The Application of Dual-Branch Multi-Layer Perceptron Intelligent Algorithm in the Prediction of Sweet Spots in Tight Gas Exploration and Development

Due to the complex issues of low porosity and low permeability in tight sandstone reservoirs, non-unified data measurement, and the limitation of traditional methods by empirical formulas and simple statistical models, which make it difficult to couple the correlation of parameters, how to quickly clean data, establish a comprehensive geological-engineering sweet spot evaluation method, and improve prediction accuracy and engineering decision-making effectiveness have become an urgent technical challenge. This study takes the logging and fracturing construction data in the L area as the data set, uses the Pearson correlation coefficient method to verify the nonlinear characteristics of features, and constructs a geological-engineering integrated intelligent decision-making algorithm based on the collaborative optimization of a dual-branch multi-layer perceptron and attention mechanism. The training results of the dual-branch multi-layer perceptron model and traditional machine learning methods are compared and analyzed. The results show that the prediction error of the adopted dual-branch multi-layer perceptron neural network model is 5.44%. The weight of geological factors in this area accounts for 51.71%, and the engineering factors account for 48.29%. This method has been field-applied in 25 wells in the L area, with a production coincidence rate reaching 94.66%. The sweet spots of tight sandstone reservoirs are mainly the H5 and H6 submembers. The deep integration of machine learning interpretability and geological engineering practice provides a new approach for sweet spot prediction.