Fluids injected during secondary and tertiary floods often leave parts of the reservoir unswept mostly because of large heterogeneity and mobility ratio. Several applications require an analytical scheme that could predict production with as few parameters possible. We develop such an analytical model of volumetric sweep that aims to apply an extension of Koval’s theory where flow is assumed to be segregated under vertical equilibrium conditions for secondary and tertiary displacements. The unified theory for vertical equilibrium (viscous and dispersive) is also derived as a precursor to model development. The original Koval factor is applicable for upscaling secondary miscible floods. The new analytical model for secondary and tertiary floods is applied to provide quick estimates of oil recovery of miscible as well as immiscible displacements, which is then calibrated against field data. The model parameters, Koval factor, sweep efficiency and pore volume, estimated after history matching could be used to make reservoir management decisions. The model is very simple; history matching can be done in a spreadsheet. Single-front, gravity-free, displacements can be modeled using Koval factors. Two-front, gravity-free, displacements can also be modeled using Koval-type factors for both the fronts. These Koval-type factors, coupled with laboratory scale relative permeabilities, allows for scaling the displacement to a larger reservoir system. The new method incorporates by-passed pore volume as a parameter, a difference between this work and that of Molleai, along with Koval factors and local front velocities. For two front displacements, it also accounts for the interaction between the fronts which honors correct mass conservation, another difference with the work of Molleai. The results from new models for secondary and tertiary displacements were verified by comparing them against numerical simulations. The application was also demonstrated on actual field examples. Current techniques for reservoir surveillance rely on numerical models. The parameters on which these numerical models depend on are very large in number, introducing large uncertainty. This technique provides a way to predict performance without the use of computationally expensive fine scale simulation models, which could be used for reservoir management while reducing the uncertainty. / text

As easy target reservoirs are depleted around the world, the need for intelligent enhanced oil recovery (EOR) methods increases. The first part of this work is focused on modeling aspects of novel chemical EOR methods for naturally fractured reservoirs (NFR) involving wettability modification towards more water wet conditions. The wettability of preferentially oil wet carbonates can be modified to more water wet conditions using alkali and/or surfactant solutions. This helps the oil production by increasing the rate of spontaneous imbibition of water from fractures into the matrix. This novel method cannot be successfully implemented in the field unless all of the mechanisms involved in this process are fully understood. A wettability alteration model is developed and implemented in the chemical flooding simulator, UTCHEM. A combination of laboratory experimental results and modeling is then used to understand the mechanisms involved in this process and their relative importance. The second part of this work is focused on modeling surfactant/polymer floods using a fully implicit scheme. A fully implicit chemical flooding module with comprehensive oil/brine/surfactant phase behavior is developed and implemented in general purpose adaptive simulator, GPAS. GPAS is a fully implicit, parallel EOS compositional reservoir simulator developed at The University of Texas at Austin. The developed chemical flooding module is then validated against UTCHEM. / text