Uncertainty analysis of Archie’s parameters determination techniques in carbonate reservoirs

Hamada, G.M.; Al-Majed, Abdulaziz; Okasha, Taha; Al-Gathe, Abdelrigeeb A.

doi:10.1007/s13202-012-0042-x

Cited by 49 publications

(25 citation statements)

References 13 publications

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“…(7.1) depends on the accuracy of the estimates of the input parameters R w , R t , and F. (Hamada et al 2013) 0.102 0.002 0.51 0.14 0.10 CAPE (a, m, n) (Hamada et al 2013) 0.095 0.001 0.33 0.12 0.08 BFGSABC 0.035 0.004 0.026 0.08 0.07 The carbonate reservoirs are of great importance because they contain almost 60 % of world's oil reserves.…”

Section: Estimation Of the Archie Parametersmentioning

confidence: 99%

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Optimization Methods, Theory and Applications

Xu¹,

Wang²,

Wu³

2015

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Section: Estimation Of the Archie Parametersmentioning

confidence: 99%

“…Hereinafter, 29 carbonate core samples taken from (Hamada et al 2013) are selected as the simulations of certain wells, that is N D 29. For each core sample, the electrical resistivity R w and R t at different water saturation percentages are measured at room temperature, that is M D 30.…”

Section: Estimation Of the Archie Parametersmentioning

confidence: 99%

Optimization Methods, Theory and Applications

Xu¹,

Wang²,

Wu³

2015

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“…The accuracy of Eq. (7.1) depends on the accuracy of the estimates of the input parameters R w , R t , and F. (Hamada et al 2013) 0.28 2.34 2.12 CAPE (a, m, n) (Hamada et al 2013) (Hamada et al 2013) 0.102 0.002 0.51 0.14 0.10 CAPE (a, m, n) (Hamada et al 2013) 0 Hereinafter, 29 carbonate core samples taken from (Hamada et al 2013) are selected as the simulations of certain wells, that is N D 29. For each core sample, the electrical resistivity R w and R t at different water saturation percentages are measured at room temperature, that is M D 30.…”

Section: Estimation Of the Archie Parametersmentioning

confidence: 99%

“…The simplex method is applied in (Chen et al 1995) to identify the three parameters of Archie equation. In (Hamada et al 2002(Hamada et al , 2013, Archie parameters are being estimated by an approach based on a three-dimensional (3D) regression plot involving water saturation, formation resistivity and porosity. In (Godarzi et al 2012), an intelligent algorithm, GA, is applied to estimation of Archie parameters.…”

mentioning

confidence: 99%

“…Set x D .m; n; a/, then BFGSABC algorithm can be applied to solve the model indicated by Eq. (7.3).Based on 30 independent core samples measurements, the data obtained by the proposed hybrid method are compared with the data computed by 3D method and CAPE.Typical values of the Archie parameters obtained using the CAPE method, the 3D method (Hamada et al 2002(Hamada et al , 2013) and the proposed hybrid method are as shown in Table 7.1. Table 7.2 demonstrates the average error, the root mean square error and standard deviation of the water saturation computed by three techniques.…”

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A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Liu

et al. 2015

Optimization Methods, Theory and Applications

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Archie formula, which contains three fundamental parameters (a, m, n), is the basic equation to compute the water saturation in a clean or shaly formation. These parameters are known as Archie parameters. To identify accurately the water saturation for a given reservoir condition, it depends critically on the accurate estimates of the values of Archie parameters (a, m, n). These parameters are interdependent and hence it is difficult to identify them accurately. So we present a new hybrid global optimization technique, where a gradient-based method with BFGS update is combined with an intelligent algorithm called Artificial Bee Colony. This new hybrid global optimization technique has both the fast convergence of gradient descent algorithm and the global convergence of swarm algorithm. It is used to identify Archie parameters in carbonate reservoirs. The results obtained are highly satisfactory. To further test the effectiveness of the new hybrid global optimization method, it is applied to ten non-convex benchmark problems. The outcomes are encouraging. Keywords Archie parameters • Hybrid global optimization • ABC algorithm • Gradient-based method IntroductionAn accurate identification of oil reserve in either an undeveloped or a developed reservoir is a significant task for a petro-physicist and reservoir engineer. To calculate the hydrocarbon reserve in its formation, it is required to know the amount of the water saturation. Inaccurate calculation of the amount of the water saturation will lead to a large error in the estimation of the hydrocarbon reserve.Archie equation, which is the underlying foundation for analyzing water saturation in potential oil and gas zones, is commonly used in the calculation of the amount of the water saturation of a reservoir rock, and hence providing an estimate of the initial hydrocarbon reserve of the reservoir. The values of the input parameters of Archie water saturation model in a clean or shaly formation must be estimated as accurately as possible. From field experiments, it is observed that the values of the following three input parameters, the cementation exponent m, the saturation exponent n, and the tortuosity factor a, depend critically on the petro-physical properties of a given rock. Thus, these parameters will take different values for different fields. Furthermore, the values of a, m and n in Archie formula are interdependent. Therefore, it is of critically importance that the estimations of the values of the parameters a, m and n are done accurately (Mabrouk et al. 2013;Michael et al. 2013;Makar and Kamel 2012).There are several techniques available in the literature for estimating the values of Archie parameters a; m and n. A conventional technique is to determine n separately, independent of a and m. This approach may not be valid in real situations, as it may lead to large error in the estimation of the amount of the water saturation (Archie 1942). In (Maute et al. 1992), a data analysis approach is proposed to determine the Archie parameters m; n an...

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Universal scaling of the formation factor in porous media derived by combining percolation and effective medium theories

Ghanbarian

Hunt

Ewing

et al. 2014

Geophys. Res. Lett.

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The porosity dependence of the formation factor for geologic media is examined from the perspective of universal scaling laws from percolation and effective medium theories. Over much of the range of observed porosity, the expected percolation scaling is observed, but the values of the numerical prefactor do not conform to the simple predictions from percolation theory. Combining effective medium and percolation theories produces a numerical prefactor whose value depends on both the threshold porosity and the porosity above which the formation factor crosses from percolation to effective medium scaling. This change allows extraction of a numerical value of the prefactor, which is reasonably close to experimental values. Subsequent evaluation of the porosity dependence of the formation factor shows that difficulties in prior comparisons of theory and experiment are largely removed when percolation scaling is allowed to transition to effective medium scaling far above the percolation threshold.

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Uncertainty analysis of Archie’s parameters determination techniques in carbonate reservoirs

Cited by 49 publications

References 13 publications

Optimization Methods, Theory and Applications

Optimization Methods, Theory and Applications

A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Universal scaling of the formation factor in porous media derived by combining percolation and effective medium theories

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