Upscaling in polymer flooded reservoirs
Author(s)
Pongthunya, Potcharaporn
Type
Thesis
Abstract
Polymer flooding is one of the most successful techniques in Enhanced Oil Recovery. One of the obstacles to implementing the technique is the understanding of fluid flow in porous media at different length scales. Although many of the microscopic processes, in microns, in the reservoir are well understood, simulating fluid flow in the reservoir at the micron scale is completely impractical. Each project requires numerous simulations to cover a wide range of scenarios. To shorten the run time, the blocks in the reservoir model are generally coarsened from the core scale in centimetres to larger scales in metres or kilometres. This helps reduce the number of gridblocks for simulations from around 10 [to the power of 13] cells to at most 10 [to the power of 5] or 10 [to the power of 6] cells. Reservoir rock properties such as porosity and permeability are averaged from the small scales using various methods, known as upscaling. In practice, upscaled permeabilities are calculated using the techniques derived for water flooding. The same upscaled model is then used for studying a variety of fluid displacements and injection schemes. The impact of using upscaled models for simulations of non-Newtonian flow displacement, as in polymer flooding, is not well understood.
This study investigates the effects of upscaling errors on production forecasts in non-Newtonian flow and recommends an approach to be applied in upscaled models for better production predictions. Two permeability distributions: a two-dimensional randomly generated lognormal permeability field and a fluvial system are investigated. These models are flooded by fluids governed by a power law rheological model that represents Newtonian, shear-thinning, and shear-thickening flow behaviour. The errors in production predictions and pressure profiles are analysed. We find considerably high errors in predictions when the properties of fluid displacement are changed. These significant errors can harm economic evaluations of projects. In addition, we prove that upscaled models manipulated for a perfect match to a fine scale model under water flooding should not be used for polymer flooded modelling. Furthermore, we discover that in addition to upscaling permeability, effective viscosity should be parameterised when injecting with non-Newtonian fluid. We recommend adjusting the power law exponent of the displacing fluid model for better results. We verify the new approach and conclude that a good agreement in predictions between fine and coarse scale models can be achieved by a single phase upscaling with an adjustment of the exponent in the power law rheological model.
This study investigates the effects of upscaling errors on production forecasts in non-Newtonian flow and recommends an approach to be applied in upscaled models for better production predictions. Two permeability distributions: a two-dimensional randomly generated lognormal permeability field and a fluvial system are investigated. These models are flooded by fluids governed by a power law rheological model that represents Newtonian, shear-thinning, and shear-thickening flow behaviour. The errors in production predictions and pressure profiles are analysed. We find considerably high errors in predictions when the properties of fluid displacement are changed. These significant errors can harm economic evaluations of projects. In addition, we prove that upscaled models manipulated for a perfect match to a fine scale model under water flooding should not be used for polymer flooded modelling. Furthermore, we discover that in addition to upscaling permeability, effective viscosity should be parameterised when injecting with non-Newtonian fluid. We recommend adjusting the power law exponent of the displacing fluid model for better results. We verify the new approach and conclude that a good agreement in predictions between fine and coarse scale models can be achieved by a single phase upscaling with an adjustment of the exponent in the power law rheological model.
Date Issued
2013-01
Date Awarded
2013-01
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
King, Peter
Sponsor
The John S. Archer Endowment Fund ; Saudi Aramco
Publisher Department
Earth Science and Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)