Review of well models and assessment of their impacts on numerical reservoir simulation performance

Formation permeability distribution is important in the oil industry since it is a key tool in forecasting oil production performance, a matter of primary concern to oilfield operators. However, the direct problems of reservoir analysis are limited in practice by a lack of input parameters such as reservoir vertical heterogeneity. Therefore, inverse problem modeling is important to achieve reservoir vertical heterogeneity using production profiles, which are always known for existing reservoirs. In this way, what has occurred inside a reservoir can be understood by applying reservoir output in the reverse model. Because formation permeability can frequently be represented by a normal distribution and can describe heterogeneity and water production capability, an inverse problem of formation permeability was modeled to study reservoir vertical heterogeneity and reservoir production performance based on oil-water two-fluid flow theory and optimization problems. Moreover, through establishing an objective function, we obtained an optimal solution for water production rate. Finally, one case is simulated by this model and good results achieved, which are compatible with production and experimental data.

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“…, for each grid and conductivity definition [22][23][24][25], we can calculate the conductivity equation for all grids as follows:…”

Section: Research On Whether the Inverse Problem Model Of Formation Pmentioning

confidence: 99%

An Estimation Method of Water Drive Displacement Based on Formation Permeability Distribution and Inverse Problem Modeling

Huang

Wang

et al. 2014

Advances in Mechanical Engineering

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“…Reservoir numerical simulation methods considering LSWI contain injected water deduplication by many injection wells and coupled simulation for water chokes, injection well and reservoir. The former is suitable for reservoir possessed enough injectivity test data, and the water injection rate can be calculated by real-time monitoring test data, and these values can be used as inner boundary condition in model (Dumkwu et al 2012;Villanueva-Triana et al 2013;Stone et al 1989;Ding 2011), practically, injectivity test data are very few, so the complete injection process cannot be represented by them (Gao et al 2015). The latter needs enough data of injection well devices, such as diameters of water chokes, which should be used to calculate the pressure loss coupled with well bottom pressure (BHP) and reservoir pressure (Zhao et al 2012;Lin et al 2011), and this results in quit complex calculation.…”

Section: Introductionmentioning

confidence: 99%

Coupled numerical simulation of multi-layer reservoir developed by lean-stratified water injection

Gao

Liu

et al. 2016

J Petrol Explor Prod Technol

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Lean-stratified water injection is one of the most important technologies to increase production and develop potentials for the oilfield with extreme high water content. However, traditional models cannot entirely solve the inner boundary conditions of lean-stratified water injection. Therefore, we established the injection wellbore constraint equations, which were coupled with the oil/water two-phase numerical reservoir models, and then the seven diagonal form sparse coefficient matrix was solved by block precondition of generalized minimal residual algorithm. Considering the specific situation of lean-stratified water injection wells, reservoir geology and production schemes of the middle part of the sixth Oilfield in Xing Shugang, three mechanism models of multi-layer heterogeneous reservoir were constructed to simulate the leanstratified water injection. The influences of different segments numbers, modes of combination in segment layer and rhythm characteristics of remaining oil reserves and distribution are evaluated.

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“…In reservoir simulation, far-field areas are well-understood and fairly resolved with current simulation tools but near wellbore regions are normally under-resolved and become a source of inaccuracy in the resolution of the fluid physics in this zone [Ding, 2011;Dumkwu et al, 2012]. The under-resolved physics is modelled by including several parameters, adjusted using history matching, such as the well skin factor or the productivity index [Dumkwu et al, 2012].…”

Section: Motivationmentioning

confidence: 99%

“…The under-resolved physics is modelled by including several parameters, adjusted using history matching, such as the well skin factor or the productivity index [Dumkwu et al, 2012]. However, the logarithmic pressure drop in the surrounding wellbore region, as well as the complex thermodynamic behaviour of oil often leads to sudden variations in flow properties.…”

Section: Motivationmentioning

confidence: 99%

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A fast IMPES multiphase flow solver in porous media for reservoir simulation

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Review of well models and assessment of their impacts on numerical reservoir simulation performance

Cited by 35 publications

References 30 publications

An Estimation Method of Water Drive Displacement Based on Formation Permeability Distribution and Inverse Problem Modeling

An Estimation Method of Water Drive Displacement Based on Formation Permeability Distribution and Inverse Problem Modeling

Coupled numerical simulation of multi-layer reservoir developed by lean-stratified water injection

A fast IMPES multiphase flow solver in porous media for reservoir simulation

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