Empirical expression of permeability in terms of porosity, specific surface area, and residual water saturation of carbonate rocks

Chilingarian, George V.; Chang, Jincai; Bagrintseva, Ksenia I.

doi:10.1016/0920-4105(90)90029-3

Cited by 39 publications

(15 citation statements)

References 4 publications

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“…In the future, the authors will present additional microbiostratigraphy of the Asmari Formation. In addition, the authors are preparing equations relating porosity to permeability for the Asmari limestone by introducing two additional variables, i.e., irreducible fluid saturation and specific surface area, which is a measure of fracturing (see Chilingar et al, 1990). …”

Section: Microbiostratigraphymentioning

confidence: 99%

Asmari Limestone of Iran: A Giant of Oil and Gas Production

Lackpour¹,

Chilingar

Kalantari

2008

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Section: Microbiostratigraphymentioning

confidence: 99%

Asmari Limestone of Iran: A Giant of Oil and Gas Production

Lackpour¹,

Chilingar

Kalantari

2008

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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“…A data set published in the literature (Bagrintseva, 1977;Chilingarian et al, 1990) was used in this study to examine the permeability dependence on porous medium properties of carbonate reservoirs. The dataset was the combination of four different carbonate reservoirs located in former Union of Soviet Socialist Republics tabulated in Table 1.…”

Section: Data Descriptionmentioning

confidence: 99%

“…With respect to sorting, porosity tends to increase for perfectly sorted media and decrease as sorting becomes poorer (Beard and Weyl, 1973), thus affecting permeability. In addition to modified forms of the Kozeny-Carman equation (Payatakes et al, 1973;Dullien, 1991;Panda and Lake, 1994;Panda and Lake, 1995;Koponen et al, 1997) various other empirical (Morrow et al, 1969;Beard and Weyl, 1973) and theoretical (Berg, 1970;Scheidegger, 1974) correlations to pore and textural characteristics of rocks have been proposed, as well as permeability estimations supported on saturation measurements (Chilingarian et al, 1990) and percolation and fractal concepts (Pape et al, 1999;Costa, 2006). However, the non-uniqueness of the equation or method proposed remains an un-resolved challenge.…”

Section: Introductionmentioning

confidence: 97%

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A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

Basbug¹,

Karpyn²

2008

IJOGCT

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An artificial neural network (ANN) was designed and tested in the present study to examine the correlation between permeability estimations and porous medium properties, such as porosity, specific surface area, and irreducible water saturation. The network developed in this work uses soft computing techniques to investigate the absolute permeability dependence on pore-scale characteristics of carbonate reservoirs. The present study indicates that ANN generated permeability values are consistent with those obtained from core analysis. Results from this study confirm the complex relationship among permeability, porosity, specific surface area and irreducible water saturation of carbonate reservoirs, and suggest that variations in specific surface area affect the magnitude of irreducible water saturations, thus creating an apparent dependence of permeability on irreducible water saturation. Additional observations support a direct relationship between porosity and permeability, and an inverse relationship between specific surface area and permeability. [

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“…In addition to modified forms of the Kozeny-Carman equation 6,[8][9][10][11] , various other empirical 7, 12 and theoretical 13,14 correlations to pore and textural characteristics of rocks have been proposed, as well as permeability estimations supported on saturation measurements 15 and percolation and fractal concepts 16,17 . However, the non-uniqueness of the equation or method proposed remains an un-resolved challenge.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Permeability from Porosity, Specific Surface Area, and Irreducible Water Saturation using an Artificial Neural Network

Basbug¹,

Karpyn²

2007

Proceedings of Latin American &Amp; Caribbean Petroleum Engineering Conference

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fax 01-972-952-9435. AbstractAn Artificial Neural Network (ANN) was designed and tested in the present study to examine the correlation between permeability estimations and porous medium properties, such as porosity, specific surface area, and irreducible water saturation. The network developed in this work is a predictive tool that uses soft computing techniques to estimate absolute permeability of carbonate reservoirs. The Artificial Neural Network toolbox of MATLAB ® R2006b and the Feed Forward Error Back Propagation methodology were used in the construction of the network. Carbonate reservoir field data presented in the literature were utilized in the training, testing, and validation of the proposed model. The present study indicates that ANN generated permeability values are consistent with those obtained from core analysis. Results from this study confirm the complex relationship among permeability, porosity, specific surface area and irreducible water saturation of carbonate reservoirs, and suggest that variations in specific surface area affect the magnitude of irreducible water saturations, thus creating an apparent dependence of permeability on irreducible water saturation. Additional observations support a direct relationship between porosity and permeability, and an inverse relationship between specific surface area and permeability.

show abstract

Empirical expression of permeability in terms of porosity, specific surface area, and residual water saturation of carbonate rocks

Cited by 39 publications

References 4 publications

Asmari Limestone of Iran: A Giant of Oil and Gas Production

Asmari Limestone of Iran: A Giant of Oil and Gas Production

A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

Estimation of Permeability from Porosity, Specific Surface Area, and Irreducible Water Saturation using an Artificial Neural Network

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