In order to explore the feasibility of the Computational Fluid Dynamics (CFD) method for plunger gas lift and the flow pattern in a long wellbore, a lab simulation test was conducted to verify the reliability of the CFD simulation results. Verification data were provided for CFD simulation through carrying out a plunger gas lift lab test for vertical wells, identifying the plunger movement patterns and delivery rates under different flow pressures, and determining the minimum plunger startup pressure or differential pressure and leakage, and an identical CFD physical model was created on the basis of the lab test model, CFD dynamic grid programming and CFD simulation were conducted under test conditions, and a comparison of the simulation and test results was made to identify the calculation accuracy and the rationality of the CFD model and method; finally, boundary conditions such as temperature and pressure were set according to the actual long wellbore (200 m) conditions, the CFD simulation was performed, and the impacts of the downhole conditions on the gas lift performance were analyzed, so as to develop CFD calculation methods to predict the bottom-hole flow pressure, plunger speed, and delivery rate. The results show: the average plunger speed range is 7.74–22.5 m/s when the flow pressure varies from 199.77 to 632.93 kPa, and the leakage rate increases in a nearly linear way with the speed; compared with the lab test results, the simulation results from the created dynamic grid model and multi-phase turbulent flow model have the leakage error of 7.2% and the plunger speed average error is smaller than 11.1%; under long-wellbore conditions, the plunger lift speed shows the change pattern of increasing and then decreasing, the wellbore pressure has a wave-like drop, and in addition to this pressure drop characteristic, the fact that the plunger startup pressure differential increases with the wellhead pressure should be considered (when the bottom-hole pressure is 15 MPa, the wellhead pressure must not exceed 10 MPa).

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology transforms one-dimensional time-series pressure data into two-dimensional image representations using the element-wise summation of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), which simultaneously preserves global operational trends and local transient dynamics for vision model analysis. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT + GAF + BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score, with an F1-score of 0.93. Visualizations using t-SNE confirm the ViT’s superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems.

During the natural-energy production stage of shale gas wells without external energy supply, the utilization efficiency of formation energy directly determines a single well’s cumulative gas production and free-flowing life. Using the maximization of natural-energy utilization efficiency as the criterion, this paper establishes an integrated reservoir–wellbore–process energy model to determine the intervention timing and production allocation of the velocity string and foam drainage processes, and to predict the cumulative gas production and free-flowing life after optimization. The model couples three parts: on the reservoir side, based on Tan fractal theory, it accounts for matrix stress sensitivity, Knudsen diffusion, Klinkenberg slippage, and Langmuir adsorption–desorption; on the fracture side, it defines a non-uniform conductivity distribution function along the horizontal section, which agrees with field PLT measurements; on the wellbore side, it establishes a variable-mass-flow pressure-drop equation accounting for density variation. Application to Well W1 in the Sichuan Basin shows that the optimized scheme improves natural-energy utilization efficiency by reducing wellbore energy loss, thereby prolonging the effective production period and increasing cumulative gas production during the natural-energy production stage.