Ranking and Upscaling of Geostatistical Reservoir Models by Use of Streamline Simulation: A Field Case Study

Ates, Harun; Bahar, Asnul; Salem, Salem El-Abd; Charfeddine, Mohsen; Kelkar, Mohan

doi:10.2118/81497-pa

Cited by 47 publications

(11 citation statements)

References 16 publications

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“…In the past several decades, the geostatistical techniques have become increasingly important to build complex reservoir realizations, which can meet the demands imposed by the needs for improving reservoir management, reservoir simulation, production prediction and optimization (13,14) . In general, when adopting the geostatistical reservoir characterization, it is a common practice to generate a large number of reservoir realizations to assess the uncertainty in reservoir description and then to perform production prediction.…”

Section: Geostatistical Techniquementioning

confidence: 99%

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Chen

Yang

2010

Journal of Canadian Petroleum Technology

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CO2 flooding has gained momentum in the oil and gas industry and might be suitable for approximately 80% of oil reservoirs worldwide based on the oil recovery criteria alone. In addition to miscibility, production performance needs to be optimized to achieve higher sweep efficiency and oil recovery. Although many techniques have been made available for production optimization in the upstream oil and gas industry, it is still a challenging task to optimize production performance in the presence of physical and/or financial uncertainties. In this paper, a new technique is developed to optimize production performance in a CO2 flooding reservoir under uncertainty. More specifically, potential uncertainties influencing production performance are analyzed and assessed by using the geostatistical technique. This enables us to integrate the available information within a unified and consistent framework and to generate multiple geological realizations accounting for uncertainty and spatial variability. Subsequently, the net present value (NPV) is selected as the objective function to be optimized by using the genetic algorithm, while well rates of the injectors and the flowing bottomhole pressure for the producers are chosen as the controlling variables. In addition, corresponding modifications have been made to accelerate the convergence speed of the genetic algorithm. A field case is used to demonstrate the procedures of the newly developed technique and the optimized results show that the oil recovery and the NPV can be increased by 6.4% and 9.2%, respectively. It is also found that the genetic algorithm is a powerful and reliable search method to optimize production performance of reservoirs with complex structures. Introduction CO2 flooding is considered as a promising and practical enhanced oil recovery (EOR) process because it not only increases oil recovery, but also reduces greenhouse gas emissions by sequestrating CO2 in the depleted reservoirs. In practice, CO2 flooding performance can be greatly affected by the reservoir heterogeneity, which can severely reduce the sweep efficiency, result in early CO2 breakthrough at the producers, and thus, leave a large amount of bypassed oil in the reservoir(1). Therefore, it is of fundamental and practical importance to optimize production performance of a CO2 flooding reservoir.

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Section: Geostatistical Techniquementioning

confidence: 99%

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Chen

Yang

2010

Journal of Canadian Petroleum Technology

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“…Herein, we have used a two-point-flux approximation for the spatial discretization (Aziz and Settari 1979). In particular, this means that Eq.…”

Section: Waterflood Optimizationmentioning

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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“…For this purpose, between finite difference and streamline, we choose streamline simulator, which is supposed to be faster for a larger number of grids. Another reason for choosing streamline simulation is that it has been used for ranking purposes in the industry frequently (Ates et al 2005).…”

Section: Application Of Fmm In Pressure-propagation Problemsmentioning

confidence: 99%

“…Selecting the correct static realizations, which can capture dynamic uncertainties of the models, is an important task and is an active area of research. Ballin et al (1992) proposed the concept of ranking for the first time. They proposed to select a few realizations from a large range of realizations by running a fast simulation (tracer simulation) and then conduct the comprehensive simulation (full finitedifference simulation) on those selected models to save time and to bound uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Ranking of Multiple Realizations by Use of the Fast-Marching Method

Sharifi

Kelkar

Bahar³

et al. 2014

SPE Journal

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One of the great challenges in reservoir modeling is to understand and quantify the dynamic uncertainties in geocellular models. Uncertainties in static parameters are easy to identify in geocellular models. Unfortunately, those models contain at least one to two orders of magnitude more gridblocks than typical simulation models. This means that, without significant upscaling, the dynamic uncertainties in these models cannot easily be assessed. Further, if we would like to select only a few geological models that can be carried forward for future performance predictions, we do not have an objective method of selecting the models that can properly capture the dynamic-uncertainty range.One possible solution is to use a faster simulation technique, such as streamline simulation. However, even streamline simulation requires solving a pressure equation at least once. For highly heterogeneous reservoir models with multimillion cells and in the presence of capillary effects or an expansion-dominated process, this can pose a challenge. If we use static permeability thresholds to determine the connected volume, it would not account for how tortuous the connection is between the connected gridblock and the well location.In this paper, we use the fast-marching method (FMM) as a computationally efficient method for calculating the pressure/ front propagation time on the basis of reservoir properties. This method is based on solving the Eikonal equation by use of upwind finite-difference approximation. In this method, pressure/front location (radius of investigation) can be calculated as a function of time without running any flow simulation. We demonstrate that dynamically connected volume based on pressure-propagation time is a very good proxy for ultimate recovery from a well in the primary-depletion process. With a predetermined threshold propagation time, a large number of geocellular models can be ranked. FMM can be scaled almost linearly with the number of gridblocks in the model. Two main advantages of this ranking method compared with other methods are that this method determines dynamic connectivity in the reservoir and that it is computationally much more efficient. We demonstrate the validity of the method by comparing ranking of multiple geocellular realizations (on Cartesian grid with heterogeneous and anisotropic permeability) by use of FMM with ranking from flow simulation. This method will allow us to select the geological models that can truly capture the range of dynamic uncertainty very efficiently.

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Ranking and Upscaling of Geostatistical Reservoir Models by Use of Streamline Simulation: A Field Case Study

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435.

Cited by 47 publications

References 16 publications

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

The Application of Flow Diagnostics for Reservoir Management

Dynamic Ranking of Multiple Realizations by Use of the Fast-Marching Method

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