Random walk particle modelling of polymer injection using matlab reservoir simulation toolbox

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2017

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Mamak, Gökhan

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Enhanced oil recovery (EOR) is essential to increase the maximum recoverable oil by natural means of production. Having chosen an EOR method, the effectiveness of the method should be analyzed before applying to a reservoir since the methods are generally costly. Polymer injection is a chemical EOR process, where the injected polymer with water increases the water viscosity, and help increasing the sweep efficiency in the reservoir. In order to model the effects of polymer injection, the random-walk particle tracking method is implemented on MATLAB Reservoir Simulation Toolbox (MRST), an open source code for MATLAB for reservoir modelling. Our approach is to utilize the random walk form of transport equation for the advection/diffusion of the injected chemical in the porous medium whereas the continuity equations are solved by the black-oil simulator MRST. Verification of MRST models integrity is done by comparing its simple transport model with a known analytical solution, and its polymer model with ECLIPSE 100 Black Oil Simulator’s results. Then the particle tracking method is applied on one-dimensional and two-dimensional injection scenarios. The method overcomes the numerical diffusion problem, which is a problem of finite difference/finite volume discretization techniques. We can also use the method to model the effect of dispersion coefficient, which is hard to obtain by normal methods. However, the random nature of the solution sometimes causes convergence problems in some two-dimensional simulations.

Subject Keywords

Oil fields, Enhanced oil recovery.

URI

http://etd.lib.metu.edu.tr/upload/12621348/index.pdf
https://hdl.handle.net/11511/26762

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

G. Mamak, “Random walk particle modelling of polymer injection using matlab reservoir simulation toolbox,” M.S. - Master of Science, Middle East Technical University, 2017.