Three compositional reservoir simulators have been developed in the Department of Petroleum and Geosystems Engineering at The University of Texas at Austin (UT-Austin): UTCOMP (miscible gas flooding simulator), UTCHEM (chemical flooding simulator), and GPAS (General Purpose Adaptive Simulator). UTCOMP and UTCHEM simulators have been used by various oil companies for solving a variety of field problems. The efficiency and accuracy of each simulator becomes critically important when they are used to solve field problems. In this study, two well-developed solver packages, SAMG and HYPRE, along with existing solvers were compared. Our numerical results showed that SAMG can be an excellent solver for the usage in the three simulators for solving problems with a high accuracy requirement and long simulation times, and BoomerAMG in HYPRE package can also be a good solver for application in the UTCHEM simulator. In order to investigate the flexibility and the efficiency of a partitioned coupling method, the second part of this thesis presents a new implementation using a partition method for a thermal module in an equation-of-state (EOS) compositional simulator, the General Purpose Adaptive Simulator (GPAS) developed at The University of Texas at Austin. The finite difference method (FDM) was used for the solution of governing partial differential equations. Specifically, the new coupled implementation was based on the Schur complement method. For the partition method, two suitable acceleration techniques were constructed. One technique was the optimized choice of preconditioner for the Schur complement; the other was the optimized selection of tolerances for the two solution steps. To validate the implementation, we present simulation examples of hot water injection in an oil reservoir. The numerical comparison between the new implementation and the traditional, fully implicit method showed that the partition method is not only more flexible, but also faster than the classical, fully implicit method for the same test problems without sacrificing accuracy. In conclusion, the new implementation of the partition method is a more flexible and more efficient method for coupling a new module into an existing simulator than the classical, fully implicit method.The third part of this thesis presents another type of coupling method, iterative coupling methods, which has been implemented into GPAS with thermal module, FICM (Fully, Iterative Coupling Method) and GICM (General, Iterative Coupling Method), LICM (Loose, Iterative Coupling Method). The results show that LICM is divergent, and GICM and FICM can work normally. GICM is the fastest among the compared methods, and FICM has a similar efficiency as CFIM (Classic Fully Implicit Method). Although GICM is the fastest method, GICM is less accurate than FICM for in the test cases carried out in this study. / text

In the past thirty years, the development of compositional reservoir simulators using various equations of state (EOS) has been addressed in the literature. However, the development of compositional thermal simulators in conjunction with EOS formulation has been ignored, in particular. Therefore in this work, a fully implicit, parallel, compositional EOS-based simulator has been developed. In this model, an equation of state is used for equilibrium calculations among all phases (oil, gas, and aqueous). Also, the physical properties are calculated based on an equation of state, hence obviating the need for using steam tables for calculation of water/steam properties. The governing equations for the model comprise fugacity equations between the three phases, material balance, pore volume constraint and energy equations. The governing partial differential equations are solved using finite difference approximations. In the steam injection process, the solubility of oil in water-rich phase and the solubility of water in oil phase can be high. This model takes into account the solubility of water in oil phase and the solubility of hydrocarbon components in water-rich phase, using three-phase flash calculations. This simulator can be used in various thermal flooding processes (i.e. hot water or steam injections). Since the simulator was implemented for parallel computers, it is capable of solving large-scale thermal flooding problems. The simulator is successfully validated using analytical solutions. Also, simulations are carried out to compare this model with commercial simulators. The use of an EOS for calculation of various properties for each phase automatically satisfies the thermodynamic consistency requirements. On the other hand, using the K-value approach, which is not thermodynamically robust, may lead to results that are thermodynamically inconsistent. This simulator accurately tracks all components and mass transfer between phases using an EOS; hence, it will produce thermodynamically consistent results and project accurate prediction of thermal recovery processes. Electrical heating model, Joule heating and in-situ thermal desorption methods, and hot-chemical flooding model have also been implemented in the simulator. In the electrical heating model, electrical current equation is solved along with other governing equations by considering electrical heat generation. For implementation of the hot-chemical heating model, first the effect of temperature on the phase behavior model and other properties of the chemical flooding model is considered. Next, the material and energy balance and volume constraints equations are solved with a fully implicit method. The models are validated with other solutions and different cases are tested with the implemented models. / text