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Prediction of Flow Profiles in Different Well Layers using Fiber Optic Data Analysis

Arab, Alireza | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57961 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Bazargan, Mohammad
  7. Abstract:
  8. Downhole temperature measurement is one of the key tools for monitoring and analyzing the performance of production wells, evaluating water injection, and identifying multiphase flows within wells. The development of fiber optic technology and tools such as distributed temperature sensors has enabled the recording of high-resolution and detailed temperature profiles. These data, which measure temperature over a wide range along the wellbore, allow researchers to analyze complex downhole processes and identify fluid entry from various reservoir layers. However, accurately distinguishing the contribution of each layer to the total flow rate, especially under multiphase flow conditions and complex well geometries, remains a major challenge in this field. To address this challenge, combining advanced thermal modeling with precise optimization methods is essential. In this regard, the use of intelligent methods such as evolutionary algorithms has proven highly effective due to their ability to search complex and nonlinear solution spaces. This research presents a novel approach for analyzing and interpreting temperature data using a hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO), aiming to predict the flow profile of different layers. In this study, a well and reservoir temperature model was first developed using mass, momentum, and energy balance equations. Then, using temperature data and applying the HGAPSO optimization algorithm, a precise inversion of the flow profile within the well was performed. This approach enables the simultaneous estimation of fluid properties and the flow rate of each layer. Advantages of this method include high accuracy under multiphase conditions, sensitivity to subtle changes in temperature data, and fast convergence. The results obtained from this research demonstrated that the proposed algorithm offers high accuracy and efficiency in predicting flow profiles. This accuracy is well maintained even under complex conditions such as multiphase flows. Studies show that the algorithm, by leveraging advanced modeling and input data analysis, provides highly accurate results. Moreover, the algorithm’s adaptability to various operational conditions makes it a powerful and effective tool for studying and optimizing fluid flows in diverse systems
  9. Keywords:
  10. Fiber Optics ; Optimization ; Down-Hole Measurement ; Well Logging System ; Production Profile Predictio

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