# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 5 Conclusions  
This study proposes a deep learning model integrating convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and Transformer for predicting carbonate reservoir porosity. A comparative analysis with XGBoost, LSTM, BiLSTM, CNN-BiLSTM, and BiLSTM-Transformer models yields the following conclusions:  
The strong heterogeneity of carbonate reservoirs results in wide-ranging and unevenly distributed logging data. To address this, the proposed approach employs data preprocessing, sensitivity parameter analysis, and feature extraction, effectively mitigating heterogeneity and enhancing data quality and model stability.  
Ablation experiments demonstrate that CNN's feature extraction capability and Transformer's global dependency modeling significantly improve prediction performance. This hybrid model surpasses traditional machine learning methods and single deep learning architectures in terms of prediction accuracy, generalization ability, and stability.  
Compared to existing studies, this model reduces RMSE and MAE by 0.016 and 0.014, respectively, while improving R\({}^{2}\) by 0.23, demonstrating superior predictive performance. Most previous studies have relied on either single deep learning models or traditional machine learning techniques, with limited exploration of hybrid frameworks combining CNN, BiLSTM, and Transformer. This study confirms the advantages of integrating local feature extraction, sequential modeling, and global dependency capturing, offering a novel approach for predicting porosity in heterogeneous carbonate reservoirs.  
The primary contribution of this study is the introduction of an efficient prediction method tailored for highly heterogeneous reservoirs, providing new insights into accurate porosity evaluation. However, the model's reliance on high-quality data and substantial computational resources presents challenges. Its applicability in regions with sparse or low-quality data remains to be further validated. Future research should explore the integration of prior geological knowledge, multi-scale data fusion, and seismic data to enhance model interpretability and applicability. Additionally, further studies should assess the model's performance across different geological settings to establish its robustness and generalization capabilities.  
Conceptualization, S.L. ([PERSON]) and S.T.; methodology, Y.Q.; software, Y.Q.; validation, J.R.; formal analysis, D.G.; investigation, Y.Q.; resources, Q.L.; data curation, S.L. ([PERSON]); writing--original draft preparation, Y.Q.; writing--review and editing, S.L. (ShuliiangLuo); visualization, Y.Q.; supervision, S.L. ([PERSON]); funding acquisition, D.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Youth Fund Project \"Sedimentary evolution of Late Ordovician carbonate platform margin in Tazhong area and its response to paleostructure and sea level change (Project No.41502104)\".  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
Authors [PERSON], [PERSON] were employed by PetroChina Southwest Oil & Gas Field Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Acoustic time difference logging \\ CAL & Caliper logging \\ CNL & Compensated neutron logging \\ DEN & Density logging \\ GR & Gamma ray logging \\ RT & True formation resistivity logging \\ Rxo & Flushed zone formation resistivity logging \\ POR & Measured Porosity \\ XGBoost & Extreme gradient boosting \\ CNN & Convolutional neural network \\ LSTM & Long short-term neural network \\ BiLSTM & Bidirectional long short-term memory \\ \end{tabular}