Fracture enhancement based on artificial ants and fuzzy c-means clustering (FCMC) in Dezful Embayment of Iran

作者: A Naseri , J Mohammadzadeh , M , H Tabatabaei , S

DOI: 10.1088/1742-2132/12/2/227

关键词:

摘要: This paper deals with the application of ant colony algorithm (AC) to a seismic dataset from Dezful Embayment in southwest region Iran. The objective approach is generate an accurate representation faults and discontinuities assist pertinent matters such as well planning field optimization. AC analyzed all spatial attributes which features were extracted. True fault information was detected by many artificial ants, whereas noise remains reflectors eliminated. Furthermore, fracture enhancement procedure conducted three steps on data area. In first step several chaos, variance/coherence dip deviation taken into account; resulting maps indicate high-resolution contrast for variance attribute. Subsequently, performed finally elimination non-faulting events carried out simulating behavior colonies. After considering stepwise attribute optimization, focusing chaos particular, fusion generated used algorithm. map displayed highest performance feature detection along main structural trend, confined NW–SE direction. Thus, optimized might be greater confidence network more accuracy resolution. order assess detection, cross validate reliability method used, fuzzy c-means clustering (FCMC) employed same dataset. Comparing illustrates effectiveness preference due its high resolution compared FCMC method. Accordingly, 3D planes discontinuity determined distribution fractures planning. Results revealed that impedance location probability related area vicinity faults, whilst low probably could zones permeability flow conduits. Analysis under present study suggests orientation magnitude exhibiting trend susceptible stimulation likely open fluid flow.

参考文章(67)
Yasin Hajizadeh, Vasily Demyanov, Linah Mohamed, Mike Christie, Comparison of Evolutionary and Swarm Intelligence Methods for History Matching and Uncertainty Quantification in Petroleum Reservoir Models Intelligent Computational Optimization in Engineering. pp. 209- 240 ,(2011) , 10.1007/978-3-642-21705-0_8
Leonardo Azevedo Guerra Raposo Pereira, Seismic attributes in hydrocarbon reservoirs characterization Universidade de Aveiro. ,(2009)
M. Dorigo, Optimization, Learning and Natural Algorithms Ph.D. Thesis, Politecnico di Milano, Italy. ,(1992)
S.I. Pedersen, T. Randen, L. Sonneland, O. Steen, Automatic 3D Fault Interpretation by Artificial Ants 64th EAGE Conference & Exhibition. ,(2002) , 10.3997/2214-4609-PDB.5.G037
Oliver Korb, Thomas Stützle, Thomas E. Exner, PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design Ant Colony Optimization and Swarm Intelligence. pp. 247- 258 ,(2006) , 10.1007/11839088_22
Michael Spann, Jonathan Turner, David Gibson, Automatic Fault Detection for 3D Seismic Data digital image computing: techniques and applications. pp. 821- 830 ,(2003)
Ou Chenghua, Wei Chen, Zhonggao Ma, Quantitative identification and analysis of sub-seismic extensional structure system: technique schemes and processes Journal of Geophysics and Engineering. ,vol. 12, pp. 502- 514 ,(2015) , 10.1088/1742-2132/12/3/502
Fatemeh Razavi, Farhang Jalali-Farahani, Ant Colony Optimization: A Leading Algorithm in Future Optimization of Petroleum Engineering Processes Artificial Intelligence and Soft Computing – ICAISC 2008. pp. 469- 478 ,(2006) , 10.1007/978-3-540-69731-2_46
Nasir M. Rajpoot, Usman Ali, Arshad Hussain, Mahmud Qureshi, Kashif Saleem, A novel image coding algorithm using ant colony system vector quantization ,(2004)
Thomas Stützle, Marco Dorigo, ACO algorithms for the quadratic assignment problem New ideas in optimization. pp. 33- 50 ,(1999)