Downhole parameter prediction method based on multi-layer water injection model and historical data-based model parameter identification.

Wireless communication has become a preferred direction for the development of layered water injection tools due to its low cost and high reliability. However, the wireless system relies on the underground battery for power supply，and each communication will consume a significant amount of energy. In order to save energy consumption, the wireless system adopts the intermittent sleep communication mode, with intervals of usually more than one month. During the idle time of communication, the downhole parameters such as pressure and flowrate will change as the pressure and flowrate at the wellhead. Therefore, it is crucial to predict downhole parameters based on the wellhead pressure and flowrate. In this study, a downhole parameter prediction method based on multi-layer water injection model is proposed. A multilayer injection prediction model was established based on the hydraulic analysis of the tubing string, and the model parameters were identified and updated using the historical data uploaded each time. The pressure and flow rate measured at the wellhead were used as inputs to the model, and the recursive relationship between layers in the multilayer model was utilized to predict downhole parameters for each layer. A model parameter optimization method based on time-weighting is proposed in order to address the gradual changes in model parameters during water injection. This method assigns greater weight to more recent historical data, resulting in optimized model parameters. Experimental results show that the proposed method can effectively predict the flowrate and pressure of each layer, with a prediction deviation of less than 5% F.S., which provides technical support for the application and popularization of the wireless layered water injection system.

Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model.

Because of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of manufacturing. Ultrasonic nondestructive testing has become one of the most accepted methods for inspection of homogeneous and fixed-thickness composites, or layered and fixed-interface-shape composites, but a carbon fiber sucker rod with multi-layered structures and irregular interlayer interfaces increases the difficulty of testing. In this paper, a novel defect detection method based on multi-sensor information fusion and a deep belief network (DBN) model was proposed to identify online its defects. A water-immersed ultrasonic array with 32 ultrasonic probes was designed to realize the online and full-coverage scanning of carbon fiber rods in radial and axial positions. Then, a multi-sensor information fusion method was proposed to integrate amplitudes and times-of-flight of the received ultrasonic pulse-echo signals with the spatial angle information of each probe into defect images with obvious defects including small cracks, transverse cracks, holes, and chapped cracks. Three geometric features and two texture features from the defect images characterizing the four types of defects were extracted. Finally, a DBN-based defect identification model was constructed and trained to identify the four types of defects of the carbon fiber rods. The testing results showed that the defect identification accuracy of the proposed method was 95.11%.