Integrated reservoir modeling has become an important part of day-to-day decision analysis in oil and gas management practices. A very attractive and promising technology is the use of time-lapse or 4D seismic as an essential component in subsurface modeling. Today, 4D seismic is enabling oil companies to optimize production and increase recovery through monitoring fluid movements throughout the reservoir. 4D seismic advances are also being driven by an increased need by the petroleum engineering community to become more quantitative and accurate in our ability to monitor reservoir processes. Qualitative interpretations of time-lapse anomalies are being replaced by quantitative inversions of 4D seismic data to produce accurate maps of fluid saturations, pore pressure, temperature, among others. Within all steps involved in this subsurface modeling process, the most demanding one is integrating the geologic model with dynamic field data, including 4Dseismic when available. The validation of the geologic model with observed dynamic data is accomplished through a "history matching" (HM) process typically carried out with well-based measurements. Due to low resolution of production data, the validation process is severely limited in its reservoir areal coverage, compromising the quality of the model and any subsequent predictive exercise. This research will aim to provide a novel history matching approach that can use information from high-resolution seismic data to supplement the areally sparse production data. The proposed approach will utilize streamline-derived sensitivities as means of relating the forward model performance with the prior geologic model. The essential ideas underlying this approach are similar to those used for high-frequency approximations in seismic wave propagation. In both cases, this leads to solutions that are defined along "streamlines" (fluid flow), or "rays" (seismic wave propagation). Synthetic and field data examples will be used extensively to demonstrate the value and contribution of this work. Our results show that the problem of non-uniqueness in this complex history matching problem is greatly reduced when constraints in the form of saturation maps from spatially closely sampled seismic data are included. Further on, our methodology can be used to quickly identify discrepancies between static and dynamic modeling. Reducing this gap will ensure robust and reliable models leading to accurate predictions and ultimately an optimum hydrocarbon extraction.

Reservoir simulation models are becoming increasingly sophisticated in tandem with the rapid development of geological modeling methods. Widely used commercial simulators usually model flow through heavily faulted and structurally complex geometries with the flexibility provided by corner-point geometry. However, the nonorthogonality component present within these frameworks may compromise the solution accuracy of the model and the subsequent operational decisions involved. We propose a systematic methodology to evaluate the impact of complex gridding introducing a new streamline formulation for corner-point geometry. Based on a new time-like variable, the new formulation provides a significantly simpler and more robust development to handle the complexity in structurally demanding and faulted systems. It retains the simplicity and speed of streamline-based flow models and provides an efficient way to visualize nonorthogonal effects. Applied to various geometries showing challenging features of geology and flow, the displacement fronts obtained from streamline-derived analytic calculation identified the discrepancies characteristic between known solutions and results from two widely used commercial simulators.

We propose a rigorous approach for well drainage volume calculations in gas reservoirs based on the flux field derived from dual porosity finite-difference simulation and demonstrate its application to optimize well placement. Our approach relies on a high frequency asymptotic solution of the diffusivity equation and emulates the propagation of a 'pressure front' in the reservoir along gas streamlines. The proposed approach is a generalization of the radius of drainage concept in well test analysis (Lee 1982), which allows us not only to compute rigorously the well drainage volumes as a function of time but also to examine the potential impact of infill wells on the drainage volumes of existing producers. Using these results, we present a systematic approach to optimize well placement to maximize the Estimated Ultimate Recovery. A history matching algorithm is proposed that sequentially calibrates reservoir parameters from the global-to-local scale considering parameter uncertainty and the resolution of the data. Parameter updates are constrained to the prior geologic heterogeneity and performed parsimoniously to the smallest spatial scales at which they can be resolved by the available data. In the first step of the workflow, Genetic Algorithm is used to assess the uncertainty in global parameters that influence field-scale flow behavior, specifically reservoir energy. To identify the reservoir volume over which each regional multiplier is applied, we have developed a novel approach to heterogeneity segmentation from spectral clustering theory. The proposed clustering can capture main feature of prior model by using second eigenvector of graph affinity matrix. In the second stage of the workflow, we parameterize the high-resolution heterogeneity in the spectral domain using the Grid Connectivity based Transform to severely compress the dimension of the calibration parameter set. The GCT implicitly imposes geological continuity and promotes minimal changes to each prior model in the ensemble during the calibration process. The field scale utility of the workflow is then demonstrated with the calibration of a model characterizing a structurally complex and highly fractured reservoir.

In this study, we apply the streamline-based production data integration method to condition a multimillion cell geologic model to historical production response for a giant Saudi Arabian reservoir. The field has been under peripheral water injection with 16 injectors and 70 producers. There is also a strong aquifer influx into the field. A total of 30 years of production history with detailed rate, infill well and re-perforation schedule were incorporated via multiple pressure updates during streamline simulation. Also, gravity and compressibility effects were included to account for water slumping and aquifer support. To our knowledge, this is the first and the largest such application of production data integration to geologic models accounting for realistic field conditions. We have developed novel techniques to analytically compute the sensitivities of the production response in the presence of gravity and changing field conditions. This makes our method computationally extremely efficient. The field application takes less than 6 hours to run on a PC. The geologic model derived after conditioning to production response was validated using field surveillance data. In particular, the flood front movement, the aquifer encroachment and bypassed oil locations obtained from the geologic model was found to be consistent with field observations. Finally, an examination of the permeability changes during production data integration revealed that most of these changes were aligned along the facies distribution, particularly the 'good' facies distribution with no resulting loss in geologic realism. We also propose a novel assisted history matching procedure for finite difference simulators using streamline derived sensitivity calculations. Unlike existing assisted history matching techniques where the user is required to manually adjust the parameters, this procedure combines the rigor of finite difference models and efficiencies of streamline simulators to perform history matching. Finite difference simulator is used to solve for pressure, flux and saturations which, in turn, are used as input for the streamline simulator for estimating the parameter sensitivities analytically. The streamline derived sensitivities are then used to update the reservoir model. The updated model is then used in the finite difference simulator in an iterative mode until a significant satisfactory history match is obtained. The assisted history matching procedure has been tested for both synthetic and field examples. The results show a significant speed-up in history matching using conventional finite difference simulators.

In this thesis, we propose two novel approaches for hybrid Ensemble Kalman Filter (EnKF) to overcome limitations of the traditional EnKF. The first approach is to swap the ensemble mean for the ensemble mode estimation to improve the covariance calculation in EnKF. The second approach is a coarse scale permeability constraint while updating in EnKF. Both hybrid EnKF approaches are coupled with the streamline based Generalized Travel Time Inversion (GTTI) algorithm for periodic updating of the mean of the ensemble and to sequentially update the ensemble in a hybrid fashion. Through the development of the hybrid EnKF algorithm, the characteristics of the EnKF are also investigated. We found that the limits of the updated values constrain the assimilation results significantly and it is important to assess the measurement error variance to have a proper balance between preserving the prior information and the observation data misfit. Overshooting problems can be mitigated with the streamline based covariance localizations and normal score transformation of the parameters to support the Gaussian error statistics. The swapping mean and mode estimation approach can give us a better matching of the data as long as the mode solution of the inversion process is satisfactory in terms of matching the observation trajectory. The coarse scale permeability constrained hybrid approach gives us better parameter estimation in terms of capturing the main trend of the permeability field and each ensemble member is driven to the posterior mode solution from the inversion process. However the WWCT responses and pressure responses need to be captured through the inversion process to generate physically plausible coarse scale permeability data to constrain hybrid EnKF updating. Uncertainty quantification methods for EnKF were developed to verify the performance of the proposed hybrid EnKF compared to the traditional EnKF. The results show better assimilation quality through a sequence of updating and a stable solution is demonstrated. The potential of the proposed hybrid approaches are promising through the synthetic examples and a field scale application.

Integration of dynamic data is critical for reliable reservoir description and has been an outstanding challenge for the petroleum industry. This work develops practical dynamic data integration techniques using streamline approaches to condition static geological models to various kinds of dynamic data, including two-phase production history, interference pressure observations and primary production data. The proposed techniques are computationally efficient and robust, and thus well-suited for large-scale field applications. We can account for realistic field conditions, such as gravity, and changing field conditions, arising from infill drilling, pattern conversion, and recompletion, etc., during the integration of two-phase production data. Our approach is fast and exhibits rapid convergence even when the initial model is far from the solution. The power and practical applicability of the proposed techniques are demonstrated with a variety of field examples. To integrate two-phase production data, a travel-time inversion analogous to seismic inversion is adopted. We extend the method via a 'generalized travel-time' inversion to ensure matching of the entire production response rather than just a single time point while retaining most of the quasi-linear property of travel-time inversion. To integrate the interference pressure data, we propose an alternating procedure of travel-time inversion and peak amplitude inversion or pressure inversion to improve the overall matching of the pressure response. A key component of the proposed techniques is the efficient computation of the sensitivities of dynamic responses with respect to reservoir parameters. These sensitivities are calculated analytically using a single forward simulation. Thus, our methods can be orders of magnitude faster than finite-difference based numerical approaches that require multiple forward simulations. Streamline approach has also been extended to identify reservoir compartmentalization and flow barriers using primary production data in conjunction with decline type-curve analysis. The streamline 'diffusive' time of flight provides an effective way to calculate the drainage volume in 3D heterogeneous reservoirs. The flow barriers and reservoir compartmentalization are inferred based on the matching of drainage volumes from streamline-based calculation and decline type-curve analysis. The proposed approach is well-suited for application in the early stages of field development with limited well data and has been illustrated using a field example from the Gulf of Mexico.

Nowadays, field development strategy has become increasingly dependent on the results of reservoir simulation models. Reservoir studies demand fast and efficient results to make investment decisions that require a reasonable trade off between accuracy and simulation time. One of the suitable options to fulfill this requirement is streamline reservoir simulation technology, which has become very popular in the last few years. Streamline (SL) simulation provides an attractive alternative to conventional reservoir simulation because SL offers high computational efficiency and minimizes numerical diffusion and grid orientation effects. However, streamline methods have weaknesses incorporating complex physical processes and can also suffer numerical accuracy problems. The main objective of this research is to evaluate the numerical accuracy of the latest SL technology, and examine the influence of different factors that may impact the solution of SL simulation models. An extensive number of numerical experiments based on sensitivity analysis were performed to determine the effects of various influential elements on the stability and results of the solution. Those experiments were applied to various models to identify the impact of factors such as mobility ratios, mapping of saturation methods, number of streamlines, time step sizes, and gravity effects. This study provides a detailed investigation of some fundamental issues that are currently unresolved in streamline simulation.

Time-lapse seismic monitoring repeats 3D seismic imaging over a reservoir to map fluid movements in a reservoir. During hydrocarbon production, the fluid saturation, pressure, and temperature of a reservoir change, thereby altering the acoustic properties of the reservoir. Time-lapse seismic analysis can illuminate these dynamic changes of reservoir properties, and therefore has strong potential for improving reservoir management. However, the response of a reservoir depends on many parameters and can be diffcult to understand and predict. Numerical modeling results integrating streamline fluid flow simulation, rock physics, and ray-Born seismic modeling address some of these problems. Calculations show that the sensitivity of amplitude changes to porosity depend on the type of sediment comprising the reservoir. For consolidated rock, high-porosity models show larger amplitude changes than low porosity models. However, in an unconsolidated formation, there is less consistent correlation between amplitude and porosity. The rapid time-lapse modeling schemes also allow statistical analysis of the uncertainty in seismic response associated with poorly known values of reservoir parameters such as permeability and dry bulk modulus. Results show that for permeability, the maximum uncertainties in time-lapse seismic signals occur at the water front, where saturation is most variable. For the dry bulk-modulus, the uncertainty is greatest near the injection well, where the maximum saturation changes occur. Time-lapse seismic methods can also be applied to monitor CO2 sequestration. Simulations show that since the acoustic properties of CO2 are very different from those of hydrocarbons and water, it is possible to image CO2 saturation using seismic monitoring. Furthermore, amplitude changes after supercritical fluid CO2 injection are larger than liquid CO2 injection. Two seismic surveys over Teal South Field, Eugene Island, Gulf of Mexico, were acquired at different times, and the numerical models provide important insights to understand changes in the reservoir. 4D seismic differences after cross-equalization show that amplitude dimming occurs in the northeast and brightening occurs in the southwest part of the field. Our forward model, which integrates production data, petrophysicals, and seismic wave propagation simulation, shows that the amplitude dimming and brightening can be explained by pore pressure drops and gas invasion, respectively.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-20T12:25:34Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) documento.pdf: 6110083 bytes, checksum: e763b9e4b979081c4ada6fef0eb596a6 (MD5) / Made available in DSpace on 2017-07-20T12:25:34Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) documento.pdf: 6110083 bytes, checksum: e763b9e4b979081c4ada6fef0eb596a6 (MD5) Previous issue date: 2015-08-03 / ANP-PRH26 / Aimportânciadageomecânicaedoestudodeesquemasdeacoplamentoentreageomecânica e fluxo multifásico têm sido cada vez mais importantes e utilizados pela indústria a medida que formações cada vez mais profundas vêem sendo descobertas e exploradas. O entendimento do comportamento do estado de tensão em um reservatório permite produzir um melhor entendimento das implicações geomecânicas que ocorrem durante a fase de explotação, isso porque durante esta fase, as alterações na poro-pressão conduzem perturbações no equilíbrio mecânico afetando o estado de tensão de formações profundas, de maneira a alterar as propriedades da rocha tais como permeabilidade e porosidade. No entanto, a simulação acoplada (hidromecânica) em um grande campo heterogêneo implica na solução de equações de fluxo e mecânica, associadas a um grande número de graus de liberdade que torna esse tipo de abordagem inviável e computacionalmente cara. Neste contexto, um simulador geomecânico-linhas de fluxoé apresentado dentro de um algoritmo sequencial iterativo. Neste trabalho, aplica-se o método de elementos finitos com volume de controle para o subproblema poro-mecânico que fornece um campo de velocidade de Darcy pós-processado e a porosidade como entradas para o subproblema de transporte. Este subproblema é resolvido através do método de decomposição de operador, no qual basea-se em um esquema preditor-corretor com os passos preditor e corretor discretizados pelos esquemas baseados em tempo de vôo e volumes finitos, respectivamente. Simulações numéricas de injeção de água foram comparadas com soluções encontradas na literatura, mostrando bons resultados. Em problemas dominados pela advecção, envolvendo um reservatório naturalmente fraturado, a abordagem implementada foi capaz de predizer a distribuição do campo de saturação ao longo de toda simulação. Além disso, para avaliar a resposta geomecânica, simulações numéricas foram realizadas em um grande sistema de reservatório-rocha capeadora em uma fase de recuperação primária de hidrocarboneto, mostrou que a formulação apresentada provou ser: uma alternativa promissora para simulação hidro-geomecânica tradicional; úteis para o modelo de fluxo de redução de ordem nos casos em que o comportamento geomecânico são mais importantes do que o comportamento de fluxo e de uma ferramenta complementar para simulação geomecânica convencional. / The importance of geomechanics and the study of coupling between geomechanics and multiphase flow have been increasingly recognized and used by the industry as deeper formations are discovered and exploited. The knowledge of the state of stress in a reservoir yields a better understanding of the geomechanical implications during exploitation stage, because during the primary recovery stage, changes in pore pressure leads to perturbations inthemechanicalequilibrium,affectingthestressstateintheformationsinawaythatalters the rock properties such as permeability and porosity. However, the coupled simulation (hydromechanical) in large field heterogeneous models involves stress and flow equations solving, associated with a large number of degrees-of-freedom which becomes infeasible and computationally costly. In this context, a geomechanical-streamline simulator is presented within a iteratively coupled framework algorithm. In the present work, we applied control volume finite element method for the poromechanics subproblem which provides a Darcy velocityfieldthroughapost-processingvelocityprocedureandporosityasinputfieldstothe transportsubproblem.Suchsubproblemissolvedbymeansofanoperatorsplittingmethod, which is based on a predictor-corrector scheme with the predictor and corrector steps discretized by a time-of-flight and a finite volume based schemes, respectively. Numerical simulations of water-flooding are compared to the numerical results available in literature, showing good results. In convection-dominated problems, involving a naturally fractured reservoir, the approach was able to predict the saturation distributions for the whole simulation correctly. Furthermore, to appraisal the geomechanical response, numerical simulation was performed in a large reservoir-caprock system in a primary hydrocarbon recovery stage, showing that the formulation presented proved be: an promising alternative to traditional hydro-geomechanical simulation; useful for flow model order reduction in cases where the geomechanical behavior are more important than the flow behavior and a complementary tool for conventional geomechanical simulations.

This work presents a new approach that combines the comprehensive capabilities of the ensemble Kalman filter (EnKF) and the flow path information from streamlines to eliminate and/or reduce some of the problems and limitations of the use of the EnKF for history matching reservoir models. The recent use of the EnKF for data assimilation and assessment of uncertainties in future forecasts in reservoir engineering seems to be promising. EnKF provides ways of incorporating any type of production data or time lapse seismic information in an efficient way. However, the use of the EnKF in history matching comes with its shares of challenges and concerns. The overshooting of parameters leading to loss of geologic realism, possible increase in the material balance errors of the updated phase(s), and limitations associated with non-Gaussian permeability distribution are some of the most critical problems of the EnKF. The use of larger ensemble size may mitigate some of these problems but are prohibitively expensive in practice. We present a streamline-based conditioning technique that can be implemented with the EnKF to eliminate or reduce the magnitude of these problems, allowing for the use of a reduced ensemble size, thereby leading to significant savings in time during field scale implementation. Our approach involves no extra computational cost and is easy to implement. Additionally, the final history matched model tends to preserve most of the geological features of the initial geologic model. A quick look at the procedure is provided that enables the implementation of this approach into the current EnKF implementations. Our procedure uses the streamline path information to condition the covariance matrix in the Kalman Update. We demonstrate the power and utility of our approach with synthetic examples and a field case. Our result shows that using the conditioned technique presented in this thesis, the overshooting/undershooting problems disappears and the limitation to work with non- Gaussian distribution is reduced. Finally, an analysis of the scalability in a parallel implementation of our computer code is given.