### Abstract

Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C _{a}) and the true formation conductivity (C _{t}). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured C _{a} and the desired output is the C _{t}. MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then C _{t}' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can work on the well log data inversion.

Original language | English |
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Title of host publication | 2012 International Joint Conference on Neural Networks, IJCNN 2012 |

DOIs | |

State | Published - 22 Aug 2012 |

Event | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012 |

### Publication series

Name | Proceedings of the International Joint Conference on Neural Networks |
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### Conference

Conference | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 |
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Country | Australia |

City | Brisbane, QLD |

Period | 10/06/12 → 15/06/12 |

### Keywords

- apparent conductivity (C )
- multilayer perceptron (MLP)
- particle swarm optimization with mutation (MPSO)
- true formation conductivity (C )

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## Cite this

*2012 International Joint Conference on Neural Networks, IJCNN 2012*[6252707] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2012.6252707