The development of fine grid geolgocial models has attracted great attention in the past decades. Meanwhile, the need for reliable upscaling and coarsening techniques is continuing. Besides the computational efficiency, upscaling can also offer other advantages. The desire for the assessment of risk and uncertainty in reservoir performance is another key issue that is attracting the researchers. Predictions are necessarily of a statistical character because uncertainty is involved in almost all the aspects of the reservoir characterization. Significantly upscaled models are desired when the full assessment of project risk and uncertainty are to be accomplished. The problem of upgridding fine scale models into the coarsened ones is still an attractive and challenging topic demanding much more effort in the reservoir simulation field. We proposed a modified static coarsening algorithm that has better performance without introducing extra computation cost. This algorithm combines adjacent layers based on static calculations such that the heterogeneity measure of a defined static property is minimized within the layers. In addition, the geological model coarsening will also rely on preserving geological marker information. This combination of static calculation and geological information enables this algorithm to generate models more closely to the true ones. The power and utility of our approaches have been demonstrated using both synthetic and field examples. To assist the optimal coarsening procedures, we developed and implemented a GUI (Graphical User Interface), named MARS. We focused on building up a C++ based user interface which enables users to handle access the upgridding simulation visually. This MARS software package is a general purpose GUI for applications that make use of graphs as an underlying data model. MARS, which allows user to create simulation cases, import and modify data, and generate graphical geological figures, is developed to facilitate the operation of this coarsening procedures and the interpretation of the results obtained by this model. The user of MARS will be graphically guided through the entire process of creating coarsening simulations.

The development of coarsened reservoir simulation models from high resolution geologic models is a critical step in a simulation study. The optimal coarsening sequence becomes particularly challenging in a fluvial channel environment where the channel sinuosity and orientation can result in pay/non-pay juxtaposition in many regions of the geologic model. The optimal coarsening sequence is also challenging in tight gas sandstones where sharp changes between sandstone and shale beds are predominant and maintaining the pay/non-pay distinction is difficult. Under such conditions, a uniform coarsening will result in mixing of pay and non-pay zones and will likely result in geologically unrealistic simulation models which create erroneous performance predictions. In particular, the upgridding algorithm must keep pay and non-pay zones distinct through a non-uniform coarsening of the geologic model. We present a coarsening algorithm to determine an optimal reservoir simulation grid by grouping fine scale geologic model cells into effective simulation cells. Our algorithm groups the layers in such a way that the heterogeneity measure of an appropriately defined static property is minimized within the layers and maximized between the layers. The optimal number of layers is then selected based on an analysis resulting in a minimum loss of heterogeneity. We demonstrate the validity of the optimal gridding by applying our method to a history matched waterflood in a structurally complex and faulted offshore turbiditic oil reservoir. The field is located in a prolific hydrocarbon basin offshore South America. More than 10 years of production data from up to 8 producing wells are available for history matching. We demonstrate that any coarsening beyond the degree indicated by our analysis overly homogenizes the properties on the simulation grid and alters the reservoir response. An application to a tight gas sandstone developed by Schlumberger DCS is also used in our verification of our algorithm. The specific details of the tight gas reservoir are confidential to Schlumberger's client. Through the use of a reservoir section we demonstrate the effectiveness of our algorithm by visually comparing the reservoir properties to a Schlumberger fine scale model.

Tight gas reservoirs provide almost one quarter of the current U.S. domestic gas production, with significant projected increases in the next several decades in both the U.S. and abroad. These reservoirs constitute an important play type, with opportunities for improved reservoir simulation & management, such as simulation model design, well placement. Our work develops robust and efficient strategies for improved tight gas reservoir simulation and management. Reservoir simulation models are usually acquired by upscaling the detailed 3D geologic models. Earlier studies of flow simulation have developed layer-based coarse reservoir simulation models, from the more detailed 3D geologic models. However, the layer-based approach cannot capture the essential sand and flow. We introduce and utilize the diffusive time of flight to understand the pressure continuity within the fluvial sands, and develop novel adaptive reservoir simulation grids to preserve the continuity of the reservoir sands. Combined with the high resolution transmissibility based upscaling of flow properties, and well index based upscaling of the well connections, we can build accurate simulation models with at least one order magnitude simulation speed up, but the predicted recoveries are almost indistinguishable from those of the geologic models. General practice of well placement usually requires reservoir simulation to predict the dynamic reservoir response. Numerous well placement scenarios require many reservoir simulation runs, which may have significant CPU demands. We propose a novel simulation-free screening approach to generate a quality map, based on a combination of static and dynamic reservoir properties. The geologic uncertainty is taken into consideration through an uncertainty map form the spatial connectivity analysis and variograms. Combining the quality map and uncertainty map, good infill well locations and drilling sequence can be determined for improved reservoir management. We apply this workflow to design the infill well drilling sequence and explore the impact of subsurface also, for a large-scale tight gas reservoir. Also, we evaluated an improved pressure approximation method, through the comparison with the leading order high frequency term of the asymptotic solution. The proposed pressure solution can better predict the heterogeneous reservoir depletion behavior, thus provide good opportunities for tight gas reservoir management.

La plupart des méthodes pour la modélisation des réservoirs fracturés reposent sur le modèle de Warren et Root (1963). Mais ce modèle reste limité par : l'hypothèse d'un volume élémentaire représentatif, l'évaluation des transferts matrice-fractures, l’idéalisation du système fracturé, l'emploi de conditions aux limites locales. La méthode développée répond à ces quatre points. Un réseau de fractures et une grille de matrice sont discrétisés conjointement, puis un changement d'échelle est réalisé. Un ensemble de nœuds représentatifs est sélectionné, et un système simplifié équivalent est construit par décimation des autres nœuds en assurant la conservation des pressions et des débits sans imposer de conditions aux limites. Enfin le nombre de connexions est réduit et les transmissibilités restantes sont calculées par une procédure d'optimisation. Ces systèmes simplifiés ont été résolus plus rapidement lors de simulations d’écoulement tout en reproduisant le comportement du modèle fin / Most methods for modeling fractured reservoirs rely on the model of Warren and Root (1963). But this model is limited by: the assumption of a representative elementary volume, the evaluation of matrix-fracture transfers, the idealization of the fractured system, the use of local boundary conditions. The developed method overcomes these four points. A fracture network and a matrix grid are jointly discretized, then an upscaling is carried out. A set of representative nodes is selected, and an equivalent simplified system is built by decimating the other nodes while ensuring the preservation of pressure and flow rate and without imposing any boundary conditions. Finally the number of connexions is reduced and the remaining transmissibilities are evaluated by an optimization procedure. These simplified systems have been solved more quickly by the flow simulator while reproducing the fine model behavior

Uncertainty quantification involves sampling the reservoir parameters correctly from a posterior probability function that is conditioned to both static and dynamic data. Rigorous sampling methods like Markov Chain Monte Carlo (MCMC) are known to sample from the distribution but can be computationally prohibitive for high resolution reservoir models. Approximate sampling methods are more efficient but less rigorous for nonlinear inverse problems. There is a need for an efficient and rigorous approach to uncertainty quantification for the nonlinear inverse problems. First, we propose a two-stage MCMC approach using sensitivities for quantifying uncertainty in history matching geological models. In the first stage, we compute the acceptance probability for a proposed change in reservoir parameters based on a linearized approximation to flow simulation in a small neighborhood of the previously computed dynamic data. In the second stage, those proposals that passed a selected criterion of the first stage are assessed by running full flow simulations to assure the rigorousness. Second, we propose a two-stage MCMC approach using response surface models for quantifying uncertainty. The formulation allows us to history match three-phase flow simultaneously. The built response exists independently of expensive flow simulation, and provides efficient samples for the reservoir simulation and MCMC in the second stage. Third, we propose a two-stage MCMC approach using upscaling and non-parametric regressions for quantifying uncertainty. A coarse grid model acts as a surrogate for the fine grid model by flow-based upscaling. The response correction of the coarse-scale model is performed by error modeling via the non-parametric regression to approximate the response of the computationally expensive fine-scale model. Our proposed two-stage sampling approaches are computationally efficient and rigorous with a significantly higher acceptance rate compared to traditional MCMC algorithms. Finally, we developed a coarsening algorithm to determine an optimal reservoir simulation grid by grouping fine scale layers in such a way that the heterogeneity measure of a defined static property is minimized within the layers. The optimal number of layers is then selected based on a statistical analysis. The power and utility of our approaches have been demonstrated using both synthetic and field examples.