A Method for Prediction of In-situ Stress Based on Empirical Formula and BP Neural Network

To solve the problems of complex in-situ stress of tight sandstone reservoir, few sample points of experimental data, difficulty in in-situ stress prediction, etc., a method for one-dimensional, two-dimensional and three-dimensional in-situ stress prediction based on geomechanics and BP neural network was innovatively proposed by comprehensively using various data such as core data, mechanical experimental data, logging data, etc. In this method, the rock mechanics parameters of single well in the study area were predicted by neural network method using the logging data as the learning sample and measured rock physical parameters as the monitoring data first; then the in-situ stress of single well was accordingly calculated by empirical formula, and predicted and analyzed by neural network algorithm using the calculated in-situ stress of single well selected by error analysis and the indoor measured in-situ stress as the monitoring data and the conventional logging data as the learning samples. The application in the actual areas shows that the predicted results of in-situ stress not only conform to the measured data, but also follow the logging curves, and thus provide an important basis for the design of integrated geological engineering scheme.