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Diagenesis is rarely accounted for in the standard modeling workflows for carbonate reservoirs, although it has a huge impact on both porosity and permeability. This can be explained by at least two reasons: first, it is difficult to quantify the influence of diagenetic overprints on porosity and permeability; second, the integration of the diagenetic effects in carbonate reservoir models makes history matching much more difficult. Herein, a modeling methodology is proposed, in which the diagenetic imprints are included in the reservoir model and calibrated with dynamic data. The key point consists in defining a parametrization technique able to capture these diagenetic imprints. We assume that distinct regions of occurrence of a given diagenetic phase can be identified within the reservoir. Therefore, restricting our attention to a facies, we may distinguish regions characterized by low, medium or high proportions of the targeted diagenetic phase. The advantage of this parametrization technique is that the proportions of these regions can be easily driven by a reduced number of proportionality coefficients. Then, the overall modeling approach is integrated in an optimization workflow making it possible to vary the proportions of the region with a given occurrence for a given diagenetic phase, the variograms characterizing the spatial distribution of the regions, or even the way they are spatially distributed. The optimization process is run to adjust these various unknown parameters in order to match production history. The potential of the proposed methodology is finally investigated through the study of a two-dimensional numerical example.

Gravity drainage is one of the essential recovery mechanisms in naturally fractured reservoirs. Several mathematical formulas have been proposed to simulate the drainage process using the dual-porosity model. Nevertheless, they were varied in their abilities to capture the real saturation profiles and recovery speed in the reservoir. Therefore, understanding each mathematical model can help in deciding the best gravity model that suits each reservoir case. Real field data from a naturally fractured carbonate reservoir from the Middle East have used to examine the performance of various gravity equations. The reservoir represents a gas&ndash, oil system and has four decades of production history, which provided the required mean to evaluate the performance of each gravity model. The simulation outcomes demonstrated remarkable differences in the oil and gas saturation profile and in the oil recovery speed from the matrix blocks, which attributed to a different definition of the flow potential in the vertical direction. Moreover, a sensitivity study showed that some matrix parameters such as block height and vertical permeability exhibited a different behavior and effectiveness in each gravity model, which highlighted the associated uncertainty to the possible range that often used in the simulation. These parameters should be modelled accurately to avoid overestimation of the oil recovery from the matrix blocks, recovery speed, and to capture the advanced gas front in the oil zone.

Diagenesis refers to all the physicochemical processes that transform sediments. In the Mississippian Limestone, the rocks have been submitted to a complex geological and diagenetic history that makes its hydrocarbon exploitation difficult. Now, the oil and gas industry faces important challenges to keep production plateau and identify remaining productive areas. Therefore, the characterization and interpretation of diagenetic processes are crucial in order to understand the evolution and distribution of porosity and permeability properties. Moreover, diagenesis is rarely taken into account in the standard modeling of carbonate reservoirs. This can be explained by the difficulties in the modeling of diagenetic overprints and the complexity added in the historical adjustment of production data. This work addresses the diagenesis characterization and modeling of carbonate rocks, applied to a core extracted from the Mississippian Limestone. And an innovative methodology is proposed to incorporate the diagenetic phases inside the geological model as part of the modeling and the history-matching workflow., La diagenèse fait référence à tous les processus physico-chimiques qui transforment les sédiments. Dans le calcaire du Mississippien, les roches ont été soumises à une histoire géologique et diagénétique complexe qui rend leur exploitation d'hydrocarbures difficile. Maintenant, l'industrie pétrolière et gazière fait face à d'importants défis pour maintenir le plateau de production et identifier les zones productives restantes. Par conséquent, la caractérisation et l'interprétation des processus diagénétiques sont cruciales pour comprendre l'évolution et la distribution des propriétés de porosité et de perméabilité. De plus, la diagenèse est rarement prise en compte dans la modélisation standard des réservoirs carbonatés. Ceci peut s'expliquer par les difficultés dans la modélisation des surimpressions diagénétiques et la complexité ajoutée dans l'ajustement historique des données de production. Ce travail porte sur la caractérisation diagénétique et la modélisation des roches carbonatées, appliquées à un noyau extrait du calcaire du Mississippien. Et une méthodologie innovante est proposée pour intégrer les phases diagénétiques à l'intérieur du modèle géologique dans le cadre du workflow de modélisation et de l’ajustement historique de la production.

In this work, we propose to follow a multi-scale approach for spatial reservoir properties characterization using direct (well observations) and indirect (seismic and production history) data at different resolutions. Two decompositions are used to parameterize the problem: the wavelets and the Gaussian pyramids. Using these parameterizations, we show the advantages of the multi-scale approach with two uncertainty quantification problems based on minimization. The first one concerns the simulation of property fields from a multiple points geostatistics algorithm. It is shown that the multi-scale approach based on Gaussian pyramids improves the quality of the output realizations, the match of the conditioning data and the computational time compared to the standard approach. The second problem concerns the preservation of the prior models during the assimilation of the production history. In order to re-parameterize the problem, we develop a new 3D grid adaptive wavelet transform, which can be used on complex reservoir grids containing dead or zero volume cells. An ensemble-based optimization method is integrated in the multi-scale history matching approach, so that an estimation of the uncertainty is obtained at the end of the optimization. This method is applied on several application examples where we observe that the final realizations better preserve the spatial distribution of the prior models and are less noisy than the realizations updated using a standard approach, while matching the production data equally well., Dans ce travail, nous proposons de suivre une approche multi-échelles pour simuler des propriétés spatiales des réservoirs, permettant d'intégrer des données directes (observation de puits) ou indirectes (sismique et données de production) de résolutions différentes. Deux paramétrisations sont utilisées pour résoudre ce problème: les ondelettes et les pyramides gaussiennes. A l'aide de ces paramétrisations, nous démontrons les avantages de l'approche multi-échelles sur deux types de problèmes d'estimations des incertitudes basés sur la minimisation d'une distance. Le premier problème traite de la simulation de propriétés à partir d'un algorithme de géostatistique multipoints. Il est montré que l'approche multi-échelles basée sur les pyramides gaussiennes améliore la qualité des réalisations générées, respecte davantage les données et réduit les temps de calculs par rapport à l'approche standard. Le second problème traite de la préservation des modèles a priori lors de l'assimilation des données d'historique de production. Pour re-paramétriser le problème, nous développons une transformée en ondelette 3D applicable à des grilles stratigraphiques complexes de réservoir, possédant des cellules mortes ou de volume négligeable. Afin d'estimer les incertitudes liées à l'aspect mal posé du problème inverse, une méthode d'optimisation basée ensemble est intégrée dans l'approche multi-échelles de calage historique. A l'aide de plusieurs exemples d'applications, nous montrons que l'inversion multi-échelles permet de mieux préserver les modèles a priori et est moins assujettie au bruit que les approches standards, tout en respectant aussi bien les données de conditionnement.

The research presented in this thesis aims at improving computer models that allow simulations of water, oil and gas flows in subsurface petroleum reservoirs. This is done by integrating, or assimilating, measurements into physics-bases models. In recent years petroleum technology has developed rapidly. Nowadays wells can be drilled to a depth of up to 10 km, not just vertically, but also at an angle, horizontally or with branches. Moreover, downhole valves can be installed which can be opened or closed from the surface and advanced sensors can be placed in the subsurface. This technology has the potential to drain petroleum reservoirs much more efficiently. In order to do so, the technology needs to be used sensibly, which requires adequate knowledge of subsurface physical processes. Large amounts of measurements can contribute to this, but conventional methods are often ad hoc and not suited to handle the large amounts of data that are available nowadays. Good "data assimilation" methods are very important to ensure that the growing demand for energy in the near future can be met. The objective of this thesis is to apply data assimilation techniques, invented and developed in other areas of research, to petroleum reservoir engineering, to modify them to be better suited for their new application, and to investigate how they can help to integrate both production data and seismic data to support decision-making in petroleum reservoir management.

The research presented in this thesis aims at improving computer models that allow simulations of water, oil and gas flows in subsurface petroleum reservoirs. This is done by integrating, or assimilating, measurements into physics-bases models. In recent years petroleum technology has developed rapidly. Nowadays wells can be drilled to a depth of up to 10 km, not just vertically, but also at an angle, horizontally or with branches. Moreover, downhole valves can be installed which can be opened or closed from the surface and advanced sensors can be placed in the subsurface. This technology has the potential to drain petroleum reservoirs much more efficiently. In order to do so, the technology needs to be used sensibly, which requires adequate knowledge of subsurface physical processes. Large amounts of measurements can contribute to this, but conventional methods are often ad hoc and not suited to handle the large amounts of data that are available nowadays. Good "data assimilation" methods are very important to ensure that the growing demand for energy in the near future can be met. The objective of this thesis is to apply data assimilation techniques, invented and developed in other areas of research, to petroleum reservoir engineering, to modify them to be better suited for their new application, and to investigate how they can help to integrate both production data and seismic data to support decision-making in petroleum reservoir management.

The objective of this study is to develop new methodologies to identify the spatial distribution of permeability values inside the heterogeneous core samples. We developed laboratory viscous miscible displacements by injecting high viscosity glycerin into the core samples initially saturated by low viscosity brine. The pressure drop across the samples was measured as a function of time until breakthrough. Meanwhile, CT scan measurements provided a 3D porosity map plus several 3D maps of concentration distribution inside the core samples at different times. A simple permeability mapping technique was developed deducing a one-dimensional permeability profile along the flow direction from the measured pressure drop data. The method was validated with both numerical and laboratory experiments. To go beyond one-dimensional characterization of permeability into cores, we developed an iterative process for matching pressure and concentration data. This method consisted of two steps: a simple optimization for capturing the permeability heterogeneity along the flow direction axis and a complex optimization for capturing transversal permeability heterogeneities. The methodology was validated by numerical data. It was also applied to the data collected from two laboratory viscous miscible displacements. We showed that the final 3D permeability models reproduce well the measured pressure drop and concentration data, L'objet de cette thèse est de développer des méthodologies permettant d'identifier la distribution spatiale des valeurs de perméabilité dans des échantillons de roches. Nous avons tout d'abord développé en laboratoire des expériences d'injection de fluide miscible très visqueux dans des échantillons initialement saturés par une saumure peu visqueuse. Pendant l'injection, l'évolution au cours du temps de la pression différentielle entre les deux faces de l'échantillon a été enregistrée par des capteurs de pression. En outre, des mesures scanner ont fourni une carte 3D de la porosité ainsi que des cartes 3D décrivant la distribution spatiale des concentrations dans l'échantillon à différents temps. Nous avons mis en place une méthode d'interprétation donnant directement le profil 1D de la perméabilité le long de la direction d'écoulement à partir de la pression différentielle mesurée au cours du temps. Cette méthode a été validée numériquement et expérimentalement. Puis, afin d'affiner la description de l'agencement des valeurs de perméabilité dans l'échantillon, c'est à dire d'obtenir un modèle 3D de perméabilité représentatif de l'échantillon, nous avons développé une méthodologie itérative de calage des pressions et des concentrations. Cette méthode passe par deux étapes : une optimisation simple pour capturer l'hétérogénéité dans la direction de l'écoulement et une optimisation complexe pour capturer l'hétérogénéité transverse. Cette méthode a été validée à partir de tests numériques. La méthode a été appliquée à deux des expériences d'injection de fluide visqueux. Nous avons pu alors déterminer des modèles de perméabilité capables de reproduire assez bien les données de pression et de concentration acquises pendant l'injection