An Evolving Neural Network Using an Ant Colony Algorithm for a Permeability Estimation of the Reservoir

Abstract An ant colony optimization algorithm (ACA) has the powerful ability to search for a globally optimal solution, and the back-propagation (BP) algorithm features rapid convergence on local optima. A proper hybrid of the two algorithms (ACA-BP) may accelerate the evolution of neural networks and improve their forecasting precision. An ACA-BP scheme adopts an ACA to search for the optimal combination of weights in the solution space and then uses a BP algorithm to obtain the accurate optimal solution quickly. The ACA-BP and BP algorithms were applied to predict the permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran, utilizing available geophysical well log data. Experimental results showed that the proposed ACA-BP scheme was more efficient and effective than the BP algorithm.

[1]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[2]  Shahab D. Mohaghegh,et al.  State-Of-The-Art in Permeability Determination From Well Log Data: Part 2- Verifiable, Accurate Permeability Predictions, the Touch-Stone of All Models , 1995 .

[3]  R. Nasimi,et al.  Permeability Estimation of a Reservoir Based on Neural Networks Coupled with Genetic Algorithms , 2011 .

[4]  Marcus Randall,et al.  Anti-pheromone as a Tool for Better Exploration of Search Space , 2002, Ant Algorithms.

[5]  J. Wiener,et al.  Predict permeability from wireline logs using neural networks , 1995 .

[6]  H. I. Bilgesu,et al.  Improving the Simulation of Waterflood Performance With the Use of Neural Networks , 2000 .

[7]  R. Nasimi,et al.  Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling , 2011 .

[8]  Luca Maria Gambardella,et al.  Solving symmetric and asymmetric TSPs by ant colonies , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[10]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[11]  Sungzoon Cho,et al.  Multiple permeability predictions using an observational learning algorithm , 2000 .

[12]  Shahab D. Mohaghegh,et al.  Design and Development of An Artificial Neural Network for Estimation of Formation Permeability , 1995 .

[13]  Rasoul Irani,et al.  Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir , 2011, Expert Syst. Appl..

[14]  R. K. Dube,et al.  Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network , 2007, Journal of Materials Engineering and Performance.

[15]  Paulin Coulibaly,et al.  Nonstationary hydrological time series forecasting using nonlinear dynamic methods , 2005 .

[16]  Walter J. Gutjahr,et al.  A Graph-based Ant System and its convergence , 2000, Future Gener. Comput. Syst..

[17]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  J. Zhong,et al.  Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel , 2008 .

[19]  Morteza Ahmadi,et al.  Design of neural networks using genetic algorithm for the permeability estimation of the reservoir , 2007 .

[20]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[21]  H. K. D. H. Bhadeshia,et al.  Neural Networks in Materials Science , 1999 .

[22]  R Nasimi,et al.  Notice of Violation of IEEE Publication PrinciplesA hybrid particle swarm optimization–neural network strategy for permeability estimation of the reservoir , 2010, SPEEDAM 2010.