Timestep Selection During Streamline Simulation via Transverse Flux Correction

Osako, Ichiro; Datta‐Gupta, Akhil; King, Micháel J.

doi:10.2118/79688-ms

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…For the unfavorable mobility ratio case (M=10), the cross flow is not significant and we require fewer pressure updates. 15 Figures 15a and b show that the rates are almost constant throughout the optimization process. The optimized water arrival occurred at 350 Days.…”

Section: Impact Of Mobility Ratio: 2d Heterogeneous Examplementioning

confidence: 95%

“…The selection of the time step size depends, among others, on transverse fluxes arising from mobility and unsteady state effects according to guidelines provided by Osako et.al. 15 A Smart Well Example. This example demonstrates the applicability of our approach to mange waterfront using smart wells with inflow control valves (ICV).…”

Section: Impact Of Mobility Ratio: 2d Heterogeneous Examplementioning

confidence: 99%

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Optimal Waterflood Management Using Rate Control

Alhuthali

Oyerinde

Datta‐Gupta

2006

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractField scale rate optimization problems often involve highly complex reservoir models, production and facilities related constraints and a large number of unknowns. All these make optimal reservoir management via rate and flood front control difficult without efficient optimization tools. Some aspects of the optimization problem have been studied before using mainly optimal control theory. However, the applications todate have been limited to rather small problems because of the computation time and the complexities associated with the formulation and solution of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep efficiency under realistic field conditions has still remained largely unexplored.We propose a practical and efficient approach for computing optimal injection and production rates and thereby manage the waterflood front to maximize sweep efficiency and delay the arrival time to minimize water cycling. Our work relies on equalizing the arrival times of the waterfront at all producers within selected sub-regions of a water flood project. The arrival time optimization has favorable quasilinear properties and the optimization proceeds smoothly even if our initial conditions are far from the solution. Furthermore, the sensitivity of the arrival time with respect to injection and production rates can be calculated analytically using a single flow simulation. This makes our approach computationally efficient and suitable for large-scale field applications. The arrival time optimization ensures appropriate rate allocation and flood front management by delaying the water breakthrough at the producing wells.Multiple examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include several 2D synthetic examples for validation purposes and a 3D field application. In addition, we demonstrate the potential of the approach to optimize the flow profile along injection/production segments of horizontal-smart wells

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Section: Impact Of Mobility Ratio: 2d Heterogeneous Examplementioning

confidence: 95%

Section: Impact Of Mobility Ratio: 2d Heterogeneous Examplementioning

confidence: 99%

Optimal Waterflood Management Using Rate Control

Alhuthali

Oyerinde

Datta‐Gupta

2006

SPE Annual Technical Conference and Exhibition

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“…The major advantage of this technique is the possibility of using large time steps to solve the mass and energy transport along the streamlines. When we use the finite-difference solution the maximum time step is limited by the Courant-Fredrichs-Levy (CFL) condition (Osako et al 2003). On the other hand, there is no restriction for the time step size when the analytical solution is used (no restriction to the flux advance along streamlines), and the solution is free from numerical diffusion.…”

Section: Streamline Simulation Strategymentioning

confidence: 99%

Numerical Simulation of 2D Hot Waterflooding Using Analytical Solutions Along Streamlines

Vicente

Priimenko

Pires

2014

SPE Latin America and Caribbean Petroleum Engineering Conference

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Streamline simulation is an alternative method to conventional finite difference methods, more suited to complex reservoir physics but limited in size by computational effort. The main feature of streamline modeling is replacing the space variable by the time-of-flight. In this approach, the fluid transport equation is solved along each streamline. This method is more attractive in convection-dominated flow and in large heterogeneous models. In high paraffinic oil reservoirs cold water flooding does not lead to satisfactory ultimate recovery factors due to the precipitation of solid paraffin from the liquid in the pore space. For this particular case, hot water flooding is an enhanced oil recovery technique option to increase oil production. In this work the process of displacement of waxy crude by a hot water slug followed by cold water drive in porous media was modeled. The liquid phase is composed by two pseudo-components, oil and paraffin. It was assumed that the viscosities of the components and paraffin concentration in the liquid phase are functions of temperature only. The simulations were run in 2D incompressible models without heat losses to adjacent layers and neglecting gravity and capillary effects. The system of hyperbolic equations that represents the mass conservation of each component was solved analytically. The solution of the continuous injection problem is self-similar, but in the case of slug injection interaction between waves of different families occurs. An important characteristic arising from the use of analytical solutions along streamlines is the ability to capture correctly the saturation and temperature shocks. Using analytical solutions along streamlines allowed to reduce the number of time steps and achieved results free of numerical diffusion. The results of streamline simulations were compared a commercial finite difference based simulator and presented close agreement with lower computational cost. The simulations were performed for two cases. The first one considers the injection of a high temperature water slug followed by water drive at the reservoir initial temperature. The second case analyzes the injection of a water slug at a lower temperature followed by water drive at a temperature higher than the reservoir temperature.

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“…Mallison et al (2004) have studied mapping errors in streamline simulations. Osaka and Datta-Gupta (2004) proposed a novel grid-based corrector algorithm to account for the transverse flux between successive pressure updates. Current compositional streamline simulators, as reported in the literature, can handle only two hydrocarbon phases.…”

Section: List Of Symbols C I Composition Of Component I F Jmentioning

confidence: 99%

Compositional Streamline Simulation: A Parallel Implementation

Bhambri

Mohanty

2011

Transp Porous Med

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Compositional reservoir simulators are needed to model oil recovery from petroleum reservoirs by miscible gas injection. This article describes the development and application of a parallel version of a compositional streamline simulator. A compositional streamline module is developed and integrated with an existing finite-difference simulator. Finitedifference calculations including pressure solution are performed on a single processor. The movement of fluids (which includes streamline tracing, mappings, flux calculations, and one-dimensional solver) is done along streamlines in the streamline module. The streamline module is parallelized by distributing streamlines among different processors because computations along any streamline are independent of other streamlines and no communication is required. Flux calculation along streamlines is computationally expensive primarily due to flash calculations that are performed to distribute components among the hydrocarbon phases. Simultaneous solution of this time-consuming step results in reduction of total CPU time. Communication (gathering) across the streamlines or processors is achieved by using message passing interface (MPI). Test runs are conducted for different examples to investigate the performance of the parallel streamline simulator. Results indicate that significant reduction in CPU time can be obtained by distributing streamlines on different processors.

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Timestep Selection During Streamline Simulation via Transverse Flux Correction

Cited by 17 publications

References 13 publications

Optimal Waterflood Management Using Rate Control

Optimal Waterflood Management Using Rate Control

Numerical Simulation of 2D Hot Waterflooding Using Analytical Solutions Along Streamlines

Compositional Streamline Simulation: A Parallel Implementation

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