### Abstract

We adopt the radial basis function network RBF for well log data inversion. We propose the 3 layers RBF. Inside RBF, the first layer is the K-means clustering method and PFS-test. Then the 1-layer perceptron is replaced by 2-layer perceptron. It can do more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at 2-layer perceptron. The input of the network is the apparent conductivity Ca and the output of the network is the true formation conductivity Ct. 25 simulated well log data are used in the training. From experimental results, the network with 10 input data, first layer with 27 nodes, second layer with 9 hidden nodes and 10 output nodes can get the smallest average mean absolute error in the training. After training in the network, we apply it to do the inversion of the 6 simulated well log data and 1 real field well log data to get the inverted Ct. Result is good. It shows that the RBF can do the well log data inversion.

Original language | English |
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Pages (from-to) | 499-503 |

Number of pages | 5 |

Journal | SEG Technical Program Expanded Abstracts |

Volume | 30 |

Issue number | 1 |

DOIs | |

State | Published - 1 Jan 2011 |

### Keywords

- Apparent resistivity
- Borehole geophysics
- Inversion
- Neural networks

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## Cite this

*SEG Technical Program Expanded Abstracts*,

*30*(1), 499-503. https://doi.org/10.1190/1.3628131