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Using Intelligent Local Thermodynamic Models in Simulation of Thermal EOR Processes

Khorsandi, Saeeid | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40324 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Bozorgmehri, Ramin; Pishvaie, Mahmoud Reza
  7. Abstract:
  8. Fluid property calculation is one of the most time consuming parts of compositional reservoir simulation. Also in thermal reservoir simulations, more iterations and smaller time steps increase number of fluid properties calculations required. Fluid property calculations require multiphase equilibrium calculations which could be very time consuming depending on the level of sophistication of the fluid model used. In this work a simulator with capability to use EoS for fluid property calculations is developed. The simulator uses full implicit finite difference solution scheme. EoS is used as fluid model to increase the accuracy and applicability of simulator for different fluid properties and conditions. To omit negative effect of using Eos, corresponding to increase of computational demands, intelligent local thermodynamic models are used to eliminate repeated calculations. Classic multiphase equilibrium calculations are almost the same for all fluid property calculations during a simulation and each time it start from the scratch and independent of previous calculations; the main idea of local model is to reuse results of one rigorous calculation at nearby conditions and do not waste information by repeating calculations from the starting point. Two approaches are used to incorporate intelligent models as local thermodynamic models; the first one is to predict phase changes using fuzzy logic models. Stability test are one of the time consuming parts of multiphase flash calculations. Unnecessary stability tests can be eliminated from calculations using a fuzzy logic model. Considering a fluid whose its properties are completely determined by some method in a predefined condition, local intelligent model can be used to predict the effect of small changes in conditions of fluid on phase stabilities. In the second approach, neural networks are used to calculate k-values independent of phase compositions and also to estimate error of these calculations using another neural networks model. Most important effect of calculating k-values independent of phase composition is the elimination of the iterative procedure corresponding to the dependency of the k-values and compositions
  9. Keywords:
  10. Reservior Simulation ; Neural Network ; Thermodynamic Equilibrium ; Fuzzy Logic

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