Loading...
| Friend's email | |
| Your name | |
| Your email | |
| enter code | |
This page was sent successfuly
- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 39849 (06)
- University: Sharif University of Technology
- Department: Chemical and Petroleum Engineering
- Advisor(s): Pishvaie, Mahmoud Reza; Badakhshan, Amir; Kamali, Mohammad Reza
- Abstract:
- Management decisions, Enhanced Oil Recovery, and reservoir development plans in petroleum industries are based on predictions by reservoir simulation. Due to uncertainties in model parameters or engineering assumptions, the simulation results are not accurate, while they are correct. One remedy is to match simulation results with engineering measurements, typically pressure and flow rate, by adjusting model parameters. In Reservoir Engineering literature, this is called History Matching and is an optimization problem. Common methods of history matching are based on finite difference simulation, and therefore have a very low speed. In addition, these methods usually ignore Geostatistical constraints; therefore the resulting model parameters may not be consistent with reservoir Geostatistics. The purpose of this project is to use streamline simulation for history matching production data. The streamline simulation is very fast and therefore considerably enhances the speed of history matching algorithm. Besides, in this project we try to include Geostatistical constraints in optimization problem. This causes the results of history matching to be consistent with reservoir Geostatistics. Coding of streamline simulation, choosing a suitable optimization algorithm, and applying the algorithm to two synthetic reservoirs are the essential parts of this project. The proposed algorithm needs fewer flow simulations with respect to traditional history matching procedures; this causes the proposed algorithm to be very fast and computationally efficient. By using the Gradual Deformation Technique, Geostatistical constraints are included in the intermediate objective function. Moreover, the Gradual Deformation Technique reduces the number of independent variables in optimization problem; therefore the minimization task becomes easier. Unlike the previous algorithms, which only adjusted the permeability distribution, our algorithm is designed to adjust the permeability and porosity distributions simultaneously. Our algorithm intelligently, with the aid of streamline trajectories, adjusts the permeability and porosity only in regions that cause mismatch between simulation results and field observations. The proposed algorithm is completely automatic and there is no need for user experience.
- Keywords:
- Production History Matching ; Optimization ; Simulation ; Streamline ; Objective Function ; Geostatiatics
Digital Object List-
محتواي پايان نامه - view
Bookmark