# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 1 Introduction  
Geophysical well logging is a crucial technical process in oilfield exploration and development, where the accurate calculation of reservoir parameters plays a significant role in reservoir characterization and hydrocarbon resource evaluation. Currently, methods for determining reservoir parameters are mainly classified into direct and indirect measurements. Direct measurement involves laboratory analysis of core samples to obtain rock physical properties, which is the most precise approach. However, due to its high cost and time-consuming nature, it is difficult to implement on a large scale. Indirect methodsinclude conventional well log interpretation techniques and artificial intelligence (AI)-based approaches. Conventional interpretation relies on measuring the physical responses related to reservoir parameters and then deriving them using physical models or empirical formulas. While this method is easy to apply, it has limited adaptability and struggles to meet the demands of complex reservoir environments. With advancements in exploration technologies and the increasing intensity of oilfield development, both direct measurement and conventional interpretation methods are insufficient to meet the efficiency and quality requirements of well logging. Therefore, leveraging advanced AI algorithms to enhance the efficiency and accuracy of well logging data processing and interpretation has become a prominent research focus [1; 2; 3; 4; 5; 6; 7]. In recent years, AI and big data technologies have been widely applied in petroleum exploration and development, achieving remarkable breakthroughs. [PERSON] et al. (2020) [8] proposed a multi-layer long short-term memory (LSTM) network that captures deep sequential dependencies to improve porosity prediction accuracy at significant inflection points. However, it still has limitations when handling discontinuous sequences and highly heterogeneous data. [PERSON] et al. (2022) [9] introduced a reservoir parameter prediction method based on transfer learning, significantly enhancing predictive performance, yet failing to fully address the local optimum problem. [PERSON] et al. (2023) [10] optimized the extreme gradient boosting (XGBoost) algorithm using a grid search (GS) optimization technique, improving the performance of porosity prediction models. However, due to the computational complexity of the grid search, the optimization efficiency and accuracy remained constrained. [PERSON] et al. (2022) [11] developed an XGBoost-based porosity prediction model using hybrid optimization methods, enhancing prediction performance while also suggesting directions for further optimization. [PERSON] et al. (2024) [12] combined deep learning with geological information to improve porosity interpretation accuracy for carbonate reservoirs with complex pore structures, providing valuable references for carbonate reservoir parameter prediction. Despite the progress made in applying AI to porosity prediction, several challenges remain: (1) Some models exhibit poor adaptability to discontinuous sequential data, making it difficult to effectively model the complex heterogeneity of reservoirs. (2) Traditional machine learning methods rely on manually crafted features, limiting their ability to fully exploit the deep information in well logging data. (3) Existing deep learning models mainly focus on either local features or time-series dependencies, lacking effective mechanisms to model global dependencies. Therefore, constructing a deep learning model that integrates local features, sequential information, and global dependencies has become a key research direction for porosity prediction in carbonate reservoirs. In oil and gas exploration and development, studying carbonate reservoir parameters is of paramount importance. Due to influences such as depositional environments, dissolution, and diagenesis, carbonate reservoirs exhibit significant variability in properties such as porosity, lithology, hydrocarbon content, and electrical characteristics.