Application of ANN for well logs

Today, I read the paper ‘Application of ANN for well logs’ and know a little more about ANN process.

Summary

Architecture of an ANN includes a large number of neurons organized in different layers, with the neurons of one layer connected to neurons of another layer by means of adjusting weights.

It begins with randomly generated weights and the iterations are continued till the goal, which is to adjust them so that the error is minimal, is achieved.

The activation function of the input layer is tansig, which convolutes the input layer neuron weights with the input data. This is passed to the hidden layer where the product of weights and input from previous layer is integrated with the activating function purelin. Subsequently, the value is passed to the output layer consisting of a single neuron.

Using backpropagation learning algorithm, the network iterates and updates the weights of the input, output and hidden layer neuron. Iteration continues until the target error goal is reached.

Backpropagation learning algorithm:

1.     Levenberg-Marquardt training algorithm

2.     Error calculated uses Mean Squared Algorithm

One common problem during training is data over-fitting. To overcome the problem and prevent the network to memorize the examples, training data set is divided into three subsets: training set, validation set, test set.

This technique is completely data driven and does not require any prior assumptions.

LMA:

Like other numeric minimization algorithms, the Levenberg–Marquardt algorithm is an iterative procedure. To start a minimization, the user has to provide an initial guess for the parameter vector, β. In cases with only one minimum, an uninformed standard guess like  will work fine; in cases with multiple minima, the algorithm converges to the global minimum only if the initial guess is already somewhat close to the final solution.

The results of cases' comparison is shown below:

The improvements of the paper may be done:

1.     Use more wells’ data and give more cases to prove.

2.     The LMA finds only a local minimum, so maybe there are other algorithms for better results.

(The LMA interpolates between the Gauss-Newton algorithm (GNA) and the method of gradient descent. It is more robust than the GNA.)

Tomorrow, I will read more papers and books on methods for pseudo NMR.