Abstract:Flow diagnostics, as referred to herein, are computational tools derived from controlled numerical flow experiments that yield quantitative information regarding the flow behavior of a reservoir model in settings much simpler than would be encountered in the actual field. In contrast to output from traditional reservoir simulators, flow-diagnostic measures can be obtained within seconds. The methodology can be used to evaluate, rank, and/or compare realizations or strategies, and the computational speed makes … Show more
“…They proposed application of flow-capacity/storage-capacity F-; diagram, Lorenz coefficient. Moyner et al (2014) used flow diagnostics for reservoir management. They used Lorenz coefficient as the popular measure of heterogeneity in the context of streamline.…”
In recent years, improving oil recovery (IOR) has become an important subject for the petroleum industry. One IOR method is infill drilling, which improves hydrocarbon recovery from virgin zones of the reservoir. Determining the appropriate location for the infill wells is very challenging and greatly depends on different factors such as the reservoir heterogeneity. This study aims to investigate the effect of reservoir heterogeneity on the location of infill well. In order to characterize the effect of heterogeneity on infill well locations, some geostatistical methods, e.g., sequential gaussian simulation, have been applied to generate various heterogeneity models. In particular, different correlation ranges (R) were used to observe the effect of heterogeneity. Results revealed that an increase in correlation ranges will lead to (1) a higher field oil production total, and (2) a faster expansion of the drainage radius which consequently reduced the need for infill wells. The results of this study will help engineers to appropriately design infill drilling schemes.
“…They proposed application of flow-capacity/storage-capacity F-; diagram, Lorenz coefficient. Moyner et al (2014) used flow diagnostics for reservoir management. They used Lorenz coefficient as the popular measure of heterogeneity in the context of streamline.…”
In recent years, improving oil recovery (IOR) has become an important subject for the petroleum industry. One IOR method is infill drilling, which improves hydrocarbon recovery from virgin zones of the reservoir. Determining the appropriate location for the infill wells is very challenging and greatly depends on different factors such as the reservoir heterogeneity. This study aims to investigate the effect of reservoir heterogeneity on the location of infill well. In order to characterize the effect of heterogeneity on infill well locations, some geostatistical methods, e.g., sequential gaussian simulation, have been applied to generate various heterogeneity models. In particular, different correlation ranges (R) were used to observe the effect of heterogeneity. Results revealed that an increase in correlation ranges will lead to (1) a higher field oil production total, and (2) a faster expansion of the drainage radius which consequently reduced the need for infill wells. The results of this study will help engineers to appropriately design infill drilling schemes.
“…In this step we assume unit mobility ratio, which results in a linear flow equation. We note that other simulation-based approaches such as flow diagnostics [31,30] could also be used in this step, though it is not clear that such approaches would provide problems amenable to global optimization.…”
Section: First Subproblem: Optimization Of Fmentioning
confidence: 99%
“…Flow diagnostics tools [29,30], typically constructed based on reduced-physics simulations, rep-resent another effective set of surrogate treatments that can be used for optimization. For example, Møyner et al [31] performed well control optimization for systems involving two-and three-phase flow. Lie et al [30] used quantities derived from TOF and multiscale methods as fast proxies for well control optimization under waterflood.…”
Large numbers of flow simulations are typically required for the determination of optimal well settings. These simulations are often computationally demanding, which poses challenges for the optimizations. In this paper we present a new two-step surrogate treatment (ST) that reduces the computational expense associated with well control optimization. The method is applicable for oil production via waterflood, with well rates optimized at a single control period. The two-step ST entails two separate optimizations, which can both be performed very efficiently. In the first optimization, optimal well-rate ratios (i.e., the fraction of total injection or production associated with each well) are determined such that a measure of velocity variability over the field is minimized, leading to more uniform sweep. In the second step, overall injection and production rates are determined. The flow physics in the first step is highly simplified, while the actual physical system is simulated in the second step. Near-globally-optimal results can be determined in both cases, as the first optimization is posed as a QP problem, and the second step entails just a single optimization variable. Under full parallelization, the overall elapsed time for the ST corresponds to the runtime for 1-2 full-order simulations. Results are presented for multiple well configurations, for 2D and 3D channelized models, and comparisons with formal optimization procedures (mesh adaptive direct search or MADS, and an adjoint-gradient method) are conducted. Three different fluid mobility ratios (M = 1, 3 and 5) are considered. Optimization results demonstrate that the two-step ST provides results in reasonable agreement with those from MADS and adjoint-gradient methods, with speedups of 5× or more. We also show that the ST is applicable in the inner-loop in field development optimization, where it will be especially useful since many different well configurations must be evaluated.
“…Therefore, the search direction is not limited to the eight directions of an initial well position guessed. Møyner et al (2014) applied flow diagnostics to an existing well positioning method, so that both production rates and well position could be optimized simultaneously.…”
Water flooding uses water injection to increase oil production from a reservoir. In water flooding, the position of production and injection wells is one of key factors for high oil recovery. However, well positioning is typically planned with lack of geology information. Therefore, it is difficult to decide an optimal well position due to geological heterogeneity and uncertainty. This paper proposes a method to optimize the position of an injection well, thereby maximizing sweep efficiency by equalizing arrival times of the injected water. After estimating average watercut of all the producing wells from an injection well assumed, timeshifts of individual watercut are calculated to match the average watercut. Sensitivity is computed by using the Buckley-Leverett solution. Many equivalent reservoir models are generated and applied to consider geological uncertainty. The arrival time optimization proposed in this study can find the location of an optimal injection well, even if initial conditions are far from the solution or multiple equi-probable models considered at the same time. Also, a lot of simulation time can be saved because of fast convergence and analytical sensitivity computations. The suggested method is applicable to a reservoir that has geological uncertainty because of its calculation efficiency.
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