Design vol. 20, PWS publishing company, Boston, 1996.

[24] X. Qu, J. Feng, W. Sun,Parallel genetic algorithm model based on AHP and neural networks for enterprise comprehensive business, Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP' 08 International Conference on, IEEE, 2008.

[25] A. Yamazaki, M. De Souto, T. Ludermir, Optimization of neural network weights and architectures for odor recognition using simulated annealing, Proceedings International Joint Conference on Neural Networks, 2002.

[26] M.A. Ahmadi, Neural network based unified particle swarm optimization for prediction of asphaltene precipitation, Fluid Phase Equilib. 314 (2012) 46-51.

[27] R. Hecht-Nielsen, Theory of the backpropagation neural network, Neural Networks for Perception, Elsevier, 1992, pp. 65-93.