Research on intelligent fracturing parameter optimization method based on deep learning and construction curve feature extraction - Qiao, Xin; Ren, Dengfeng; Liu, Ju; Wang, Chengwen; Peng, Fen; Zhong, Bowen; Peng, Fei; Feng, Qi; Huang, Cheng (Frontiers in Earth Science, 2025)
IntroductionOptimizing fracturing parameters under multi-factor, complex conditions remains challenging in low-permeability reservoirs.MethodsWe extract stage-aware construction-curve features, compute composite correlations (Pearson, Kendall’s Tau, Random Forest), train an SSA-BP surrogate to predict open flow capacity (OFC), and apply a GA to optimize fluid volume, pump rate, and proppant concentration.ResultsTwenty key factors were retained. Among four regressors, SSA-BP performed best (highest R², lowest MSE). GA-optimized parameters improved OFC in multiple wells; a field application (Well A-X) showed increased daily gas and OFC after adjustments.DiscussionThe integrated feature-extraction + SSA-BP + GA workflow provides accurate OFC prediction and practical parameter optimization. Limitations include single-field data (∼70 wells) and SSA-BP computational cost; future work will expand datasets and explore lighter models.