A Novel Empirical Equation for Relative Permeability in Low Permeability Reservoirs

Ge, Yulei; Li, Shurong; Qu, Kexin

doi:10.1016/j.cjche.2014.09.031

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…K is the absolute permeability of rock; , where A,B,C,D are the identification coefficients, the specific method is shown in [21]. The descending of permeability is caused by the adsorption of polymer, it can be described as …”

Section: Appendixmentioning

confidence: 99%

“…In this way, the original optimization problem is transformed to the solution of integral equation (21). However, in normal conditions, this equation is hard to solve.…”

Section: Snapshots Methodsmentioning

confidence: 99%

“…However, in normal conditions, this equation is hard to solve. When the number of time samples is less than space samples, the snapshots method [17] is a good way to solve (21). This method can significantly reduce calculation quantities.…”

Section: Snapshots Methodsmentioning

confidence: 99%

“…, to make sure that (20) is always equal to zero, if and only if (21) is true. This is the necessary condition of extremum for (14).…”

Section: Three-dimensional Spatial-temporal Methods Based On Kl Decompmentioning

confidence: 99%

“…Because the whole system is five-dimensional considering the time and value of water saturation, it can't be plotted in the picture directly. In order to illustrate clearly, the 21 grids ranging from ( ) 1,11,3 to ( ) 21,11,3 at the third layer is chose. The result and error of modelling are shown in Figures 6 and 7.…”

Section: Modelling and Verification For Asp Floodingmentioning

confidence: 99%

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Spatial-Temporal Separation Based on the Dynamic Recurrent Wavelet Neural Network Modelling for ASP Flooding

Li¹

2017

AJAM

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Abstract:In this paper, a three-dimensional spatial-temporal decomposition modelling method is proposed to build the alkali-surfactant-polymer (ASP) flooding model, in which a new dynamic recurrent wavelet neural network (DRWNN) is presented to identify the temporal coefficients. At first, the detailed mathematical model of ASP flooding is described which is a complex distributed parameter system. Then a three-dimensional spatial-temporal modelling method is inferred based on Karhunen-Loeve (K-L) decomposition to decompose the water saturation of reservoir into a series of spatial basis functions and corresponding temporal coefficients. Furthermore, the recurrent wavelet neural network is used to acquire the identification model, in which the injection concentrations of ASP flooding and temporal coefficients are taken as the input and output information. In order to improve the capability of dynamic modelling, DRWNN is proposed through adding feedback layers and setting the different weights with time to achieve dynamic memory of the past information. Considering the gradient descent method for the neural networks training easily leads to local minimum and slow convergence speed, the spectral conjugate gradient method is introduced to optimize the weights of DRWNN. At last, DRWNN is used to build the relation between the moisture content of production wells and the water saturation of the corresponding grids. Thus, the final approximate model of ASP flooding is finished. The accuracy is proved by model with four injection wells and nine production wells through data from the mechanism model.

show abstract

Section: Appendixmentioning

confidence: 99%

“…In this way, the original optimization problem is transformed to the solution of integral equation (21). However, in normal conditions, this equation is hard to solve.…”

Section: Snapshots Methodsmentioning

confidence: 99%

Section: Snapshots Methodsmentioning

confidence: 99%

“…, to make sure that (20) is always equal to zero, if and only if (21) is true. This is the necessary condition of extremum for (14).…”

Section: Three-dimensional Spatial-temporal Methods Based On Kl Decompmentioning

confidence: 99%

Section: Modelling and Verification For Asp Floodingmentioning

confidence: 99%

See 3 more Smart Citations

Spatial-Temporal Separation Based on the Dynamic Recurrent Wavelet Neural Network Modelling for ASP Flooding

Li¹

2017

AJAM

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Optimization of ASP flooding based on dynamic scale IDP with mixed-integer

Chang

et al. 2017

Applied Mathematical Modelling

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Optimization for ASP flooding based on adaptive rationalized Haar function approximation

Zhang

2018

Chinese Journal of Chemical Engineering

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A Novel Empirical Equation for Relative Permeability in Low Permeability Reservoirs

Cited by 18 publications

References 14 publications

Spatial-Temporal Separation Based on the Dynamic Recurrent Wavelet Neural Network Modelling for ASP Flooding

Spatial-Temporal Separation Based on the Dynamic Recurrent Wavelet Neural Network Modelling for ASP Flooding

Optimization of ASP flooding based on dynamic scale IDP with mixed-integer

Optimization for ASP flooding based on adaptive rationalized Haar function approximation

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