The goal of this study is to test out the accuracy of the upscaling approach (Jinyu Tang, 2021) for a geothermal doublet. This approach tries to simulate the physical thermal dispersion. When modeling in a geothermal reservoir it would be easy if a lot of layers can be upscaled. However, when doing upscaling one can not just take an average of the layer properties. This results in a very inaccurate representation of reality. That is where the upscaling comes in. First, some other points need to be considered. The reservoir is simulated in DARTS (Wang, Voskov, Khait, & Bruhn, 2020). This simulator uses numerical methods to model the reservoir. Before starting on modeling geothermal reservoirs first a 1D case is evaluated to check for numerical dispersion that comes into play. With that evaluation done the 2D cases were simulated. The full reservoir has 91 layers, these can be upscaled into 9 layers eventually. To start building up to that scenario first 2 other scenarios are evaluated. First, an upscaled section of the first block of upscaled layers is evaluated. This was originally 10 layers and now upscaled to one layer. This one layer was first simulated in a one grid simulation and then a single grid simulation. These results are compared to a simulation of the original layer properties and a simulation of an arithmetic average of the reservoir properties. This is also done on a second scenario with 2 upscaled groups and eventually the full 9 layer reservoir (originally 91 layers). With these scenarios evaluated the upscaling shows to give a better simulation of the reservoir compared to using an average for the layer properties. This is all compared with a simulation of the original layer properties.

Foam is a promising solution for improving the poor sweep efficiency of gas injection for enhanced oil recovery (EOR), in that foam lowers gas mobility significantly by trapping gas bubbles. Most of oils destabilize foam, the effect of which dominates foam strength when foam is in contact with oil and is key to success of foam EOR, but not yet fully understood. This study focuses on understanding oil displacement by foam in lab scale and providing insights for field-scale applications. We carry out the study from two perspectives: a) X-ray CT corefloods that give data on both saturation and pressure response in a foam displacement and b) data fitting to corefloods results.

Experimental studies are conducted with two types of simplified model oil to avoid confusion of the effect of multi-components of crude oil: hexadecane (C16) that is benign to foam and a mixture of C16 and oleic acid that is greatly detrimental to foam stability. CT scanning provides data on oil saturation (So) and distribution along a core during the corefloods. Such key information relates So to foam dynamics as implied by pressure response, in particular foam generation and propagation suggesting efficiency of oil displacement by foam. Specifically, foam is injected in two ways: co-injection of surfactant solution and gas with the intention to generate foam in situ in the presence of oil and injection of pregenerated foam. The co-injection serves to check the effect of oil on foam generation and propagation (anti-foaming effect) in the displacement. The second way investigates de-foaming effect of oil on foam displacement. The results show that foam generation in situ in the presence of oil is possible only when the oil is relatively benign to foam. When oil is very detrimental to foam, the in-situ generation is very difficult even at oil saturation as low as residual oil saturation. The displacement of pregenerated foam with oil consists of two stages of foam propagation due to change in So. Foam drains down So with a weaker strength due to high So in the primary propagation. In the secondary propagation, foam propagates faster with a larger strength, pushing oil forward like a piston.

Fitting coreflood data is then performed using a most widely used local-equilibrium implicit-texture foam model in CMG STARS™ simulator. We propose a fitting guideline for estimating foam simulation parameters based on steady-state foam flow data with oil in a recent study that does not give data on So. The model parameters estimated using this method serve as an initial guess for finding the optimal parameters to fit the coreflood data in simulation. A simulator for foam simulation in STARS is set up for this purpose. A scheme is then developed to optimize the model parameters in fitting the dynamic data. The challenges and difficulties in the fitting are briefly summarized for future work.

The mobilization of the trapped residual oil is an important part of Enhanced Oil Recovery. The desaturation of nonwetting fluids from porous media is often described using capillary numbers which are a ratio of viscous forces over the capillary forces between the wetting and the nonwetting fluids. Twodimensional microfluidic devices (micromodels) play an important role as they allow for the visualization of the two phase flow in porous media. Even though the existing definitions for capillary numbers work fine for describing the mobilization of the trapped nonwetting phase in geological rock, problems arise when applying these definitions for capillary numbers on micromodels. The conventional definition of the capillary numbers do not allow to visualize the desaturation of nonwetting phases in different micromodels on a single trend. This research presents the derivation of a new definition of a capillary number that can be used to better analyze the mobilization of trapped nonwetting ganglions inside micromodels. The new capillary number is based on a force balance on a trapped ganglion and the corresponding mobilization criteria of the trapped nonwetting phase. The new definition of the capillary number consists of the conventional definition of the capillary number and a geometric term that accounts for the geometry of the micromodel, including the sizes of the pore bodies and throats as well as the ganglion length. The functionality of similar definitions of the capillary number for roughened fractures has previously been published by Al Quaimi and Rossen (2017). The functionally of the new capillary number has been tested by analyzing published desaturation data for various micromodels, each using different wetting and nonwetting phase combinations using both the conventional and the new definition of the capillary number. It is found that the new definition allows for better analysis of the mobilization of the trapped nonwetting fluid inside micromodels, as the desaturation curves for different models can now be visualized by a single trend. Also, it is suggested that the geometrical parameters of the new equation are more significant for describing the mobilization of the trapped nonwetting phase than the of the medium and the interfacial properties of the wetting and nonwetting phases.