Accurate Representation of Near-well Effects in Coarse-Scale Models of Primary Oil Production

Nakashima, Toshinori; Durlofsky, Louis J.

doi:10.1007/s11242-009-9479-x

Cited by 15 publications

(25 citation statements)

References 32 publications

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“…Our description of the governing equations and the multiphase near-well upscaling procedure parallels and extends that presented in [29] for the oil-gas case. Some details are repeated here for completeness.…”

Section: Three-phase Flow Equationssupporting

confidence: 53%

“…Near-well effects, particularly the detailed interaction of the flow field with fine-scale permeability heterogeneity, can strongly impact the performance of such systems. In recent work [29], we developed a procedure for the coarse-scale representation of near-well effects in oil-gas systems. This approach was applied for modeling primary production, in which fluid is produced but there is no injection to maintain formation pressure.…”

Section: Introductionmentioning

confidence: 99%

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Near-well upscaling for three-phase flows

Nakashima

Durlofsky

2011

Comput Geosci

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Large-scale flow models constructed using standard coarsening procedures may not accurately resolve detailed near-well effects. Such effects are often important to capture, however, as the interaction of the well with the formation can have a dominant impact on process performance. In this work, a near-well upscaling procedure, which provides three-phase wellblock properties, is developed and tested. The overall approach represents an extension of a recently developed oil-gas upscaling procedure and entails the use of local well computations (over a region referred to as the local well model (LWM)) along with a gradient-based optimization procedure to minimize the mismatch between fine and coarse-scale well rates, for oil, gas, and water, over the LWM. The gradients required for the minimization are computed efficiently through solution of adjoint equations. The LWM boundary conditions are determined using an iterative local-global procedure. With this approach, pressures and saturations computed during a global coarse-scale simulation are interpolated onto LWM boundaries and then used as boundary conditions for the fine-scale LWM computations. In addition to extending the overall approach to the three-phase case, this work also introduces new treatments that provide improved accuracy in cases with significant flux from the gas cap into the well block. The near-well multiphase upscaling method is applied to heterogeneous reservoir models, with production from vertical and horizontal wells. Simulation results illustrate that the method is able to accurately capture key near-well effects and to provide predictions for component production rates that are in close agreement with reference fine-scale results. The level of accuracy of the procedure is shown to be significantly higher than that of a standard approach which uses only upscaled single-phase flow parameters.

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Section: Three-phase Flow Equationssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Near-well upscaling for three-phase flows

Nakashima

Durlofsky

2011

Comput Geosci

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“…Rather, EnLU acts to reproduce the results of the full flow-based approach with much less computation. For very challenging cases, global or local-global two-phase upscaling (e.g., [6,26]), along with analogous transmissibility upscaling, can provide much more accurate coarse-scale models. These more powerful methods are fully compatible with EnLU and should be incorporated into the general procedure in future work.…”

Section: Discussionmentioning

confidence: 99%

“…The ensemble level upscaling developed here represents a much more efficient alternative for generating these k * rj . If required, more accurate two-phase upscaling techniques (either global or quasi global approaches; see, e.g., [6,26]) can be applied. As noted earlier, the ensemble level upscaling procedure is not restricted to specific upscaling methods and can be applied in conjunction with any combination of single-and two-phase (flowbased) upscaling techniques.…”

Section: Upscaling In Inter-well Regionsmentioning

confidence: 99%

Statistical assignment of upscaled flow functions for an ensemble of geological models

2010

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Upscaled flow functions are often needed to account for the effects of fine-scale permeability heterogeneity in coarse-scale simulation models. We present procedures in which the required coarse-scale flow functions are statistically assigned to an ensemble of upscaled geological models. This can be viewed as an extension and further development of a recently developed ensemble level upscaling (EnLU) approach. The method aims to efficiently generate coarse-scale flow models capable of reproducing the ensemble statistics (e.g., cumulative distribution function) of finescale flow predictions for multiple reservoir models. The most expensive part of standard coarsening procedures is typically the generation of upscaled two-phase flow functions (e.g., relative permeabilities). EnLU provides a means for efficiently generating these upscaled functions using stochastic simulation. This involves the use of coarse-block attributes that are both fast to compute and correlate closely with the upscaled two-phase functions. In this paper, improved attributes for use in EnLU, namely the coefficient of variation of the fine-scale single-phase velocity field (computed during computation of upscaled absolute permeability) and the integral range of the fine-scale permeability variogram, are identified. Geostatistical simulation methods, which account for spatial correlations of Y. Chen (B) Chevron Energy Technology Company, the statistically generated upscaled functions, are also applied. The overall methodology thus enables the efficient generation of coarse-scale flow models. The procedure is tested on 3D well-driven flow problems with different permeability distributions and variable fluid mobility ratios. EnLU is shown to capture the ensemble statistics of fine-scale flow results (water and oil flow rates as a function of time) with similar accuracy to full flow-based upscaling methods but with computational speedups of more than an order of magnitude.

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“…Finally, a related topic that may be considered in future applications is the straightforward use of surrogate reservoir models as approximations to the full physics model during the whole, or parts, of the optimization procedure. Possibly, these surrogates can be upscaled variants of the original model (Nakashima and Durlofsky, 2010), or be developed by, e.g., using some of the advanced flow diagnostic tools developed by SINTEF (Lie et al, 2012).…”

Section: Cost-reducing Topics For Further Use Of Optimization Frameworkmentioning

confidence: 99%

Joint optimization of oil well placement and controls

et al. 2012

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In this thesis we optimize the drilling location and operational controls of wells in a joint manner to improve the overall development strategy for a petroleum field. In particular, in this thesis we treat the integrated problem of searching for an improved well placement configuration while also taking into account the control settings of the production and/or injector wells planned for the development of the hydrocarbon asset. In oil field development, the well placement and well control problems are commonly performed in a sequential manner. However, this type of sequential approach cannot be expected to yield optimal solutions because it relies on handling well production controls using heuristic techniques during the well placement part of the procedure. In this work, we develop a nested (joint) optimization approach that seeks to capture the interdependency between the well configuration and the associated controls during the optimization search.This thesis summarizes the development of the joint approach; from establishing the methodology while using relatively simple cases and performing thorough comparisons against sequential approaches, to further extending and finally testing the methodology using a real field case model. This progression naturally divides the work in this thesis into two parts with different research focus. The first part of this work (Chapter 2) focuses chiefly on creating proper definitions and on establishing the proposed methodology against common approaches. The second part of this thesis (Chapters 3 and 4), on the other hand, focuses mainly on applying the developed methodology within a real field case scenario involving the North Sea Martin Linge oil reservoir. The dual aim of this application work is both to further develop the methodology, and to produce and test optimization solutions that may serve as decision-support to engineering efforts within the development work process of the Martin Linge field.Chapter 2 establishes the core of the methodology followed in this thesis. This chapter introduces the joint and sequential approaches as different ways to solve for the coupled well placement and control problem. The joint approach embeds the well control optimization within the search for optimum well placement configurations. Derivativefree methods based on pattern search are used to solve for the well-positioning part of the problem, while the well control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Compared to reasonable sequential approaches, the joint optimization yields a significant increase in net present value of up to 20%. Compared to the sequential procedures, though, the joint approach requires about an order of magnitude increase in the total number of reservoir simulations performed during optimization. This increase, however, is somewhat mitigated by i the parallel implementation of some of the pattern search algorithms used in this work.Chapter 3 focuses on extending and applying ...

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Accurate Representation of Near-well Effects in Coarse-Scale Models of Primary Oil Production

Cited by 15 publications

References 32 publications

Near-well upscaling for three-phase flows

Near-well upscaling for three-phase flows

Statistical assignment of upscaled flow functions for an ensemble of geological models

Joint optimization of oil well placement and controls

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