When surfactant is used as emulsifier, the stability of emulsion is often greatly reduced with the influence of reservoir conditions (temperature, pressure, salinity, etc. ), which shortens the validity period of emulsion. Pickering emulsion has a wide range of applications in the oil and gas field due to its advantages of good stability and easy regulation. In this article, the formation, stabilization mechanism, and influencing factors of Pickering emulsions were introduced, and the application status and prospects of Pickering emulsions in oil and gas field were summarized. It was pointed out that Pickering emulsion has many advantages and important research value when applied in deep strata and complicated reservoirs. It is expected that this article can effectively reflect the application value of Pickering emulsion in oil and gas field and promote the application of Pickering emulsion in petroleum industry.

Owing to the importance of rod pumping system fault detection using an indicator diagram, indicator diagram identification has been a challenging task in the computer‐vision field. The gradual changing fault is a special type of fault because it is not clearly indicated in the indicator diagram at the onset of its occurrence and can only be identified when an irreversible damage in the well has been caused. In this paper, we proposed a new method that combines the convolutional neural network (CNN) and long short‐term memory (LSTM) network to perform a gradual changing fault classification. In particular, we employed CNN to extract the indicator diagram multilevel abstraction features based on its hierarchical structure. We considered the change in the time series of indicator diagrams as a sequence and employed LSTM to perform recognition. Compared with traditional mathematical model diagnosis methods, CNN‐LSTM overcame the limitations of the traditional mathematical model theoretical analysis such as unclear assumption conditions and improved the diagnosis accuracy. Finally, 1.3 million sets of well production were set as a training dataset and used to evaluate CNN‐LSTM. The results demonstrated the effectiveness of utilizing CNN and LSTM to recognize a gradual changing fault using the indicator diagram and characteristic parameters. The accuracy reached 98.4%, and the loss was less than 0.9%.

In view of reservoirs with low pressure, medium porosity, and low permeability in Changqing Oilfield, along with the “double high stage” in the middle and late development featuring a sharp rise in water cut and a low comprehensive water flooding recovery rate, a study of a composite surfactant flooding system was conducted. It is found that there is a synergistic effect between dodecyl hydroxypropyl sulfobetaine (HPSB) and naphthenic petroleum sulfonate (NPS). When the ratio of HPSB to NPS is 8:2, the best interfacial activity is exhibited in the formation water of the target block, which can reduce the interfacial tension of oil–water equilibrium to 1 × 10⁻³ mN/m and has strong salt tolerance. The synergistic effect of the two makes the composite system have good anti-adsorption performance, with little adsorption loss on the sand surface, and no significant chromatographic separation in the formation. The composite system also has good stability under reservoir conditions. The 0.2% composite surfactant system can reduce the water injection pressure from 1.52 to 1.16 MPa, a decrease of 23.7%; and increase the oil recovery ratio from 45.71 to 63.33%, an increase of 17.62%. These results demonstrate that the composite system (HPSB:NPS = 8:2) has a positive impact on pressure reduction and production increase in the low permeability reservoirs of Changqing Oilfield.

Accurately obtaining the working status of the sucker rod pumping wells is a challenging problem for oil production. Sensors at the polished rod collect working data to form surface dynamometer cards for fault diagnosis. A prevalent method for recognizing these cards is the convolutional neural network (CNN). However, this approach has two problems: an unbalanced dataset due to varying fault frequencies and similar dynamometer card shapes that complicate recognition. This leads to a low accuracy of fault diagnosis in practice, which is unsatisfactory. Therefore, this paper proposes a hybrid approach of the deep learning method and rule-based method for fault diagnosis of sucker rod pumping wells. Specifically, when the CNN model alone fails to achieve satisfactory accuracy in the working status, historical monitoring data of the relevant wells can be collected, and expert rules can assist CNN to improve diagnostic accuracy. By analyzing time series data of factors such as the maximum and minimum loads, the area of the dynamometer card, and the load difference, a knowledgebase of expert rules can be created. When performing fault diagnosis, both the dynamometer cards and related time series data are used as inputs. The dynamometer cards are used for the CNN model to diagnose, and the related time series data are used for expert rules to diagnose. The diagnostic results and the confidence levels of the two methods are obtained and compared. When the two diagnostic results conflict, the one with higher confidence is preserved. Out of the 2066 wells and 7 fault statuses analyzed in field applications, the hybrid approach demonstrated a 21.25% increase in fault diagnosis accuracy compared with using only the CNN model. Additionally, the overall accuracy rate of the hybrid approach exceeded 95%, indicating its high effectiveness in diagnosing faults in sucker rod pumping wells.