Shale reservoirs are characterized by extremely low porosity and permeability, poor connectivity, and high content of clay minerals. This leads to the reservoir being vulnerable to imbibition damage caused by foaming agent solutions during foam drainage gas recovery. It results in the decrease of reservoir permeability and the reduction of gas well production and ultimate recovery. Therefore, as the most commonly used foam drainage gas production, it is particularly important. This study is structured as follows. First, we analyze and evaluate the characteristics of shale reservoirs within the target area, and that of mineral composition and microscopic pore throat structures. Second, we study foaming agent types and two types are selected to be applied in subsequent sensitivity tests. Simultaneously, the nuclear magnetic resonance (NMR) method was used to study the microscopic characteristics of reservoir damage and imbibition damage of shale, caused by the impact of foaming agent solutions during the foam drainage and gas recovery processes. Finally, it is concluded that the degree of damage to the core permeability is minimized when the concentration of foaming agents is 0.4–0.6%. A trend has been established for increased damage to the cores with increasing exposure time. Additionally, this study provides technical guidance for damage evaluation and reservoir protection in shale reservoir exploitation.

Foam drainage gas production is a gas production technology for low-pressure gas well in domestic and abroad industries. In Qinghai Oilfield’s Sebei Gasfield, sand existence often occurs during drainage production. In order to reveal the impacts of formation sands on drainage gas production, a lab test was conducted to make a study on foam performances of different foam drainage agents in case of sand existence. A laboratory test was employed to study the foam performances of different foam drainage agents under varies scenes of sand existence, to reveal the impacts of formation sands during foam drainage gas recovery. In this study, four foam drainage agents, i.e., OP-10, Sodium dodecyl benzene sulfonate, Hexadecyl Trimethyl Ammonium Bromide (CTAB), and Lauramidopropyl Hydroxy Sulfobetaine, were selected to compare with the field compound foam drainage agent QH-X1. Following patterns can be revealed by macroscopic and microscopic experimental results: Foam formed by an ionic-type foam drainage agent is more stable when the hydrophilic sand particles are smaller (which means they have larger specific surface areas and the adsorption energy levels between the sand and bubbles are higher); while foam formed by non-ionic foam drainage agent OP-10 has a short stable period because its polarity is weaker than that of ionic-type agent foam and the adsorbability of the foam on the hydrophilic particles is weak; under different sand conditions, the impacts of different foam drainage agent types on the average bubble area, bubble wall thickness, and Plateau Boundary are markedly different, and appear decreasing tendency with anionic foam drainage agents, is the trend can be more obvious when the sand particles are coarser. The study on QH-X1 shows that the foam parameters are generally lower as the sand particles are smaller, but the changing trends can be different after sand is involved. Particles constitute an adsorption film at the gas-liquid interface and a laminar structure within each interbubble thin liquid film, thus improving bubble coalescence and unloading stability. So, the inflow of the hydrophilic sands increases the stiffness, half-life period, and comprehensive rate of the QH-X1-induced foam.

To address the liquid-loading problem at the bottom of deep gas-reservoir wells, a gas-driven jet pump for downhole deliquification was designed, and its key geometric parameters were optimized. Using high-pressure natural gas as the motive fluid, the device achieves stable liquid lifting and continuous drainage through efficient gas–liquid mixing. In this study, the internal flow characteristics of the gas jet pump were numerically investigated using CFD simulations within a steady VOF-RSM multiphase turbulence framework. Published experimental data for a prototype from the open literature were used to validate the numerical model, and the maximum relative deviation between simulation and measurement was 17.5%, satisfying the accuracy requirements for engineering CFD analysis. The effects of three key structural parameters—area ratio, nozzle-to-throat distance, and throat length-to-diameter ratio—on the liquid production rate and pumping efficiency were systematically examined through single-factor sensitivity analysis combined with an orthogonal experimental design. Range analysis showed the following order of influence: area ratio > throat length-to-diameter ratio > nozzle-to-throat distance. The optimal parameter combination was determined as an area ratio of 0.0494, a throat length-to-diameter ratio of 5, and a nozzle-to-throat distance of 7.2 mm. Under this optimized configuration, the maximum liquid production rate reaches 80 m³/d. To achieve a target liquid production rate of 20 m³/d, the required gas injection rate is reduced by 33%, and a peak pumping efficiency of 7.2% is achieved at gas injection rates between 2000 and 3000 m³/d.

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.