Permeability Prediction of Tight Sandstone Reservoirs Using Improved BP Neural Network

Abstract

By analyzing the permeability controlling factors of tight sandstone reservoir in Wuhaozhuang Oil Field, the permeability is considered to be mainly controlled by porosity, clay content, irreducible water saturation and diagenetic coefficient. Because the conventional BP algorithm has its drawbacks such as slow convergence speed and easy falling into the local minimum value, an improved three-layer feed-forward BP neural network model is built by MATLAB neural network toolbox to predict permeability according to the four permeability controlling factors, while studying samples of model are selected based on the representative core analysis data. The simulation based on improved neural network model shows that the improved model has a faster convergence speed and better accuracy. The consistency between model prediction value and lab test value is good and the mean squared error is less. Therefore, the new model can meet the needs of the development geology research of oil field better in the future.

A train F-TR lock anti-lifting detection method based on improved BP neural network

“…As the research on artificial neural networks becomes mature, Artificial neural networks have been widely applicated in various industries. It is able for artificial neural network to approximate complex nonlinear mappings with arbitrary precision [16]. This provides an excellent solution for problems with low accuracy in indirect measurements.…”