Ruijie Zhang

Digitalization is leading the development direction of oilfields in the future. And the precise measurement of produced fluids is the core component supporting the construction of digital oilfields. To mitigate the adverse effects of liquid carryover from gas wells on metering devices at the wellhead, this paper proposed a compact tandem-type gas–liquid separator structure (CTGLS) based on the principle of cyclone separation. The internal flow field characteristics and separation performance of the gas–liquid separator were analyzed through numerical simulation and experimental methods. The influence of various liquid concentrations, inlet flow rates, and overflow split ratios on the velocity field, medium distribution, and separation efficiency of gas–liquid separators was obtained. The optimal regulatory relationship of the underflow split ratio under different operating parameters was elucidated. The results indicate that, as the liquid concentration increases, the axial velocity changes significantly within the underflow region of the secondary separator, while the liquid volume fraction in the cyclone chamber increases gradually. Increasing the inlet flow rate and the split ratio can enhance the axial velocity at the overflow outlet, but it will reduce the liquid phase separation efficiency. The mathematical model correlating the operating parameters with the separation efficiency was established using the response surface method. And the best operation regulation mechanism of the split ratio was obtained under different inlet flow rates and liquid concentrations.

Water injection development is one of the commonly used development methods in oilfields. The water injection system is large and complex, with horizontally and vertically interconnected pipe networks often buried underground. Faults in the pipe networks can not be detected and handled in time, posing significant safety hazards to production. This paper focuses on fault diagnosis and decision-making within the water injection system. We established a fault tree for the oilfield water injection system and proposed for the first time the optimized BP neural network with the Self-Adaptive Differential Evolution Algorithm. This method constructed a two-layer fault diagnosis model for the water injection system. The model diagnosed fault positions and types based on the parameters such as fault point flow, pressure and pipeline flow. Compared to the traditional BP algorithm, this algorithm had better diagnostic accuracy and faster convergence speed. Simultaneously, the decision tree CART method was employed to classify decision types based on multiple parameter indicators of fault points and generated decisions. We designed and implemented a fault diagnosis and decision platform for oilfield water injection system. In the final we built an experimental pipeline network model with EPANET to simulate system fault conditions. The diagnostic performance of the algorithm was tested. The results show that the proposed method achieved a 99% accuracy rate in diagnosing faults in the water injection system. This method significantly improved the scientific management of water injection system, holding great potential for broad application and value in achieving smart oilfields.

Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain–machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.