Dynamic optimization for the core-flooding problem in reservoir engineering

Kameswaran, Shivakumar; Biegler, Lorenz T.; Staus, George H.

doi:10.1016/j.compchemeng.2005.02.038

Cited by 31 publications

(14 citation statements)

References 10 publications

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“…Examples of simulation problem domains that have benefited from multigrid techniques include porous media transport [10,11], molecular dynamics [12][13][14], fluid dynamics [10,11,15], and neural network simulations (and neurotransmitter diffusion) [16].…”

Section: Historymentioning

confidence: 99%

OpenMG: A new multigrid implementation in Python

Bertalan

Islam

Sidje

et al. 2014

Numer. Linear Algebra Appl.

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SUMMARYIn many large-scale computations, systems of equations arise in the form Au D b, where A is a linear operation to be performed on the unknown data u, producing the known right-hand side, b, which represents some constraint of known or assumed behavior of the system being modeled. Because such systems can be very large, solving them directly can be too slow. In contrast, a multigrid method removes different components of the error at different resolutions using smoothers that reduce high-frequency components of the error more readily than low. Here, we present an open-source multigrid solver written only in Python. OpenMG is a pure Python experimentation environment for testing multigrid concepts, not a production solver. The particular restriction method implemented is for 'standard' multigrid. By making the code simple and modular, we make the algorithmic details clear. The resulting solver is tested on an implicit pressure reservoir simulation problem with satisfactory results.

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Section: Historymentioning

confidence: 99%

OpenMG: A new multigrid implementation in Python

Bertalan

Islam

Sidje

et al. 2014

Numer. Linear Algebra Appl.

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“…The advantage of using the numerical experiment is that the true properties of the core samples are known, which can be used to compare with the estimation results, thus the estimation results can be quantified (Richmond et al, 1990;Chardaire-Riviere et al, 1992;Kameswaran et al, 2005). This experiment is setup to simulate a 1D two phase (oil and water) flow in a water-wet core sample.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The pressure drop, saturation profile and water production were regarded as observation data, while the B-spline model was used to represent relative permeability and capillary pressure curves. Kameswaran et al (2005) used an interior point algorithm to solve the large-scale nonlinear programming problem. The estimated parameters included relative permeability, capillary pressure and absolute permeability of the core sample, while the power-law model was used to represent the relative permeability curves.…”

Section: Introductionmentioning

confidence: 99%

Relative Permeability Estimation from Displacement Experiments Using EnKF Method

Yang

Arhuoma

2010

International Oil and Gas Conference and Exhibition in China

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The relative permeability is a crucial parameter for accurately evaluating reservoir performance. Two-phase relative permeability curves are normally obtained by either directly or indirectly interpreting the displacement experiment data. As for the direct interpretation, the core samples are assumed to be homogeneous, while the capillary forces are normally neglected. Although the indirect interpreting approach is able to take heterogeneity of the core sample into account, calculating the derivatives of the objective functions through the graphical or numerical methods is prone to considerable errors. In this paper, a new method is developed to calculate the absolute and relative permeability from unsteady-state, two-phase immiscible displacement experiments. The permeability data is determined by history matching the experimentally observed pressure drop, production data and water saturation profiles via the ensemble Kalman filter (EnKF) algorithm. The power-law model is utilized to represent the relative permeability. Both the absolute and relative permeability are calculated simultaneously by assimilating the observed data. The newly developed method is validated using a numerical coreflooding experiment. It has been found that estimations of absolute and relative permeability are improved progressively as more observation data are assimilated. In addition, this method is convenient to be implemented as the derivative of the objective function is not required.

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“…In this study, we reconsider this approach with a different discretization method and more recent optimizer. In particular, we apply Radau collocation on finite elements for temporal discretization 13, 14. Since the performance of this approach solely depends on the optimization solver, it is crucial to choose an efficient solver.…”

Section: Optimization Strategy and Treatment Of Css Conditionmentioning

confidence: 99%