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.