Journal of Jilin University(Earth Science Edition) ›› 2015, Vol. 45 ›› Issue (3): 926-933.doi: 10.13278/j.cnki.jjuese.201503301

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Gravity Anomaly Extraction for Hydrocarbon Based on Particle Swarm Optimization and Cellular Neural Network

Du Runlin1,2, Liu Zhan1   

  1. 1. School of Geosciences, China University of Petroleum(East China), Qingdao 266555, Shandong, China;
    2. The Key Laboratory of Marine Hydrocarbon Resources and Environment Geology, Qingdao Institute of Marine Geology, Qingdao 266071, Shandong, China
  • Received:2014-08-15 Published:2015-05-26

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Abstract:

Cellular neural network is an effective method for extracting useful anomalies with a strong capability of horizontal resolution. Its key point is to optimize the template when using cellular neural network to extract hydrocarbon gravity anomaly information. We presented an improved algorithm based on the characteristics of the cellular neural network and the particle swarm optimization, joined constraint condition in template, and introduced the shrinkage factors in particle swarm optimization individual characteristics through the analysis of the dynamic performance of cellular neural network. Through the model testing and the actual data processing, the algorithm proved to be more stable to optimize the template for extracting reservoir gravity anomaly information.

Key words: cellular neural network, particle swarm optimization, gravity anomaly, shrinkage factors, petroleum gas

CLC Number: 

  • P631.1

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