The goal of this project was to develop an advanced reservoir simulation and visualization tool for compositional flow and transport coupled with geomechanical deformation in porous media, with advanced mobility control methods such as foam, to better model and predict oil production during carbon dioxide (CO₂) flooding and improve predictions of oil production from residual oil zones.

The University of Texas at Austin, Austin, Texas 78713-7726

Carbon dioxide enhanced oit recovery (CO₂-EOR) has exhibited strong growth in the past 30 years and has expanded despite extreme crude oil price fluctuations. However, there are several issues challenging the oil recovery, economic efficiency, and applicability of the process.

Predictive simulation may be the only means to develop optimal strategies in the absence of complete characterization of the reservoir geology. It was critical to develop robust, scalable, and mechanistic numerical modeling tools because of the multiple scales of the various interacting processes in the reservoir, the basin scale of the reservoir, the need for long time predictions, and the potential for significant coupling between geomechanics and flow.

This project will develop an advanced CO₂-EOR simulator with visualization capability, coupled with an advanced phase behavior and foam mobility control module, coupled with geomechanical deformation in porous media, and an advanced grid for complex reservoirs. The project will also include support for the modeling of complex coupled fluid flow and transport processes, geomechanical deformation, three-phase flash, a mechanistic foam model, and a comprehensive relative permeability model that includes the effects of composition, interfacial tension, and hysteresis. The simulator will be used to accurately predict changes in the reservoir system during injection of viscous CO₂ and aid in the design and optimization of new generation CO₂ injection projects with permanent CO₂ storage in mind. The research will result in a computational framework and modules with advanced numerical algorithms and underlying technology for research in CO₂ applications, which will be validated against published results and benchmarked against other simulators. The project will support the education and training of an interdisciplinary work force.

Development of a new, computationally efficient, high-performance reservoir simulator with visualization capability for new generation CO₂-EOR and CO₂ storage will help maximize oil production after waterflooding and from residual oil zones. Success of this proposed project may help to build stakeholder confidence in the effectiveness of this advanced technology as well as result in optimal recovery strategies to produce a significant portion of the remaining domestic oil resources.

• The simulator was made user-friendly and more compatible with commercial simulators (i.e., Eclipse and CMG) by adding reservoir properties as separate input files.
• A simplified population balance foam model was implemented.
• A general three-phase relative permeability model with hysteresis was implemented.
• Completed the coupling of the geomechanics module to the simulator.
• Acquired field data from an oil company planning to conduct a CO₂ pilot test.
• Successfully validated the foam model and its implementation by modeling and history matching two published foam experiments using both nitrogen and CO₂.
• A beta version of the visualization package was developed using the Sciencesoft tool, S3graf3D.
• A robust and efficient solver was identified and used for unstructured grids.
• The simulation framework was assembled. The completed simulator is an isothermal, three-dimensional, four-phase, compositional, equation-of–state simulator capable of simulating various recovery processes.
• An unstructured grid discretization was formulated, implemented, and tested against commercial simulators.
• Several solvers have been identified for better computational efficiency and accuracy.
• A set of benchmark cases was completed.
• A user manual was prepared.
• Phillips and Denbury are interested in using this simulator in their field operations, specifically, for foam/CO₂.

The team finished  testing and validating recently implemented features in the new simulator UT-DOECO₂.  Both empirical and semi-empirical foam models were  tested using published experimental data. The first foam model is based on two simultaneous mechanisms affecting mobility of gas in the presence of foam, namely, the reduction of the gas relative permeability because of gas trapping, and the increase in gas apparent viscosity due to the resistance of movement of lamellae. The second foam model follows the local-equilibrium approximation method to the population balance modeling where the foam texture is approximated by placing foam-generation and coalescence rates in equilibrium. This foam-texture approximation increases the applicability of population balance models in the full-field three-dimensional studies. The aim was to validate the models and gain some confidence in their prediction capability.  

A modified version of Jerauld’s three-phase general hysteresis relative permeability model has been incorporated into the new simulator for further testing. This model is applicable to mixed-wet reservoirs and approaches the correct two- and three- phase flow when one phase appears or disappears due to mass transfer with other phase(s).  The trapping behavior of oil and gas phases is capillary number dependent and scaled proportional to the capillary number of the trapped phase, i.e., oil and/or gas.  The curvatures of the gas and oil relative permeabilities were also scaled according to the capillary desaturation curve. The team performed simulations to test Jerauld’s model, specifically, its impact on oil recovery, hysteretic behavior, and CO₂ breakthrough times for different mobility control methods such as Water-Alternating-Gas.

The project has been completed.  The simulator, documentation, and sample input files are available on the University of Texas at Austin website [external site].

$799,558

$199,884

NETL – Sinisha (Jay) Jikich (sinisha.jikich@netl.doe.gov or 304-285-4320)
The University of Texas at Austin – Mojdeh Delshad (delshad@mail.utexas.edu or 512-4711-3219)