Neural network using Bayesian algorithm for estimation of petroleum reservoir

This paper is concerned with the estimation of reservoir parameters in the presence of noise and outliers using neural network (NN) and Bayesian algorithm. The paper aims to discuss these issues.

Outlier detection is of great importance to prediction of time series data. A reliable predictive methodology is proposed based on NN and Bayesian algorithm to efficiency estimates of the parameters of a petroleum reservoir. This strategy is applied to estimate the parameters of Marun reservoir located in Ahwaz, Iran utilizing available geophysical well log data.

For an evaluation purpose, the performance and generalization capabilities of Bayes-ANN are compared with the common technique of back propagation (BP).

The experimental results demonstrate that the proposed hybrid Bayes-NN algorithm is able to reveal a better performance than conventional BP NN algorithms.

Helped oil and gas companies to estimation of petroleum reservoir parameters more accurate than other methods in the presence of noise and outliers.