A novel approach to estimate reservoir permeability using machine learning method

Reservoir permeability in upstream of petroleum engineering plays an essential role in crude oil production. Due to the high cost and difficulty in direct measurement of the permeability, having a robust model of this parameter based on the openhole logs and data is preferred. The sonic, volumetric density, gamma ray, total porosity, neutron porosity logs are available in the time of logging and have the highest correlation with reservoir permeability characteristics. To estimate the permeability of the reservoir based on these available data, a new intelligent method of Genetic Algorithm (GA) and Wavelet Neural Network (WNN) is derived. In the developed model, a new objective function has been introduced. For avoiding more complexity of the objective function, the initializing weights of neural network has been done by GA. Then, the training levenberg marquardt algorithm is utilized to update the optimal weighting. In other words, wavelet as activation function of neural network enhances exploitation search abilities of the algorithm and leads to a robust model. In the following, a sample reservoir as a source of data in this field is selected to evaluate the effectiveness of the proposed algorithm in the permeability estimation. For the sake of comparison, two algorithms of BP-ANN and GA-BP, which have been already presented in the literature, are applied for the same data sets and the superiority of developed model in estimation has been illustrated.