Application of electrical rock typing for quantification of pore network geometry and cementation factor assessment

Kolah-kaj, Parvin; Kord, Shahin; Soleymanzadeh, Aboozar

doi:10.1016/j.petrol.2021.109426

Cited by 11 publications

(2 citation statements)

References 46 publications

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“…Recent studies have shown that rock typing methods originated from traditional petrophysical models are very practical tools for hydraulic and electrical flow unit (HFU and EFU) identification, representative sample selection, permeability prediction, cementation exponent estimation, and fluid saturation determination. ,− Mirzaei-Paiaman et al and Kolah-Kaj et al gave a good summary of different rock typing indices. Different from the rock textural classifications of Dunham and the pore type classifications of Choquette and Pray, rock typing is to classify rocks according to special microstructure parameters or petrophysical properties.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

et al. 2023

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The permeability (k) and electrical resistivity of carbonate rocks with similar porosity (ϕ) may exhibit multiple orders of magnitude differences due to the diversity of pore types and the multiscale characteristics of pore size. As a result, traditional permeability-porosity and resistivity-porosity models usually have strong limitations when applied to carbonate reservoirs, let alone evaluate permeability from electrical resistivity. In this study, a new rock typing parameter named relative transport quality index (RTQI) is proposed to categorize rock samples using permeability and formation resistivity factor (F). Samples with similar RTQI values tend to have similar relative capacities of hydraulic and electrical transport. Subsequently, the classification performance and microscopic differences between RTQI and other rock typing indices including flow zone indicator (FZI), current zone indicator (CZI), and electrical quality index (EQI) were compared and discussed based on the reported petrophysical data of 316 carbonate rock samples. The results indicate that the RTQI method significantly reduces the scattering of k and F by dividing the samples into distinct groups, and each RTQI group corresponds to a special k−F correlation with a high determination coefficient. However, the number of rock types identified by different rock typing indices is different, meaning that samples belonging to the same rock type in the k−F plot of the RTQI method may be classified into different rock types in the k−ϕ plot of the FZI method and the F−ϕ plots of the CZI and EQI methods, which may be caused by the microscopic differences of rock typing parameters. Different from the strong dependence of FZI on hydraulic tortuosity and CZI/EQI on electrical tortuosity, RTQI is mainly dominated by the ratio of electrical tortuosity to hydraulic tortuosity. Additionally, in view of the good correlation between petrophysical data after rock typing, two workflows are proposed to estimate the hydraulic and electrical parameters of carbonate rocks from log data.

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Section: Introductionmentioning

confidence: 99%

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

et al. 2023

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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%

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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Enhancing permeability prediction in tight and deep carbonate formations: new insights from pore description and electrical property using gene expression programming

Rostami,

Helalizadeh,

Moghaddam

et al. 2024

Arab J Geosci

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Application of electrical rock typing for quantification of pore network geometry and cementation factor assessment

Cited by 11 publications

References 46 publications

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

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

Enhancing permeability prediction in tight and deep carbonate formations: new insights from pore description and electrical property using gene expression programming

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