Prediction of pore volume compressibility by a new non-linear equation in carbonate reservoirs

Farahani, Milad; Aghaei, Hamed; Saki, Mohammad; Asadolahpour, Seyed Reza

doi:10.1016/j.engeos.2022.04.005

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…The helium gas was employed to determine effective porosity by gas expansion and permeability measurements. More details of the experimental procedures were explained in previous manuscripts (Farahani et al 2022; Feng and Pandey 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Many researchers have studied the experimental relationships of compressibility, mainly versus porosity (Farahani et al 2022). For example, Horne (1990) proposed some correlations for calculating based on initial porosity for limestone, consolidated, and unconsolidated sandstone rocks.…”

Section: Experimental Relationshipsmentioning

confidence: 99%

“…Hydrocarbons and formation water ow inside the reservoir rock pore bodies. The compression of a reservoir rock results in changing the shape of pore bodies and closing ow channels, consequently reducing the formation permeability (Farahani et al 2022). However, this compression provides part of the necessary energy for oil out ow.…”

Section: Introductionmentioning

confidence: 99%

“…So far, the proposed relations were only based on the initial porosity. Farahani et al (2022) derived an empirical relationship between measured pore compressibility and porosity at each stress step for a carbonate reservoir. They considered the importance of the net stress effect.…”

mentioning

confidence: 99%

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Prediction of Reservoir Compressibility Using Subsurface Cores, Well Logs, and Seismic Data by Neural Network

VALI,

HajiZadeh

2023

Preprint

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In this study, three-dimensional pore volume compressibility of a carbonate reservoir was predicted. The primary data of the model were petrophysical parameters, measured compressibility factor on core samples, conventional well logs, and three-dimensional seismic attributes. Neural network algorithms were employed to propagate the compressibility data along the well axis and to predict the distribution of compressibility within three-dimensional seismic acquisition area. A probabilistic neural network algorithm resulted in a correlation of 85% between the predicted and measured compressibility along the wells-axis. The seismic attributes were extracted to find the best correlation and minimum error between the generated and target attributes. The correlation coefficient of 78% indicates the high accuracy of the model and the optimal choice of neural network algorithms. The results of this study provide insights into the application of seismic data to field-wide prediction of reservoir compressibility.

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Section: Methodsmentioning

confidence: 99%

Section: Experimental Relationshipsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Prediction of Reservoir Compressibility Using Subsurface Cores, Well Logs, and Seismic Data by Neural Network

VALI,

HajiZadeh

2023

Preprint

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“…To deal with these issues, several empirical relationships have been proposed as an alternative for lab-based estimation of Cp (e.g. see [8][9][10][11][12][13]).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Pore Compressibility Lab-based measurements; Case Study of Supercritical CO2 Exposure and Dissolution

Ali¹,

ToorajiPour²,

Aghaei³

et al. 2022

Preprint

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Injection of carbon dioxide into underground geological formations with proper structure is a common approach performed, such as, for geo-sequestration purposes. During this operation, host rock may then undergo a series of chemical and petrophysical alterations including dissolution and changes in pore structure. Subsequently, knowledge of potential variation in reservoir rock properties against these alterations is of great interest in planning for the geological storage of CO2. Amid these properties is the pore compressibility as a key factor in understanding of rock geomechanical behaviors. However, lab-based analysis of pore compressibility is a challenging procedure with ongoing issues. The present work provides a comparison between two different approaches for lab-based measurement of pore compressibility. The work compares usage of sister plugs and a single one to determine the effect of dissolution and applied pressure on pore compressibility in limestone samples exposed to supercritical CO2. For this purpose, core-flooding experiments with pore-water rich in CO2 were carried out to resemble real reservoir condition while the samples were undergone pore compressibility measurements with the above-mentioned approaches. In the end, it was revealed that using a single plug will provide more precise results compared to the sister plugs, however, attention must be paid to a number of critical parameters. In fact, injection and confining pressures are potential to cause pore closure, and so change the internal structure, during pre-injection pore compressibility measurement. Moreover, in case of using sister-plug approach, it is important to screen the samples based on their pore type and internal structure,

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Deformation of the void space of pores and fractures of carbonates: Comprehensive analysis of core and field data

Martyushev,

Ponomareva,

Davoodi

et al. 2025

Energy Geoscience

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Prediction of pore volume compressibility by a new non-linear equation in carbonate reservoirs

Cited by 6 publications

References 18 publications

Prediction of Reservoir Compressibility Using Subsurface Cores, Well Logs, and Seismic Data by Neural Network

Prediction of Reservoir Compressibility Using Subsurface Cores, Well Logs, and Seismic Data by Neural Network

Optimization of Pore Compressibility Lab-based measurements; Case Study of Supercritical CO2 Exposure and Dissolution

Deformation of the void space of pores and fractures of carbonates: Comprehensive analysis of core and field data

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