Unsteady-state (USS) core flood experiments provide data for deriving twophase relative permeability and capillary pressure functions. The experimental data is uncertain due to measurement errors, and the accuracy of the derived flow functions is limited by both data and modeling errors.History matching provides a reasonable means of deriving in-phase flow functions from uncertain unsteady-state experimental data. This approach is preferred to other analytical procedures, which involve data smoothing and differentiation. Data smoothing leads to loss of information while data differentiation is a mathematically unstable procedure, which could be error magnifying. The problem is non-linear, inverse and ill posed. Hence the history-matching procedure gives a non-unique solution.This paper presents a procedure for quantifying the uncertainty in two-phase flow functions, using unsteady-state experimental data. We validate the methodology using synthetic data.We investigate the impact of uncertain flow functions on a homogeneous reservoir model using the Buckley-Leverett theory. Using a synthetic, heterogeneous reservoir model, we estimate the uncertainty in oil recovery efficiency due to uncertainty in the flow functions.
In the early stages of field development, there are many uncertainties and the current trend is to generate coarse-scale models so that many simulations can be run rapidly in order to determine the main sensitivities in a model. However, at a later stage, more data are available and there is less uncertainty in the model, so a more detailed modelling approach is desirable. In this paper, we discuss two modelling procedures which are suitable for different stages of the life of a field, and address the problem of upscaling at each stage. First, we consider a novel approach for generating coarse-scale models for evaluating uncertainty. When there is a large amount of uncertainty, the generation of fine-scale models is too time-consuming, and upscaling them by large factors may produce large errors. We demonstrate an alternative approach for modelling at a coarse scale, while preserving the heterogeneity of the fine-scale distribution, in such a way as to reproduce the fine-scale flow results. Secondly, we focus on more detailed geological models, generated at a later stage of field development. These models may comprise millions of grid cells, and may be highly heterogeneous. Such models require upscaling, but traditional methods may be very inaccurate. We have developed a method for upscaling using well-drive boundary conditions. Tests of this method show that it can reliably reproduce the fine-scale recovery in a range of models.
Latest technological developments and applications made optimal control methods usage in optimal well placement in intelligent fields practical and beneficial to increase the production. Effective usage of these methods strongly depends on the detailed evaluation of the economic view and performance in reservoirs that have high uncertainty, particularly. There are several methods of optimization of well placement ranging from classical reservoir engineering to derivative-free and hybrid methods. TNO's Olympus model used globally as a benchmark model in ISAAP-2 Challenge in used. Geological modeling software is coupled with the commercial full-physics reservoir simulator as well as the optimization software in order to produce different geological realizations to represent the geological uncertainty and run the simulation model with differing inputs of optimization and uncertainty in a loop. Results are outlined in detail in a comparative way including comparison to the previous study to illustrate the challenges and benefits of smart wells and optimization of placement of them in intelligent fields. Results indicate that classical reservoir engineering principles still prove useful in the beginning of the optimization process. Then, derivative-free and hybrid methods introduce significant improvement on economics. There are certain challenges in CPU requirements however the state-of-the-art facilities provided significant reduction in runtimes along with the help of the hybrid methods where proxies are built and used for faster runtimes. Despite higher initial capital expenses, smart wells provide significant advantages in recovery and economics compared to that of the conventional wells where these is less control on the production/injection at the layer level. Literature lacks a comprehensive study that takes into account the optimization of well placement in smart fields focusing on smart wells and the all major available methods for optimization. This study closes that gap providing a strong reference building on top of the previous study extending it to intelligent fields which are becoming very common and useful in oil and gas industry in conventional and unconventional applications.
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