TY - GEN

T1 - Well log data inversion using radial basis function network

AU - Huang, Kou-Yuan

AU - Shen, Liang Chi

AU - Weng, Li Sheng

PY - 2011/11/16

Y1 - 2011/11/16

N2 - We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original features are the network input for training process. According to our experimental results, the three-layer radial basis function can get smaller error between the desired output and the actual output. The network with 10 input features, first layer with 27 nodes, second layer with 10 hidden node, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. After simulation, we apply the network to the real field data. The result is good. It shows that the RBF can do the well log data inversion.

AB - We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original features are the network input for training process. According to our experimental results, the three-layer radial basis function can get smaller error between the desired output and the actual output. The network with 10 input features, first layer with 27 nodes, second layer with 10 hidden node, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. After simulation, we apply the network to the real field data. The result is good. It shows that the RBF can do the well log data inversion.

KW - multilayer perceptron

KW - Radial basis function network

KW - well log inversion

UR - http://www.scopus.com/inward/record.url?scp=80955141845&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2011.6050217

DO - 10.1109/IGARSS.2011.6050217

M3 - Conference contribution

AN - SCOPUS:80955141845

SN - 9781457710056

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4439

EP - 4442

BT - 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings

T2 - 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011

Y2 - 24 July 2011 through 29 July 2011

ER -