Addressing the challenges of high-dimensional redundancy, noise interference, and parameter missing in the net present value prediction of shale gas wells, an intelligent prediction model PCA-DFNN integrating Principal Component Analysis and Deep Feedforward Neural Network is proposed. Based on actual data from 48 shale gas wells, Principal Component Analysis is first performed on 19 input features to reduce dimensionality, extracting 9 core principal components, which achieve a cumulative variance contribution rate of 88.05%. Subsequently, a deep neural network model is constructed for comparative modeling. The results indicate that the PCA-DFNN model achieves a coefficient of determination on the independent test set that improves from 0.6439 in the original model to 0.6882, an increase of 0.0443, or approximately 6.9%, with faster training convergence and superior generalization ability. The research confirms that the proposed method can effectively eliminate feature redundancy, filter noise, and circumvent the uncertainty of missing value imputation, providing a more reliable technical tool for the early economic evaluation of shale gas.
Su et al. (2026) studied this question.