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Application of Artificial Intelligence for Screening of Improved Oil Recovery Methods

Rezaeian, Javad | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52631 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Jamshidi, Saeid; Jahanbakhshi, Saman
  7. Abstract:
  8. To achieve the highest amount of FOPT during reservoir life and the most net present value, the parameters that are effective in the production of the field are adjusted to be the best performance. In order to achieve these conditions, the reservoir model and the surface will be checked integrated so that the optimal mode of each parameter is determined in interaction with other parameters, so the first integrated model of the reservoir and the surface and then optimizing the target functions of cumulative production and the net present value is performed using the genetic algorithm.Since the number of parameters of reservoir model and surface in the studied field in this study is high, first, sensitivity analysis is done to determine the most effective parameters and the less important parameters are not considered in optimization. Another issue that is important in sensitivity analysis and optimization is the high number of necessary runs to achieve the response. Since it takes a long time to run the original integrated model, it is necessary to use a surrogate model that can simulate the behavior of the original model with high accuracy and respond in a shorter time. In this study, Polynomial Chaos Expansion have been used to create a surrogate model.In short, in this study, the integrated reservoir and surface model for an Iranian oil field was constructed and after selecting the most important parameters of model and creating a surrogate model, optimization was performed for two objective functions of cumulative oil production and net present value using genetic algorithm as one of the branches of artificial intelligence
  9. Keywords:
  10. Artificial Intelligence ; Genetic Algorithm ; Surrogate Model ; Sensitivity Analysis ; Integrated Assesment Model ; Integrated Model

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