This paper established a numerical simulation model to analyze the pressure transient and rate transient behaviors in reservoir with complex fracture network. Firstly, the fractures are introduced into the coordinate system through the position, angle, and length. Secondly, a mathematical model is established by using unstable seepage model. Thirdly, the central difference method was used to solve the model and local grid refinement method is introduced to describe the network fractures. Finally, we compared the results obtained from this paper’s model with the production data. The results show acceptable and reasonable matches for typical well. Meanwhile, the sensitivity of two properties is discussed. The model solution is verified with an analytical method thoroughly. The novelty of this paper is to introduce each fracture in fracture network into the coordinate system. Then, the grid refinement is achieved according to the fracture information. The presented new model simplifies the analysis of the pressure transient and rate transient of the reservoir with complex fracture network, and it is more efficient than the conventional numerical method. Compared with the analytical methods, the new model describes the fractures system in more detail. However, the new model treats fractures as reservoirs with higher permeability in the central difference method, which is simpler and rougher than traditional numerical methods.

Selection of well and reservoir is an important step in the process of stimulation and transformation of oil fields. Good measures can effectively save the cost in the process of oil field development and greatly increase the production of oil fields. Aiming at the problem of well and reservoir selection in petroleum engineering, a method of oil well production prediction based on radial basis function network is proposed in this paper. According to the field data of Xinjiang oilfield, the main controlling factors with greater influence are selected by correlation analysis after data pretreatment. Then we randomly divide the data into training data set and prediction data set, and use the training data set to create a radial basis function network. Finally, we use the radial basis function network to predict the prediction data set, and the final prediction accuracy reaches 80%.

The cost of oil field drilling engineering is very high. Effective well selection and formation selection will avoid waste of energy and resources. Aiming at the problem of well selection and reservoir selection in oilfield, this paper presents a clustering discriminant prediction method for oil well production based on Mahalanobis distance. Based on the field data of Xinjiang oilfield, the main control factors with greater influence are selected after data pretreatment in this paper. Then we randomly divide the data into training data set and prediction data set, and establish a discriminant model of oil well classification based on Mahalanobis distance. Finally, the model is used to predict the production of oil wells in the forecasting data set, and the prediction accuracy reaches 87.5%.

After hydraulic fracturing, some treatments intended for production enhancement fail to yield predetermined effects. The main reason is the insufficient research about the fracture propagation mechanism. There is compelling evidence that I-shaped fracture, two horizontal fractures at the junction of coalbed and cover/bottom layer, and one vertical fracture in the coalbed have formed in part of the coalbed after hydraulic fracturing. Therefore, this paper aims at I-shaped fracture propagation simulation. A novel propagation model is derived on the basis of a three-dimensional (3D) model, and the coupling conditions of vertical fracture and horizontal fractures are established based on the flow rate distribution and the bottom-hole pressure equality, respectively. Moreover, an associated PDA (pressure decline analysis of post-fracturing) model is established. Both models complement with each other and work together to guide fracturing treatment. Finally, a field case is studied to show that the proposed models can effectively investigate and simulate fracture initiation/propagation and pressure decline.

Deep coalbed methane reservoirs are characterized by complex geological conditions, strong heterogeneity, and significant variations in in situ stress, posing challenges for accurate three-dimensional in situ stress prediction. To address the issues of strong dependence on rock mechanical parameters in traditional physical models, as well as the lack of physical constraints and poor generalization capability under small-sample conditions in purely data-driven methods, this paper proposes a LightGBM prediction model that integrates physical information and data clustering. A total of 1289 fracturing clusters in the DJ block are selected as the research objects. First, the K-means algorithm is used to divide the reservoir into three categories to reduce the impact of heterogeneity. Then, a LightGBM model is constructed for each category, and physical constraints based on Huang’s model and stress–gravity equilibrium are incorporated into the loss function to ensure that the prediction results conform to mechanical laws. Taking the fracturing clusters in Category I as an example, the proposed model achieves an MAPE of 2.78% and an R² of 0.89 on the test set. Comparative experiments show that the proposed model outperforms BP neural networks, random forests, and Transformers in prediction accuracy. Ablation experiments verify the independent contributions and synergistic effects of the clustering module and the physical information constraints. Transfer experiments demonstrate that the model has good applicability to blocks with similar geological conditions. This study provides an effective method for predicting in situ stress in deep coalbed methane reservoirs, balancing accuracy and physical interpretability.

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the proposed model achieves a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 2.38 MPa, and a mean absolute percentage error (MAPE) of 2.02% on the test set, with prediction accuracy significantly outperforming benchmark models such as BPNN, XGBoost, and LSTM. Ablation experiments verify that both the SOM clustering and GAN augmentation modules effectively enhance model performance. Analysis of acid treatment parameters indicates that hydrofluoric acid (HF) concentration is the dominant factor influencing fracture pressure reduction, and the mud acid system exhibits a stronger synergistic effect compared to the single hydrochloric acid system. Reasonable optimization of acid concentration and dosage can significantly reduce fracture pressure (3.14–5.28 MPa). This method provides a theoretical basis and engineering guidance for accurate fracture pressure prediction and optimal design of acid pretreatment in tight conglomerate reservoirs.