Following parts and conclusions

Well Log Data Ming

GMM assumes that one well log matrix is one mixture model, which is a probabilistic model. This method transforms well log data into Gaussian distribution, with the parameters . The objective function of EM algorithm is the likelihood of the GGM. The likelihood maximization is shown as follows:

EM can give a relatively accurate approximation after iterations.

The figure of GMM clustering results is as follows:

Well Correlation

It enables people to do well correlation by generating electrofacies after applying model-based clustering. Then the paper determines the size of the sample and select sample points with systematic sampling method.

Discriminant analysis (DA) it is a classification method where clusters from populations are known to be a priori, assuming that different classes generate data based on different Gaussian distributions.

To facilitate future improvement, we can use core analysis to calibrate well correlation results.

Well Log Data Postprocessing

Artificial neural network (ANN)

It is a dynamic computation system which is capable of extracting and recognizing the underlying dominant patterns among data, classifying new patterns and generalizing an output based on the learned data.

Feedforward back-propagation is a common scheme for training the network.

The figure of pairwise well prediction results is as follows:

Conclusions

The system consists of three components: data preprocessing, data mining and data postprocessing. The data are normalized, reduced in dimension, modeled and selected, then correlated, finally predicted and generated.

(Some of the equations cannot be shown in the Blogger. Tomorrow, I plan to read the paper again and find something useful for pseudo well log.)