Compositional simulation is a complex task that involves solving several equations simultaneously for all grid blocks representing a petroleum reservoir. Usually, these equations are separated into two groups: primary and secondary equations. Similarly, the unknowns of the system are also separated into primary and secondary variables. Considering the large number of unknowns, there are many ways to separate such variables in order to deal with the primary variables. This work aims at comparing a number of formulations for compositional reservoir simulation. It also aims at enhancing the formulations with new features not provided in the original publications. To accomplish these objectives, various formulations prevailing in the literature are implemented in The University of Texas at Austin in-house fully implicit simulator named GPAS (General Purpose Adaptive Simulator) and their performances were compared. Subsequently, some of the formulations were enhanced and tested for various applications. The comparison of the formulations studied indicated differences in efficiency for each approach. These differences come from the fact that when one is solving for a different set of primary variables, the manipulation of the equations is analogous to the use of a preconditioner applied to a linear system of equations. Furthermore, unlike a preconditioner, changing the primary variables affects the non-linear solver. Therefore, differences in terms of the number of Newton-Raphson iterations, used for solution of nonlinear equations resulting from discretization of nonlinear partial differential equations representing fluid flow in the reservoir, are expected. In addition to these differences in the non-linear solver, many formulations explore the fact that a reduced number of equations need to be solved implicitly, thus considerably reducing the CPU time dedicated to the linear solver. Finally, new features not provided in the original published formulations such as three-phase flash calculation, physical dispersion, and unstructured grid were implemented and verified. Additionally, it was demonstrated that, in certain situations, these enhancements are essential to properly model the physical phenomena occurring in oil and gas reservoirs. / text

Global warming and greenhouse gas (GHG) emissions have recently become the significant focus of engineering research. The geological sequestration of greenhouse gases such as carbon dioxide (CO₂) is one approach that has been proposed to reduce the greenhouse gas emissions and slow down global warming. Geological sequestration involves the injection of produced CO₂ into subsurface formations and trapping the gas through many geological mechanisms, such as structural trapping, capillary trapping, dissolution, and mineralization. While some progress in our understanding of fluid flow in porous media has been made, many petrophysical phenomena, such as multi-phase flow, capillarity, geochemical reactions, geomechanical effect, etc., that occur during geological CO₂ sequestration remain inadequately studied and pose a challenge for continued study. It is critical to continue to research on these important issues. Numerical simulators are essential tools to develop a better understanding of the geologic characteristics of brine reservoirs and to build support for future CO₂ storage projects. Modeling CO₂ injection requires the implementation of multiphase flow model and an Equation of State (EOS) module to compute the dissolution of CO₂ in brine and vice versa. In this study, we used the Integrated Parallel Accurate Reservoir Simulator (IPARS) developed at the Center for Subsurface Modeling at The University of Texas at Austin to model the injection process and storage of CO₂ in saline aquifers. We developed and implemented new petrophysical models in IPARS, and applied these models to study the process of CO₂ sequestration. The research presented in this dissertation is divided into three parts. The first part of the dissertation discusses petrophysical and computational models for the mechanical, geological, petrophysical phenomena occurring during CO₂ injection and sequestration. The effectiveness of CO₂ storage in saline aquifers is governed by the interplay of capillary, viscous, and buoyancy forces. Recent experimental data reveals the impact of pressure, temperature, and salinity on interfacial tension (IFT) between CO₂ and brine. The dependence of CO₂-brine relative permeability and capillary pressure on IFT is also clearly evident in published experimental results. Improved understanding of the mechanisms that control the migration and trapping of CO₂ in the subsurface is crucial to design future storage projects for long-term, safe containment. We have developed numerical models for CO₂ trapping and migration in aquifers, including a compositional flow model, a relative permeability model, a capillary model, an interfacial tension model, and others. The heterogeneities in porosity and permeability are also coupled to the petrophysical models. We have developed and implemented a general relative permeability model that combines the effects of pressure gradient, buoyancy, and capillary pressure in a compositional and parallel simulator. The significance of IFT variations on CO₂ migration and trapping is assessed. The variation of residual saturation is modeled based on interfacial tension and trapping number, and a hysteretic trapping model is also presented. The second part of this dissertation is a model validation and sensitivity study using coreflood simulation data derived from laboratory study. The motivation of this study is to gain confidence in the results of the numerical simulator by validating the models and the numerical accuracies using laboratory and field pilot scale results. Published steady state, core-scale CO₂/brine displacement results were selected as a reference basis for our numerical study. High-resolution compositional simulations of brine displacement with supercritical CO₂ are presented using IPARS. A three-dimensional (3D) numerical model of the Berea sandstone core was constructed using heterogeneous permeability and porosity distributions based on geostatistical data. The measured capillary pressure curve was scaled using the Leverett J-function to include local heterogeneity in the sub-core scale. Simulation results indicate that accurate representation of capillary pressure at sub-core scales is critical. Water drying and the shift in relative permeability had a significant impact on the final CO₂ distribution along the core. This study provided insights into the role of heterogeneity in the final CO₂ distribution, where a slight variation in porosity gives rise to a large variation in the CO₂ saturation distribution. The third part of this study is a simulation study using IPARS for Cranfield pilot CO₂ sequestration field test, conducted by the Bureau of Economic Geology (BEG) at The University of Texas at Austin. In this CO₂ sequestration project, a total of approximately 2.5 million tons supercritical CO₂ was injected into a deep saline aquifer about ~10000 ft deep over 2 years, beginning December 1st 2009. In this chapter, we use the simulation capabilities of IPARS to numerically model the CO₂ injection process in Cranfield. We conducted a corresponding history-matching study and got good agreement with field observation. Extensive sensitivity studies were also conducted for CO₂ trapping, fluid phase behavior, relative permeability, wettability, gravity and buoyancy, and capillary effects on sequestration. Simulation results are consistent with the observed CO₂ breakthrough time at the first observation well. Numerical results are also consistent with bottomhole injection flowing pressure for the first 350 days before the rate increase. The abnormal pressure response with rate increase on day 350 indicates possible geomechanical issues, which can be represented in simulation using an induced fracture near the injection well. The recorded injection well bottomhole pressure data were successfully matched after modeling the fracture in the simulation model. Results also illustrate the importance of using accurate trapping models to predict CO₂ immobilization behavior. The impact of CO₂/brine relative permeability curves and trapping model on bottom-hole injection pressure is also demonstrated. / text

Polymers play a key role in several EOR processes such as polymer flooding, surfactant-polymer flooding and alkaline-surfactant-polymer flooding due to their critical importance of mobility control in achieving high oil recovery from these processes. Numerical simulators are used to predict the performance of all of these processes and in particular the injection rate of the chemical solutions containing polymer; since the economics is very sensitive to the injection rates. Injection rates are governed by the injection viscosity, thus, it is very important to model the polymer viscosity accurately. For the predictions to be accurate, not only the viscosity model must be accurate, but also the calculation of equivalent shear rate in each gridblock must be accurate because the non-Newtonian viscosity models depend on this shear rate. As the size of the gridblock increases, the calculation of this velocity becomes less numerically accurate, especially close to wells. This research presents improvements in polymer viscosity model. Using the improvements in shear thinning model, the laboratory polymer rheology data was better matched. For the first time, polymer viscosity was modeled for complete range of velocity using the Unified Viscosity Model for published laboratory data. New models were developed for relaxation time, time constant and high shear viscosity during that match. These models were then used to match currently available HPAM polymer's laboratory data and predict its viscosity for various concentrations for full flow velocity range. This research presents the need for injectivity correction when large grid sizes are used. Use of large grid sizes to simulate large reservoir due to computation constraints induces errors in shear rate calculations near the wellbore and underestimate polymer solution viscosity. Underestimated polymer solution viscosities lead to incorrect injectivity calculation. In some cases, depending on the well grid block size, this difference between a fine scale and a coarse simulation could be as much as 100%. This study focuses on minimizing those errors. This methodology although needs some more work, but can be used in accurate predictions of reservoir simulation studies of chemical enhanced oil recovery processes involving polymers. / text

Reservoir souring refers to the increase in the concentration of hydrogen sulfide in production fluids during waterflooding. Besides health and safety issues, H₂S content reduces the value of the produced hydrocarbon. Nitrate injection is an effective method to prevent the formation of H₂S. Although the effectiveness of nitrate injection has been proven in laboratory and field applications and biology is well-understood, modeling aspect is still in its early stages. This work describes the modeling and simulation of biological reactions associated with reservoir souring and nitrate injection for souring remediation. The model is implemented in a general purpose adaptive reservoir simulator (GPAS). We also developed a physical dispersion model in GPAS to study the effect of dispersion on reservoir souring. The basic mechanism in the biologically mediated generation of H₂S is the reaction between sulfate and organic compounds in the presence of sulfate-reducing bacteria (SRB). Several mechanisms describe the effect of nitrate injection on reservoir souring. We developed mathematical models for biological reactions to simulate each mechanism. For every biological reaction, we solve a set of ordinary differential equations along with differential equations for the transport of chemical and biological species. Souring reactions occur in the areas of the reservoir where all of the required chemical and biological species are available. Therefore, dispersion affects the extent of reservoir souring as transport of aqueous phase components and the formation of mixing zones depends on dispersive characteristics of porous media. We successfully simulated laboratory experiments in batch reactors and sand-packed column reactors to verify our model development. The results from simulation of laboratory experiments are used to find the input parameters for field-scale simulations. We also examined the effect of dispersion on reservoir souring for different compositions of injection and formation water. Dispersion effects are significant when injection water does not contain sufficient organic compounds and reactions occur in the mixing zone between injection water and formation water. With a comprehensive biological model and robust and accurate flow simulation capabilities, GPAS can predict the onset of reservoir souring and the effectiveness of nitrate injection and facilitate the design of the process. / text

Production data are the most abundant data in the field. However, they can often be of poor quality because of undocumented operational problems, or changes in operating conditions, or even recording mistakes (Nobakht et al. 2009). If this poor quality or inconsistency is not recognized as such, it can be misinterpreted as a reservoir issue other than the data quality problem that it is. Thus quality control of production data is a crucial and necessary step that must precede any further interpretation using the production data. To restore production data, we propose to use the capacitance resistance model (CRM) to realize data reconciliation. CRM is a simple reservoir simulation model that characterizes the connectivity between injectors and producers using only production and injection rate data. Because the CRM model is based on the continuity equation, it can be used to analyze the production corresponding to the injection signal in the reservoir. The problematic production data are then put into the CRM model directly and the resulting CRM output parameters are used to evaluate what the correct production response would be under current injection scheme. We also make sensitivity analysis based on synthetic fields, which are heterogeneous ideal reservoir models with imposed geology and well features in Eclipse. The aim is to show how bad data could be misleading and the best way to restore the production data. Using the CRM model itself to control data quality is a novel method to obtain clean production data. We can then apply the new clean production data in reservoir simulators or any other processes where production data quality matters. This data quality control process can help better understand the reservoir, analyze its behavior in a more ensured way and make more reliable decisions. / text

Simulation of the system behavior of hydrate-bearing geologic media involves solving fully coupled mass- and heat-balance equations. In this study, we develop a domain decomposition approach for large-scale gas hydrate simulations with coarse-granularity parallel computation. This approach partitions a simulation domain into small subdomains. The full model domain, consisting of discrete subdomains, is still simulated simultaneously by using multiple processes/processors. Each processor is dedicated to following tasks of the partitioned subdomain: updating thermophysical properties, assembling mass- and energy-balance equations, solving linear equation systems, and performing various other local computations. The linearized equation systems are solved in parallel with a parallel linear solver, using an efficient interprocess communication scheme. This new domain decomposition approach has been implemented into the TOUGH+HYDRATE code and has demonstrated excellent speedup and good scalability. In this paper, we will demonstrate applications for the new approach in simulating field-scale models for gas production from gas-hydrate deposits.

Numerical simulation has been used, as common practice, to estimate the CO2 storage capacity of depleted reservoirs. However, this method is time consuming, expensive and requires detailed input data. This investigation proposes an analytical method to estimate the ultimate CO2 storage in depleted oil and gas reservoirs by implementing a volume constrained thermodynamic equation of state (EOS) using the reservoir?s average pressure and fluid composition. This method was implemented in an algorithm which allows fast and accurate estimations of final storage, which can be used to select target storage reservoirs, and design the injection scheme and surface facilities. Impurities such as nitrogen and carbon monoxide, usually contained in power plant flue gases, are considered in the injection stream and can be handled correctly in the proposed algorithm by using their thermodynamic properties into the EOS. Results from analytical method presented excellent agreement with those from reservoir simulation. Ultimate CO2 storage capacity was predicted with an average difference of 1.3%, molar basis, between analytical and numerical methods; average oil, gas, and water saturations were also matched. Additionally, the analytical algorithm performed several orders of magnitude faster than numerical simulation, with an average of 5 seconds per run.

Reservoir Simulation is a very powerful tool used in the Oil and Gas industry to perform and provide various functions including but not limited to predicting reservoir performance, conduct sensitivity analysis to quantify uncertainty, production optimization and overall reservoir management. Compared to explored reservoirs in the past, current day reservoirs are more complex in extent and structure. As a result, reservoir simulators and algorithms used to represent dynamic systems of flow in porous media have invariably got just as complex. In order to provide the best solutions for analyzing reservoir performance, there is a need to continuously develop reservoir simulators and reservoir simulation algorithms that best represent the performance of the reservoir without compromising efficiency and accuracy. There exists several commercial reservoir simulation packages in the market that have been proven to be extremely resourceful with functionality that covers a wide range of interests in reservoir simulation yet there is the constant need to provide better and more efficient methods and algorithms to study and manage our reservoirs. This thesis aims at bridging the gap in the framework for developing these algorithms. To this end, this project has both an educational and research component. Educational because it leads to a strong understanding of the topic of reservoir simulation for students which can be daunting especially for those who require a more direct experience to fully comprehend the subject matter. It is research focused because it will serve as the foundation for developing a framework for integrating custom built external simulators and algorithms with the workflow of the model builder of our reservoir simulation package of choice i.e. Petrel with the Ocean programming environment in a seamless manner for simulating large scale multi-physics problems of flow in highly heterogeneous flow of porous media. Of particular interest are the areas of model order reduction and production optimization. In-house algorithms are being developed for these areas of interest and with the completion of this project. We hope to have developed a framework whereby we can take our algorithms specifically developed for areas of interest and add them to the workflow of the Petrel Model Builder. Currently, we have taken one of our in-house simulators i.e. a two dimensional, oil-water five-spot water flood pattern as a starting point and have been able to integrate it successfully into the “Define Simulation Case” process of Petrel as an additional choice for simulation by an end user. In the future, we will expand this simulator with updates to improve its performance, efficiency and extend its capabilities to incorporate areas of research interest.

Seismic data have been established as a valuable source of information for the construction of reservoir simulation models, most commonly for determination of the modeled geologic structure, and also for population of static petrophysical properties (e.g. porosity, permeability). More recently, the availability of repeated seismic surveys over the time scale of years (i.e., 4D seismic) has shown promising results for the qualitative determination of changes in fluid phase distributions and pressure required for determination of areas of bypassed oil, swept volumes and pressure maintenance mechanisms. Quantitatively, and currently the state of the art in reservoir model characterization, 4D seismic data have proven distinctively useful for the calibration of geologic spatial variability which ultimately contributes to the improvement of reservoir development and management strategies. Among the limited variety of techniques for the integration of dynamic seismic data into reservoir models, streamline-based techniques have been demonstrated as one of the more efficient approaches as a result of their analytical sensitivity formulations. Although streamline techniques have been used in the past to integrate time-lapse seismic attributes, the applications were limited to the simplified modeling scenarios of two-phase fluid flow and invariant streamline geometry throughout the production schedule. This research builds upon and advances existing approaches to streamline-based seismic data integration for the inclusion of both production and seismic data under varying field conditions. The proposed approach integrates data from reservoirs under active reservoir management and the corresponding simulation models can be constrained using highly detailed or realistic schedules. Fundamentally, a new derivation of seismic sensitivities is proposed that is able to represent a complex reservoir evolution between consecutive seismic surveys. The approach is further extended to manage compositional reservoir simulation with dissolution effects and gravity-convective-driven flows which, in particular, are typical of CO2 transport behavior following injection into deep saline aquifers. As a final component of this research, the benefits of dynamic data integration on the determination of swept and drained volumes by injection and production, respectively, are investigated. Several synthetic and field reservoir modeling scenarios are used for an extensive demonstration of the efficacy and practical feasibility of the proposed developments.

[pt] A exploração crescente de campos de petróleo desafiadores é acompanhada por uma também crescente preocupação pública e de companhias petrolíferas em relação a questões ambientais e de segurança. Estudos dos principais acidentes recentes relacionados a exploração de hidrocarbonetos indicam que análises geomecânicas aprofundadas podem ser a chave para prevenir tais ocorrências. Efeitos geomecânicos podem ser muito relevantes durante análises de reservatórios. Há diversas possibilidades para considerar esses efeitos, mas a análise acoplada iterativa tem mostrado ser uma das melhores soluções, pois apresenta resultados precisos em um período de tempo computacional viável. O grupo de pesquisa PUC-Rio/GTEP tem desenvolvido um programa de acoplamento que gerencia o simulador de fluxo (IMEX ou Eclipse) e o programa de elementos finitos (Abaqus ou uma solução em GPU mais rápida chamada Chronos), de uma forma interativa. O referido programa fornece uma solução abrangente para geomecânica de reservatórios. No entanto, a geração de malha, a preparação de dados e a avaliações de resultados são barreiras para a sua aplicação na rotina de trabalho da indústria. Esta dissertação apresenta a elaboração de um fluxo de trabalho desenvolvido em um modelador geológico para aplicar a simulação acoplada de fluxo-tensão para reservatórios reais de hidrocarbonetos. Este fluxo de trabalho permite de forma simples e direta a geração de malha de elementos finitos, a definição de parâmetros mecânicos, supervisão da execução da solução acoplada e, por fim, a avaliação dos resultados de fluxo e tensão em um mesmo ambiente de visualização. / [en] The growing exploration of challenging oil fields is followed by an increasing concern by members of the public and oil companies about environmental and safety issues. Studies of recent major accidents indicate that geomechanics analyses can be the key to prevent future incidents. Geomechanical effects can be very relevant during reservoirs analyses. Actually, there are many possibilities available to consider such effects, but iterative-coupled analysis has shown to be one of the best solutions because it presents accurate results in a feasible computational timeframe. The GTEP/PUC-Rio research group has developed a coupling program that manages both the flow simulator (IMEX or Eclipse) and the finite element solver (Abaqus or a faster in-house GPU solution called Chronos) in an interactive way. The mentioned program provides a wide-ranging solution for reservoir geomechanics. However, mesh generation, data preparation and results evaluations are bottlenecks for its application in the industry s work routine. This dissertation presents the development of a workflow included in a geological modeler to apply the coupled flow-stress for real hydrocarbon reservoir simulation. This workflow allows in a simple and direct manner the generation of a finite element mesh, the definition of mechanical parameters, the supervision of coupled solution execution and the evaluation of results (flow and stress) in a single viewing environment.