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Reservoir management strategies traditionally attempt to combine and balance complex geophysical, petrophysical, thermodynamic and economic factors to determine an optimal method to recover hydrocarbons from a given reservoir. Reservoir simulators have traditionally been too large and run times too long to allow for rigorous solution in conjunction with an optimization algorithm. It has also proven very difficult to marry an optimizer with the large set of nonlinear partial differential equations required for accurate reservoir simulation. A simple capacitance-resistance model (CRM) that characterizes the connectivity between injection and production wells can determine an injection scheme maximizes the value of the reservoir asset. Model parameters are identified using linear and nonlinear regression. The model is then used together with a nonlinear optimization algorithm to compute a set of future injection rates which maximize discounted net profit. This research demonstrates that this simple dynamic model provides an excellent match to historic data. Based on three case studies examining actual reservoirs, the optimal injection schemes based on the capacitance-resistive model yield a predicted increase in hydrocarbon recovery of up to 60% over the extrapolated exponential historic decline. An advantage of using a simple model is its ability to describe large reservoirs in a straightforward way with computation times that are short to moderate. However, applying the CRM to large reservoirs with many wells presents several new challenges. Reservoirs with hundreds of wells have longer production histories – new wells are created, wells are shut in for varying periods of time and production wells are converted to injection wells. Additionally, ensuring that the production data to which the CRM is fit are free from contamination or corruption is important. Several modeling techniques and heuristics are presented that provide a simple, accurate reservoir model that can be used to optimize the value of the reservoir over future time periods. In addition to optimizing reservoir performance by allocating injection, this research presents a few methods that use the CRM to find optimal well locations for new injectors. These algorithms are still in their infancy and represent the best ideas for future research. / text

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

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

[pt] Companhias de petróleo investindo no desenvolvimento de campos de hidrocarboneto dependem de estudos de reservatórios para realizarem previsões de produção e quantificarem os riscos associados à economicidade dos projetos. Neste sentido, a área de modelagem de reservatórios é de suma importância, sendo responsável por prever o desempenho futuro do reservatório sob diversas condições operacionais. Considerando que a solução dos sistemas de equações construídos a cada passo de tempo de uma simulação, durante o ciclo de linearização, é a parte que apresenta a maior demanda computacional, esta dissertação foca na análise de diferentes técnicas de solvers numéricos que podem ser aplicadas a simuladores, para mensurar seus desempenhos. Os solvers numéricos mais adequados para a solução de grandes sistemas de equações, tais como os encontrados em simulações de reservatórios, são os denominados solvers iterativos, que gradativamente aproximam a solução de um dado problema por meio da combinação de um método iterativo e um precondicionador. Os métodos iterativos avaliados nesta pesquisa foram o Gradiente Biconjugado Estabilizado (BiCGSTAB), Mínimos Resíduos Generalizado (GMRES) e Minimização Ortogonal (ORTHOMIN). Além disso, três técnicas de precondicionamento foram implementadas para auxiliar os métodos iterativos, sendo estas a Decomposição LU Incompleta (ILU), Fatoração Aninhada (NF) e Pressão Residual Restrita (CPR). A combinação destes diferentes métodos iterativos e precondicionadores permite a avaliação de diversas configurações distintas de solvers, em termos de seus desempenhos em um simulador. Os testes numéricos conduzidos neste trabalho utilizaram um novo simulador de reservatórios que está sendo desenvolvido pela Pontifícia Universidade Católica (PUC-Rio) em conjunto com a Petrobras. O objetivo dos testes foi analisar a robustez e eficiência de cada um dos solvers quanto à sua capacidade de resolver as equações de escoamento multifásico no meio poroso, visando assim auxiliar na seleção do solver mais adequado para o simulador. / [en] Petroleum companies investing in the development of hydrocarbon fields rely upon a variety of reservoir studies to perform production forecasts and quantify the risks associated with the economics of their projects. Integral to these studies is the discipline of reservoir modeling, responsible for predicting future reservoir performance under various operational conditions. Considering that the most time-demanding aspect of reservoir simulations is the solution of the systems of equations that arise within the linearization cycles at each time-step, this research focuses on analyzing different numerical solver techniques to be applied to a simulator, in order to assess their performance. The numerical solvers most suited for the solution of very large systems of equations, such as those encountered in reservoir simulations, are the so-called iterative solvers, which gradually approach the solution to a problem by combining an iterative strategy with a preconditioning method. The iterative methods examined in this research were the Stabilized Biconjugate Gradient (BiCGSTAB), the Generalized Minimum Residual (GMRES), and the Orthogonal Minimization (ORTHOMIN) methods. Furthermore, three preconditioning techniques were implemented to aid the iterative methods, namely the Incomplete LU Factorization (ILU), the Nested Factorization (NF), and the Constrained Pressure Residual (CPR) methods. The combination of these different iterative methods and preconditioners enables the appraisal of several distinct solver configurations, in terms of their performance in a simulator. The numerical tests conducted in this work made use of a new reservoir simulator currently under development at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), as part of a joint project with Petrobras. The objective of these tests was to assess the robustness and efficiency of each solver in the solution of the multiphase flow equations in porous media, and support the selection of the solver most suited for the simulator.