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ABSTRACT As a common method for liquid drainage in wave‐shaped pipelines, foam drainage has been used in gas‐gathering pipelines in various oil and gas fields. One of the common problems encountered in wave‐shaped foam drainage pipelines is the inaccurate prediction of the liquid retention rate. This issue makes it difficult to predict liquid accumulation points, which affects gas output efficiency, causes pipeline corrosion, and generates natural gas hydrates. To clarify the liquid‐holding rate law of wavy foam drainage pipelines is studied. In this study, through experiments to verify the accuracy of the numerical simulation method, we investigate the easiest liquid accumulation points and the liquid‐holding rate of wave‐shaped foam drainage pipelines, with factors such as inlet gas velocity, inlet liquid velocity, import and export differential pressure, undulation angle, and inlet temperature change rule. Among them, the error between the numerical simulation results and the experimental results is 4.68%. Finally, after considering the influence of the aforementioned factors on the liquid retention rate of wave‐shaped pipelines, a new liquid retention rate calculation model for wave‐shaped pipelines with foam drainage is established by introducing dimensionless numbers, such as gas‐phase velocity coefficient, liquid‐phase velocity coefficient, and angle correction coefficient. The method is compared with the results of previous research, and the error is within 15%. The model has a simple form and high calculation accuracy, providing a theoretical basis for pipeline inspectors to predict liquid accumulation conditions and reasonably adjust production schedules.

Stress interference is the main factor affecting hydraulic fracture propagation during multi-well hydraulic fracturing; stress interference is influenced by fracture bending, fracture hits, and asymmetric fracture propagation. To investigate the role of stress interferences among hydraulic fractures with nonuniform stress distribution in an inhomogeneous formation, a hydromechanical coupling extended finite element method was adopted to investigate the fracturing paths that occurred during the first fracturing–fracturing fluid flowback–repeat fracturing process; the asymmetric fracturing that occurred at different child well locations was also studied. The results showed that the area affected by fracturing-induced stress formed a "butterfly type" area. For child wells located within the zone, stress interference resulted in asymmetric fracture propagation; meanwhile, for child wells located outside this zone, stress interference resulted in symmetric fracture geometry. The effect of stress interference on the asymmetry of child well fracture wings was found to be negatively correlated with the distance between the parent well and the child well. [ABSTRACT FROM AUTHOR]

Natural gas foam flooding has good fluid control and formation blockage capabilities, but the high-temperature, high-pressure, and high-salinity environment puts forward higher requirements for the comprehensive performance of natural gas foam. The impact of different gas types on the overall performance of the foam system was evaluated via a high-temperature, high-pressure visual foam stirrer. It is found that nitrogen foam systems have strong stability but weak foaming ability, while carbon dioxide foam systems exhibit strong foaming ability but poor stability. The methane foam system showed relatively better and balanced foaming ability and stability, making it more suitable to oil fields. Ten surfactants were screened and the results indicated that α-olefin sodium sulfonate (AOS14, AOS12) and sodium dodecyl sulfate formed natural gas foam systems with better overall performance with methane. The effects of temperature, pressure, surfactant concentration, and foam stabilizers on the comprehensive performance of the methane-AOS14 foam system were investigated. With increase in the surfactant concentration, the foaming ability and stability of the methane-AOS14 foam system first increase and then decrease. Higher temperatures reduced the viscosity of the surfactant solution and decreased the interfacial tension between solution molecules, weakening the overall performance. Increased pressure facilitated the generation of denser foam with smaller diameters, slowing the liquid loss from foam films and enhancing the overall performance. Under high-temperature, high-pressure, and high-salinity conditions, polymer-based foam stabilizers enhanced the stability of the methane-AOS14 foam system by forming a spatial network structure on the foam film, with higher molecular weight yielding better effects. [ABSTRACT FROM AUTHOR]

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R2 (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the "black box" problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production. [ABSTRACT FROM AUTHOR]

Characterizing changes in rock properties is essential for the hydraulic fracture and re-fracture parameter optimization of shale formations. This paper proposed a hydraulic fracturing model to investigate the changes in rock properties during hydraulic fracturing using SPH, and the changes in the stress field and rock properties were quantitatively characterized. The simulation results indicated that the minimum horizontal principal stress increased by 10 MPa~15 MPa during fracture propagation, which is the main reason for the uneven propagation in multi-fracture propagation. Affected by the stress disturbance, the stimulated area was divided into four parts based on the changes in Young's modulus and permeability; the more seriously the stress disturbance was affected, the higher the permeability of the stimulated zone was, and the smaller the stimulated zone was. Meanwhile, a zone with reduced permeability appeared due to the compression effect caused by the high injection pressure, and this increased with the increase in stress disturbance. The main reason for this was that strain formed because of the compression effect from the high injection pressure. The higher the stress disturbance, the higher the accumulated strain. This new model provides a new method for fracture parameter optimization, which also provides a foundation for the re-fracture parameter optimization of shale formations. [ABSTRACT FROM AUTHOR]