International Journal of Nonlinear Analysis and Applications

A novel approach to estimate reservoir permeability using machine learning method

Document Type : Research Paper

Authors

1 Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

2 School of Engineering, Damghan University, Damghan, Iran.

Abstract

Reservoir permeability in upstream of petroleum engineering plays an essential role in crude oil production. Due to the high cost and difficulty in direct measurement of the permeability, having a robust model of this parameter based on the openhole logs and data is preferred. The sonic, volumetric density, gamma ray, total porosity, neutron porosity logs are available in the time of logging and have the highest correlation with reservoir permeability characteristics. To estimate the permeability of the reservoir based on these available data, a new intelligent method of Genetic Algorithm (GA) and Wavelet Neural Network (WNN) is derived. In the developed model, a new objective function has been introduced. For avoiding more complexity of the objective function, the initializing weights of neural network has been done by GA. Then, the training levenberg marquardt algorithm is utilized to update the optimal weighting. In other words, wavelet as activation function of neural network enhances exploitation search abilities of the algorithm and leads to a robust model. In the following, a sample reservoir as a source of data in this field is selected to evaluate the effectiveness of the proposed algorithm in the permeability estimation. For the sake of comparison, two algorithms of BP-ANN and GA-BP, which have been already presented in the literature, are applied for the same data sets and the superiority of developed model in estimation has been illustrated.

Keywords

[1] M.A. Ahmadi and Z. Chen, Comparison of machine learning methods for estimating permeability and porosity
of oil reservoirs via petro-physical logs. Petroleum, (2018).
[2] T. Aifa, Neural network applications to reservoirs: Physics-based models and data models, (2014).
[3] T. A¨ıfa, R. Baouche and K. Baddari, Neuro-fuzzy system to predict permeability and porosity from well log
data: A case study of Hassi R Mel gas field, Algeria. Journal of Petroleum Science and Engineering, 123: (2014)
217-229.
[4] A.K. Alexandridis, and A.D. Zapranis, Wavelet neural networks: A practical guide. Neural Networks, 42: (2013)
1-27.
[5] R. Baouche, T. Aifa and K. Baddari , Intelligent methods for predicting nuclear magnetic resonance of porosity
and permeability by conventional well-logs: a case study of Saharan field. Arabian Journal of Geosciences, 10(24):
(2017) 545.
[6] P.N. da Silva, et al.,. Automatic classification of carbonate rocks permeability from 1H NMR relaxation data.
Expert Systems with Applications, 42(9): (2015) 4299-4309.
[7] A.E. Eiben and S.K. Smit, Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and
Evolutionary Computation, 1(1): (2011) 19-31.
[8] A. Esmailzadeh, S. Ahmadi, R. Rooki, and R. Mikaeil, Oil Reservoir Permeability Estimation from Well Logging
Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir). Civil Engineering Journal, 3(10):
(2017) 831-840.
[9] R. Irani, and R. Nasimi, Evolving neural network using real coded genetic algorithm for permeability estimation
of the reservoir. Expert Systems with Applications, 38(8): (2011) 9862-9866.
[10] U. Iturrar´an-Viveros and J.O. Parra, Artificial neural networks applied to estimate permeability, porosity and
intrinsic attenuation using seismic attributes and well-log data. Journal of Applied Geophysics, 107: (2014) 45-54.
[11] H. Kaydani, A. Mohebbi, and M. Eftekhari, Permeability estimation in heterogeneous oil reservoirs by multi-gene
genetic programming algorithm. Journal of Petroleum Science and Engineering, 123: (2014) 201-206.
[12] R. Nasimi and R. Irani, Combining a neural network with a genetic algorithm and particle swarm optimization
for permeability estimation of the reservoir. Energy Sources, Part A: Recovery, Utilization, and Environmental
Effects, 37(4): (2015) 384-391.
[13] R. Nasimi, M. Shahbazian and R., Irani, Permeability estimation of the reservoir based on particle swarm optimization coupled with artificial neural networks. Petroleum Science and Technology, 29(22): (2011) 2329-2337.
[14] B. Rafik and B. Kamel, Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria. Egyptian journal of petroleum,
26(3): (2017) 763-778.
[15] B.S. Saljooghi and A. Hezarkhani, Comparison of WAVENET and ANN for predicting the porosity obtained from
well log data. Journal of Petroleum Science and Engineering, 123: (2014) 172-182.[16] B.S. Saljooghi and A. Hezarkhani, A new approach to improve permeability prediction of petroleum reservoirs
using neural network adaptive wavelet (wavenet). Journal of Petroleum Science and Engineering, 133: (2015)
851-861.
[17] A.A. Zerrouki, T. Aifa and K. Baddari, Prediction of natural fracture porosity from well log data by means of
fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria. Journal of Petroleum Science
and Engineering, 115: (2014) 78-89.
[18] B. Zhao, R. Ratnakar, B. Dindoruk and K. Mohanty, A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical Proxy SCAL Data. SPE Journal,(2020).
International Journal of Nonlinear Analysis and Applications
Volume 12, Special Issue
December 2021
Pages 133-145
  • Receive Date: 27 October 2020
  • Revise Date: 26 January 2021
  • Accept Date: 01 February 2021