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Fluvial sands with discontinuous interbeds and highly variable permeability patterns pose a significant challenge for further development of high water-cut oil fields with bottom aquifer in Sudan. To obtain an optimal strategy for further infill drilling a multi-disciplinary study of reservoir characterization and performance prediction was undertaken.Data from vertical observation wells and deviated wells, combined with seismic horizon data and inversion data were used to characterize the spatial distribution of reservoir architecture and interbeds. Petrophysical properties (porosity and permeability) models were then stochastically simulated under such reservoir frameworks.To avoid the distorted or zig-zag grids, vertical stair-step faulting upscaling method was taken to ensure that cells were orthogonal. Fine grids where interbeds most often occured were kept during upscaling to serve as vertical fluid flow baffle. The accurate coarse model that provided a realistic understanding of the lateral variation and distribution of reservoirs in interwell areas were then taken as the base to effectively develop reservoirs.During history matching, some scenarios were tested as alternatives in pursuit of a history match. The history matching procedure revealed the significance of interbeds as vertical prevention of water coning and permeability streaks as conduits to effectively drain reserves. Performance predictions for infill well cases are carried out based on the remaining oil distribution. The infill wells are optimized based on the incremental oil rate which can improve the cumulative oil production and recovery factor.
Fluvial sands with discontinuous interbeds and highly variable permeability patterns pose a significant challenge for further development of high water-cut oil fields with bottom aquifer in Sudan. To obtain an optimal strategy for further infill drilling a multi-disciplinary study of reservoir characterization and performance prediction was undertaken.Data from vertical observation wells and deviated wells, combined with seismic horizon data and inversion data were used to characterize the spatial distribution of reservoir architecture and interbeds. Petrophysical properties (porosity and permeability) models were then stochastically simulated under such reservoir frameworks.To avoid the distorted or zig-zag grids, vertical stair-step faulting upscaling method was taken to ensure that cells were orthogonal. Fine grids where interbeds most often occured were kept during upscaling to serve as vertical fluid flow baffle. The accurate coarse model that provided a realistic understanding of the lateral variation and distribution of reservoirs in interwell areas were then taken as the base to effectively develop reservoirs.During history matching, some scenarios were tested as alternatives in pursuit of a history match. The history matching procedure revealed the significance of interbeds as vertical prevention of water coning and permeability streaks as conduits to effectively drain reserves. Performance predictions for infill well cases are carried out based on the remaining oil distribution. The infill wells are optimized based on the incremental oil rate which can improve the cumulative oil production and recovery factor.
The weakest characteristic of most scaled reservoir models is the incompetence of describing accurately the reservoir heterogeneity. This limitation has a strong impact in the adequate match of existing historical data, usually resulting in unrealistic predictions. The scaled reservoir model validation is a fundamental step toward developing a successful reservoir simulation model. The results achieved from an imprecise simulation model can be more harmful for reservoir management decisions than not having a simulation model at all. In this paper we present a quick and efficient application designed to meet specific needs to preserve, in scaled models, the reservoir heterogeneities represented in high-resolution geostatistical realizations. The developed tool works in a systematized architecture where multi-attributes as geological, petrophysical and reservoir objects are stored in a dynamic hierarchical platform. The validation procedure, which works as a 3D visual interactive platform, includes optimization methods and transmissibility definitions using vertical windows. We have applied the proposed tool to Dina Cretaceos and Palogrande Fields, Valle Superior del Magdalena Basin in Colombia, South America. The Dina Cretaceos Field is estimated to contain over 180 million barrels of oil in place of which 26% has been produced. The Palogrande Field is estimated to contain over 230 million barrels of oil in place with a recovery of 20.5 %. Departing from high-resolution geostatistical reservoir images, scaled models for simulation purposes were built up and validated with our tool. The flow capacity tuning procedure, a strong stratigraphic constraining, and a detailed control of sand to sand connections reduced the risk of oversimplifying the scaling up process. Therefore, a much better reservoir heterogeneity representation was achieved and a suitable history matching were possible without manipulating, deforming, or even losing the physical sense of the model. Introduction Increased resolution in reservoir characterization is currently driving the need for efficient and accurate upscaling techniques to correctly model reservoir heterogeneities and avoid unrealistic predictions of fluid flow in scaled models. A comprehensive review of published upscaling methods can be found in the literature.1–9 Upscaling is a technique that transforms a detailed geologic model to a coarse-grid simulation model in such a way the fluid-flow behavior in the coarse model can be duplicated.10 This is a non-trivial step in the process of preserving the geological features of the reservoir.11–16 An accurate upscaling consists of two inseparable parts: gridding and averaging. The gridding procedure points towards capturing the global geologic features in terms of flow units, constrained by the major stratigraphic reservoir characteristics. For this purpose, the concept of Vertical Proportion Curve (VPC) can be used to define the minimal number of grid units needed to keep the vertical and areal development of the facies involved. The averaging procedure aims to assign the appropriate grid blocks petrophysical properties to be used in the reservoir simulation. In our case, we use a hybrid approach to minimize the impact of upscaling the reservoir heterogeneity. This approach takes into account the geological features to apply suitable averages. The final upscaled model is validated against the high-resolution model by tuning the well flow capacities and defining the convenient transmissibilities using vertical windows. Facing the stated problem demands a solid environment. In the last decade, object-oriented programming has gained popularity due to real-world attributes can be easily represented using object-oriented applied software. The code functionality is expressed by variables and methods implemented within each object, fitting the requirements needed to model abstract concepts. This environment can be perfectly used to solve the upscaling issue. In this paper, we develop a static data integration methodology implemented into a dynamic hierarchical platform to overcome this problem. The methodology includes merging segments of static data from reservoir modeling outputs into a set of objects to represent the numerical reservoir model prior to simulation.
Summary There have been many different approaches to quantifying cutoffs, with no single method emerging as the definitive basis for delineating net pay. Yet each of these approaches yields a different reservoir model, so it is imperative that cutoffs be fit for purpose (i.e., they are compatible with the reservoir mechanism and with a systematic methodology for the evaluation of hydrocarbons in place and the estimation of ultimate hydrocarbon recovery).These different requirements are accommodated by basing the quantification of cutoffs on reservoir-specific criteria that govern the storage and flow of hydrocarbons. In so doing, particular attention is paid to the relationships between the identification of cutoffs and key elements of the contemporary systemic practice of integrated reservoir studies. The outcome is a structured approach to the use of cutoffs in the estimation of ultimate hydrocarbon recovery. The principal benefits of a properly conditioned set of petrophysical cutoffs are a more exact characterization of the reservoir with a better synergy between the static and dynamic reservoir models, so that an energy company can more fully realize the asset value. Introduction In a literal sense, cutoffs are simply limiting values. In the context of integrated reservoir studies, they become limiting values of formation parameters. Their purpose is to eliminate those rock volumes that do not contribute significantly to the reservoir evaluation product. Typically, they have been specified in terms of the physical character of a reservoir. If used properly, cutoffs allow the best possible description and characterization of a reservoir as a basis for simulation. Yet, although physical cutoffs have been used for more than 50 years, there is still no rationalized procedure for identifying and applying them. The situation is compounded by the diverse approaches to reservoir evaluation that have been taken over that period, so that even the role of cutoffs has been unclear. These matters assume an even greater poignancy in contemporary integrated reservoir studies, which are systemic rather than parallel or sequential in nature, so that all components of the evaluation process are interlinked and, therefore, the execution of anyone of these tasks has ramifications for the others (Fig. 1). A particular aspect of the systemic approach is the provision for iteration as the reservoir knowledge-base advances. For example, simulation may be used in development studies to identify the most appropriate reservoir-depletion mechanism, but, once the development plan has been formulated, the dynamic model is retuned and progressively updated as development gets under way. The principal use of cutoffs is to delineate net pay, which can be described broadly as the summation of those depth intervals through which hydrocarbons are (economically) producible. In the context of integrated reservoir studies, net pay has an important role to play both directly and through a net-to-gross pay ratio. Net pay demarcates those intervals around a well that are the focus of the reservoir study. It defines an effective thickness that is pertinent to the identification of hydrocarbon flow units, that identifies target intervals for well completions and stimulation programs, and that is needed to estimate permeability through the analysis of well-test data. The net-to-gross pay ratio is input directly to volumetric computations of hydrocarbons in place and thence to "static" estimates of ultimate hydrocarbon recovery; it is a key indicator of hydrocarbon connectivity, and it contributes to the initializing of a reservoir simulator and thence to "dynamic" estimates of ultimate hydrocarbon recovery.
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