Correlating rock packing index, tortuosity, and effective cross-sectional area with electrical quality index

Soleymanzadeh, Aboozar; Kolah-kaj, Parvin; Najafi-Silab, Reza; Kord, Shahin

doi:10.1016/j.jngse.2021.104302

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Although many conductivity models have been published, − the most widely used method for the rock-electric analysis is Archie’s equation, − which was derived from experimental data. However, due to the ignorance of multiple factors, Archie’s law could not be utilized to explain the reported non-Archie behavior. − According to relevant studies, it is found out that the main influencing factors resulting in the non-Archie phenomenon involve water salinity, conductive matrix, pore structure, clay, and crack. − …”

Section: Introductionmentioning

confidence: 99%

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Study on Rock-Electric Characteristics of Cracked Porous Rocks by the Novel Multifactor Conductivity Model

Meng,

Ye,

Yang

et al. 2023

ACS Omega

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Due to the influence of multiple factors on the conductive properties of rocks, the Archie’s formula, considering only a single factor, makes it difficult to reasonably explain rock-electric characteristics of cracked porous rocks. In order to better describe the conductive mechanism of cracked porous rocks, a generalized multifactor conductivity model was proposed by considering and introducing multiple influencing factors such as the series-parallel structure, conductive matrix, cracks, and fluids, which is conducive to more accurate research on the conductive mechanism of rocks. It should be noted that the developed model is not only applicable to cracked porous rocks but also useful for porous rocks. Through the study and analysis of various influencing factors, it is demonstrated by the simulation results that both the conductive matrix and cracks improve the conductive ability, which are crucial factors resulting in the non-Archie behavior and low-resistivity pay zone, and rock conductivity is more sensitive to the conductive matrix and cracks in tight reservoirs with porosity below 10%. Furthermore, experimental data are available to validate the novel multifactor conductivity model, and the comparison results show its advantages in predicting and explaining the conductive properties of cracked porous rocks.

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Section: Introductionmentioning

confidence: 99%

“… 8 − 11 According to relevant studies, it is found out that the main influencing factors resulting in the non-Archie phenomenon involve water salinity, conductive matrix, pore structure, clay, and crack. 12 − 24 …”

Section: Introductionmentioning

confidence: 99%

Study on Rock-Electric Characteristics of Cracked Porous Rocks by the Novel Multifactor Conductivity Model

Meng,

Ye,

Yang

et al. 2023

ACS Omega

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Electrical rock typing using Gaussian mixture model to determine cementation factor

Najafi-Silab

Soleymanzadeh

Kolah-kaj

et al. 2023

J Petrol Explor Prod Technol

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Many studies have worked on the estimation of fluid saturation as an important petrophysical property in hydrocarbon reservoirs. Based on Archie's law, proper determination of cementation factor (m) can lead to accurate values of water saturation. Given that the m is mainly affected by electrical properties of rock, electrical quality index (EQI) can be used to estimate m through a novel rock typing technique. Despite the efficient applicability of EQI for the classification of rocks, with similar electrical behaviors, into distinct electrical rock types (ERTs), manual implementation of this method is time-consuming and gets excessively more difficult for larger datasets. In this work, a fast automated version of EQI methodology was presented. As a fuzzy clustering algorithm, Gaussian mixture model (GMM) was implemented on a large quantity of carbonate and sandstone samples to cluster them into distinct ERTs based on EQI values. To this end, 100 data points were randomly selected for testing purposes, and the remaining data points were used as training subsets for carbonate and sandstone samples. An innovative hybrid EQI-GMM approach was developed to determine the optimum number of clusters. Furthermore, results of two commonly-used criteria, namely Schwarz's Bayesian Criterion (BIC) and Akaike Information Criterion (AIC), showed that they fail to specify ERTs properly. The predicted values for m by the hybrid EQI-GMM approach were more accurate (RMSE is 0.0167 and 0.0056 for carbonate and sandstone samples, respectively) than outputs of the traditional Archie’s law (RMSE is 1.6697 and 0.1850 for carbonate and sandstone samples, respectively).

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Calculation of oil saturation in water-flooded layers based on the modified Archie model

Xian-qing

Wang

et al. 2023

Interpretation

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The Archie model is the foundation for calculating oil saturation, but limitations exist when the model is used to calculate oil saturation in water-flooded layer. In the process of water injection, the dynamic change in oil saturation will be caused by the different degrees of water flooding and the properties of the injected water. Under the dynamic condition of water flooding, the Archie model is not suitable for calculating the oil saturation of water flooded layers. By combining dynamic and static methods, a "double ratio" model of the same sedimentary facies layer in the later development stage was established: Rt = R0− R0· f( Fw)=R0[1− f( Fw)]. Based on the parameters of rock resistivity and formation water resistivity, an improved Archie model for calculating oil saturation in water flooded layers of the same sedimentary facies was established. The interpretation of the actual data of the Zhenwu Oilfield in Jiangsu, China shows that the average relative error between the calculation result and the core analysis result is 5.46%. The calculation result is reasonable, which offers a scientific basis for predicting the remaining oil distributions. The computational results have been validated by real datasets. This improved mode can provide experience-based guidance for the calculation of the remaining oil saturation of the water flooded layer in the same sedimentary interpretation layer.

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Correlating rock packing index, tortuosity, and effective cross-sectional area with electrical quality index

Cited by 3 publications

References 33 publications

Study on Rock-Electric Characteristics of Cracked Porous Rocks by the Novel Multifactor Conductivity Model

Study on Rock-Electric Characteristics of Cracked Porous Rocks by the Novel Multifactor Conductivity Model

Electrical rock typing using Gaussian mixture model to determine cementation factor

Calculation of oil saturation in water-flooded layers based on the modified Archie model

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