Maximizing volumetric sweep efficiency in waterfloods with hydrocarbon F–Φ curves

Izgec, Omer; Sayarpour, Morteza; Shook, G. Michael

doi:10.1016/j.petrol.2011.05.003

Cited by 36 publications

(12 citation statements)

References 28 publications

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“…For nonuniform initial saturations, as typically seen in field models, it will usually be desirable to restrict the heterogeneity measures to, e.g., the initial hydrocarbon volumes, to improve the correlation with the true recovery factor. Variants of this approach have been suggested for the Lorenz coefficient [8,20] but have the weakness that they only consider the heterogeneity in the oil zone and not how far, measured in τ b , the oil is from a producer. Sweep efficiency, however, generalizes more naturally to individual phases.…”

Section: Sweep Efficiency and Fractional Recoverymentioning

confidence: 99%

The use of flow diagnostics to rank model ensembles

2021

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Ensembles of geomodels provide an opportunity to investigate a range of parameters and possible operational outcomes for a reservoir. Full-featured dynamic modelling of all ensemble members is often computationally unfeasible, however some form of modelling, allowing us to discriminate between ensemble members based on their flow characteristics, is required. Flow diagnostics (based on a single-phase, steady-state simulation) can provide tools for analysing flow patterns in reservoir models but can be calculated in a much shorter time than a full-physics simulation. Heterogeneity measures derived from flow diagnostics can be used as proxies for oil recovery. More advanced flow diagnostic techniques can also be used to estimate recovery. With these tools we can rank ensemble members and choose a subset of models, representing a range of possible outcomes, which can then be simulated further. We demonstrate two types of flow diagnostics. The first are based on volume-averaged travel times, calculated on a cell by cell basis from a given flow field. The second use residence time distributions, which take longer to calculate but are more accurate and allow for direct estimation of recovery volumes. Additionally we have developed new metrics which work better for situations where we have a non-uniform initial saturation, e.g., a reservoir with an oil cap. Three different ensembles are analysed: Egg, Norne, and Brugge. Very good correlation, in terms of model ranking and recovery estimates, is found between flow diagnostics and full simulations for all three ensembles using both the cell-averaged and residence time based diagnostics.

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Section: Sweep Efficiency and Fractional Recoverymentioning

confidence: 99%

The use of flow diagnostics to rank model ensembles

2021

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“…Numerical perturbation makes the switching from rate control to pressure control and vice versa more straightforward, and does not require the use of advanced optimization tools. In general, however, we would recommend the use of an adjoint formulation, possibly in combination with the generalized-reduced-gradient method, as suggested by Kraaijevanger et al (2007). This method allows for control switching between rate and BHP, depending on which constraints become active.…”

Section: Numerical Examplesmentioning

confidence: 99%

The Application of Flow Diagnostics for Reservoir Management

2014

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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 it ideal for interactive visualization output. We also consider application of flow diagnostics as proxies in optimization of reservoirmanagement work flows. In particular, by use of finite-volume discretizations for pressure, time of flight (TOF), and stationary tracers, we efficiently compute general Lorenz coefficients (and variants) that are shown to correlate well with simulated recovery. For efficient optimization, we develop an adjoint code for gradient computations of the considered flow-diagnostic measures. We present several numerical examples, including optimization of rates, well placements, and drilling sequences for two-and threephase synthetic and real field models. Overall, optimizing the diagnostic measures implies substantial improvement in simulation-based objectives. IntroductionComputational tools for reservoir modeling play a critical role in the development of strategies for optimal recovery of hydrocarbon resources. These tools can be simply viewed as a means of forecasting recovery given a set of data, assumptions, and operating constraints; (e.g., to validate alternative hypotheses about the reservoir or systematically explore different strategies for optimal recovery).By nature, reservoir modeling is an interdisciplinary exercise, and reservoir-modeling tools must be well-integrated to promote collaboration between scientists and engineers with different backgrounds. New work flows are emerging because of recent advances in static reservoir characterization and dynamic flow simulation. Modern numerical flow simulators have evolved to include more-general grids, complex fluid descriptions, flow physics, well controls, and couplings to surface facilities. These generalizations have helped to more realistically describe fluid flow in the reservoir on the time scales associated with reservoir management. Meanwhile, modern reservoir-characterization techniques have shifted away from traditional variogram-based models toward object-and feature-based models that more accurately describe real geologic structures. To quantify uncertainty in the characterization, it is necessary to generate an ensemble of reservoir models that may include one thousand or more individual realizations. Reservoir simulation is computationally demanding, and a single simulation on a full reservoir model may require from some minutes to hours or even days. Direct evaluation of multiple production scenarios on large ensembles of Earth models is therefore impractical with full-featured flow simulators, and the comp...

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“…At present, many researchers have been conducted the water-cut changing law in water-flooding oilfields, which can be roughly divided into two categories as described as theoretical formula methods and empirical formula methods [19][20][21][22]. In terms of theoretical formula methods, it can be traced back to the classical theory Buckley-Leverett equation proposed by Buckley and Leverett [23], which for the first time elaborated the water flood mechanism in detail.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for Flow-Stop Time in Deep-Water Volatile Oilfields: A Case Study of Akpo Oilfield in Niger Delta Basin

Kang

Liu

Li³

et al. 2021

Geofluids

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Due to the difference in oil and water density, the wellhead pressure continues to decrease with water-cut rising in deep-water volatile oilfields. Once it is close to the lower limit, the production well will stop flowing. This phenomenon seriously affects the production and recoverable reserves. By taking the dynamic relative permeability which can reflect the macroscopic movement of oil and water in the reservoir as an intermediate bridge, production performance has been combined with dominant reservoir factors, including reservoir structure, reservoir connectivity, and heterogeneity. By the statistical analysis of actual data, this paper clarified the quantitative relationships between dominant reservoir factors and production performance and established the refined prediction methods for production dynamics including water-cut and liquid production rate. A prediction method for the wellhead pressure was further established, and the flow-stop time of single well can be accurately predicted. The results can be used in annual production forecast and recoverable reserve evaluation. This method had been successfully applied in Akpo oilfields in the Niger Basin. The results show that the production dynamics are significantly affected by reservoir factors in deep-water turbidite sandstone reservoir and the prediction method considering reservoir factors will be much more applicable. In deep-water volatile oilfields, the flow-stop risk of the production well in middle and high water-cut stages is very great and is mainly affected by the water-cut and liquid production rate. Judging from the application effect of Akpo oilfields, this method has high prediction accuracy and can be used to guide optimization and adjustment in deep-water oilfields.

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Maximizing volumetric sweep efficiency in waterfloods with hydrocarbon F–Φ curves

Cited by 36 publications

References 28 publications

The use of flow diagnostics to rank model ensembles

The use of flow diagnostics to rank model ensembles

The Application of Flow Diagnostics for Reservoir Management

A Prediction Method for Flow-Stop Time in Deep-Water Volatile Oilfields: A Case Study of Akpo Oilfield in Niger Delta Basin

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