The real-time and accurate monitoring of the production well fluid profile is crucial for guiding dynamic adjustments in oilfield development. It plays a critical role in evaluating the proportion of fluid produced from different production layers, optimizing parameters for horizontal well fracturing, and adjusting production dynamics. In recent years, fluid profile testing based on distributed acoustic sensing (DAS) technology has emerged as a new method with high accuracy and strong real-time capabilities. This technique is particularly suitable for the high-temperature, high-pressure, and narrow-complex downhole environments commonly found in oil and gas fields. However, most current research on distributed fiber-optic profile monitoring is focused on theoretical studies and laboratory experiments, with limited application to actual production conditions of the mining field operations. In actual production, downhole fiber-optic signals are often subject to interference from complex noise, and their response characteristics can be highly variable. Furthermore, there is a lack of mature analysis processes and models for using DAS technology to analyze downhole fluid events and calculate production profiles. This paper proposes a model and calculation process for fluid profile analysis that can be applied to mining field production scenarios. The method involves deploying distributed fiber-optic acoustic sensing to collect fiber-optic data under various operational conditions. Frequency analysis is then performed to identify effective frequency bands, and the fluid profile is calculated from the perspective of acoustic energy. This approach addresses the challenge of limited analysis methods for calculating production profiles from distributed acoustic fiber-optic data. To validate the proposed model and process, the paper analyzes data from three wells in a mining field. The results indicate that in the 400-800 Hz frequency range, the maximum difference in Fiber-Based Energy (FBE) energy occurs during well switching. This allows the system to filter out most background and environmental noise while retaining important flow-related information. After opening the wells, all three wells showed a delay in energy response. During the production phase, the production layers did not extend to all depths, and a dominant influx region was observed. However, after the second well opening, the overall fluid profile distribution became more uniform. Additionally, the intensity of the FBE energy varied between the first and second well openings, with stronger absolute FBE energy observed during the first production phase. These findings provide valuable insights into optimizing oilfield operations and improving the accuracy of fluid profile monitoring through distributed acoustic sensing technology.