Basic Applied Reservoir Simulation

Ertekin, Turgay; Abou‐Kassem, Jamal H.; King, Gary

doi:10.2118/9781555630898

Cited by 273 publications

(32 citation statements)

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“…Further let's consider productive oil reservoirs with a thickness not lesser than a few meters, assuming that the average thickness of the reservoir is much smaller than horizontal dimensions of the considered area. Then let's assume that the heat does not spread to the roof of the reservoir and use a two-dimensional non-stationary model of thermal conductivity [9,10]. In this case, the general formulation of the nonstationary thermal conductivity problem, taking into account the heat exchange at the reservoir boundaries, in the Cartesian coordinate system (x, y) associated with the reservoir, has a view [10]:…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, methods of the oil reservoirs heating with hot water or steam are quite energy-consuming, so it's important to study the processes very carefully for effective applying in practice. Nowadays, there are many methods of physical and computer modelling of oil reservoir heating, which allow to solve various practical problems [7][8][9]. However, the accuracy of these methods remains relatively low and therefore the results are mainly qualitative.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of the reservoirs heating with the aim of oil recovery increasing

Lubkov

Mosiichuk

2022

TAPR

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The object of the research is optimal installation of the heat injection wells for reservoirs heating in order to increase oil recovery and, accordingly, support oil production in the hard-reaching heterogeneous reservoirs. One of the most problematic areas in modern oil production is the difficulty of extracting high-viscosity oil from the reservoirs. So far, the most effective method to overcome this problem is the thermal method. However, the possibilities of this method are limited by its high energy consumption and the cost of relevant practice research. Thus, less expensive corresponding methods of mathematical modeling become more important. This investigation uses a combined finite-element-difference method for the non-stationary thermal conductivity problem. Numerical modeling of the temperature distribution around heat injection wells are carried out, taking into account the heterogeneity of the thermal properties of the oil reservoir and the conditions of convective heat exchange at the reservoir’s boundaries. The proposed method, due to its high accuracy and convergence of the solutions, allows to obtain reliable practical results and has a number of advantages in comparison with the same research methods. It is established that the process of heating of oil reservoirs is slow and energy consuming, so to increase profitability, it is obviously necessary to use associated production products, such as associated gas. It is shown that less wet layers heat up better and there is no sense to heat the layer for more than two weeks, because the radius of the effective heating area (with a temperature exceeding 80 °C required for outcome of high-viscosity oil from the rock) in this case is sufficient. It is also found that the operation of heat-injection wells is more profitable with their joint interaction, in that case the effective heating area of the oil reservoir and, accordingly, the number of production wells will be the largest. Another hand, the main factor in the location of heat-injection wells is defined by special characteristics of the oil-bearing section of the reservoir in each case. The configurations of the location of heat-injection wells, which were presented in this paper, cover the most optimal cases of the installations of considered oil-bearing section of the reservoirs and can be used in practice.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of the reservoirs heating with the aim of oil recovery increasing

Lubkov

Mosiichuk

2022

TAPR

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“…The nonlinear polymer flooding model, eqs 8 – 10 , could adopt the following general form: This equation was discretized using finite differences, resulting in an unconditionally stable numerical model for the flow problem, using an upwind fully implicit difference scheme and Newton–Raphson (NR) method. 57 , 58 The mesh assumes that the block size is constant. The discretized nonlinear equations at node ι and time step n + 1 take the residual ( R ε) form For either continuous injection or slug injection schemes, the boundary conditions are Dirichlet-type, and thus, ι = 2, 3,···, Ι – 1, and for time discretization, n = 0, 1, 2, ....…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…This equation was discretized using finite differences, resulting in an unconditionally stable numerical model for the flow problem, using an upwind fully implicit difference scheme and Newton−Raphson (NR) method. 57,58 The mesh assumes that the block size is constant. The discretized nonlinear equations at node ι and time step n + 1 take the residual (Rε) form…”

Section: Model For Polymermentioning

confidence: 99%

Practical Mathematical Model for the Evaluation of Main Parameters in Polymer Flooding: Rheology, Adsorption, Permeability Reduction, and Effective Salinity

2022

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Polymer flooding is one of the most used chemical enhanced oil recovery (CEOR) technologies worldwide. Because of its commercial success at the field scale, there has been an increasing interest to expand its applicability to more unfavorable mobility ratio conditions, such as more viscous oil. Therefore, an important requirement of success is to find a set of design parameters that balance material requirements and petroleum recovery benefits in a cost-effective manner. Then, prediction of oil recovery turns out to handle more detailed information and time-consuming field reservoir simulation. Thus, for an effective enhanced oil recovery project management, a quick and feasible tool is needed to identify projects for polymer flooding applications, without giving up key physical and chemical phenomena related to the recovery process and avoiding activities or projects that have no hope of achieving adequate profitability. A detailed one-dimensional mathematical model for multiphase compositional polymer flooding is presented. The mathematical formulation is based on fractional flow theory, and as a function of fluid saturation and chemical compositions, it considers phenomena such as rheology behavior (shear thinning and shear thickening), salinity variations, permeability reduction, and polymer adsorption. Moreover, by setting proper boundary and initial conditions, the formulation can model different polymer injection strategies such as slug or continuous injection. A numerical model based on finite-difference formulation with a fully implicit scheme was derived to solve the system of nonlinear equations. The validation of the numerical algorithm is verified through analytical solutions, coreflood laboratory experiments, and a CMG-STARS numerical model for waterflooding and polymer flooding. In this work, key aspects to be considered for optimum strategies that would help increase polymer flooding effectiveness are also investigated. For that purpose, the simulation tool developed is used to analyze the effects of polymer and salinity concentrations, the dependence of apparent aqueous viscosity on the shear rate, permeability reduction, reversible–irreversible polymer adsorption, polymer injection strategies on petroleum recovery, and the flow dynamics along porous media. The practical tool and analysis help connect math with physics, facilitating the upscaling from laboratory observations to field application with a better-fitted numerical simulation model, that contributes to determine favorable scenarios, and thus, it could assist engineers to understand how key parameters affect oil recovery without performing time-consuming CEOR simulations.

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“…The data grid of the oil reservoir visualization tool consists of individual cube-shaped cells. Pressure, oil and water saturation values are generated from a serial oil reservoir simulator [26] and then mapped against a color set to visually represent a single cell (grid point). The tool also has buttons for forward, playback and rewind to allow the user and to visualize the grid at a specific time for checking the produced data.…”

Section: Serial Oil Reservoir Data Visualization Toolmentioning

confidence: 99%

Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

et al. 2020

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The main goal of oil reservoir management is to provide more efficient, price-effective and environmentally more secure oil production. Oil production management includes an accurate characterization of the reservoir and strategies that involve interactions between reservoir data and human assessment. Hence, it is important to graphically visualize and handle massive data sets of oil and gas pressure / saturation levels to help decision makers in statistical analysis, history matching and recovery of hydrocarbons of the reservoir. In this article, we experimentally study the parallelization of intensive computation for a 3-D (three dimensional) oil reservoir data visualization tool. For this tool, we develop and implement a transformation and lighting model to visualize and react with the grid. Herein, we propose a hybrid (shared memory and distributed memory) parallelization technique to adapt with the data processing scalability. We tested these implementations on OpenStack Cloud Virtual Cluster. Our results indicate that although the virtual platform adds overhead for running parallel implementations, utilizing knowledge of the VM location on the compute host and network traffic among VMs to deploy the virtual environment can achieve significant performance enhancements. Hybrid parallel implementation using large data size can achieve 70× speedup over serial execution without owning a costly HPC infrastructure as the conventional parallel processing deployment model.INDEX TERMS Cloud computing, data visualization tool, HPC, hybrid (distributed/shared)-memoryparallel programming, MPI, multi-threading, OpenStack.

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Basic Applied Reservoir Simulation

Cited by 273 publications

References 0 publications

Modeling of the reservoirs heating with the aim of oil recovery increasing

Modeling of the reservoirs heating with the aim of oil recovery increasing

Practical Mathematical Model for the Evaluation of Main Parameters in Polymer Flooding: Rheology, Adsorption, Permeability Reduction, and Effective Salinity

Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

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