Petrophysical rock typing and permeability prediction in tight sandstone reservoir

Lis-Śledziona, Anita

doi:10.1007/s11600-019-00348-5

Cited by 29 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different rock typing methods have been developed to categorize reservoir rocks into petrophysical units based on geological characteristics (Dou et al, 2011;El Sawy et al, 2020). Previous studies have shown the importance of lithology/mineralogy, texture, and porosity on the pore compressibility of reservoir carbonate rocks (Aloki Bakhtiari et al, 2011;Lis-Śledziona, 2019;Satter and Iqbal, 2016;Sui et al, 2020). The selection of rock typing method with the aim of sample selection for further experimental tests, needs to have an adequate understanding and distinguishing between different classi cation criteria.…”

Section: Petrographic Studies and Rock Typingmentioning

confidence: 99%

Estimation of Pore Volume Compressibility in Carbonate Reservoir Rocks Based on a Classification

Moosavi

Bakhtiari²,

Honarmand³

2022

Geotech Geol Eng

View full text Add to dashboard Cite

Taking a vast range of carbonate reservoir rock from Asmari and Bangestan formations in southern Iran basins, this study examined the petrographically classi cation, petrological and petrophysical characteristics, and their implications on the estimation of pore volume compressibility of the carbonate reservoirs. In the current study, a method is developed to classify the carbonate reservoir rocks based on the dominant factors which is involving in elastic property of pore volumes. In order to classifying, a number of 3702 thin sections were studied. Then, the pore volume compressibility of 200 core plugs corresponding to the range of classi cation parameters were obtained and quanti ed by a pre-proven equation. The results clearly show an acceptable narrow bandwidth between the upper and lower bound of estimations based on the studied classi cation. Furthermore, the estimation of pore compressibility-stress relationship was in a good agreement with the experimental observations. Also, the study shows that integrating the routine petrophysical properties are useful for estimation of stress related properties of pore volumes into carbonate reservoir rocks.

show abstract

Section: Petrographic Studies and Rock Typingmentioning

confidence: 99%

Estimation of Pore Volume Compressibility in Carbonate Reservoir Rocks Based on a Classification

Moosavi

Bakhtiari²,

Honarmand³

2022

Geotech Geol Eng

View full text Add to dashboard Cite

show abstract

“…The differential pressure was kept constant at 200-500 mbar depending on the sample properties. The intrinsic sample permeability was derived from the apparent gas permeability, K g , determined for each pressure step using Darcy's law (Liu et al, 2017):…”

Section: Permeability Measurements and Pore Size Distributionmentioning

confidence: 99%

Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging

et al. 2021

View full text Add to dashboard Cite

Abstract. Computer X-ray microtomography (µXCT) represents a powerful tool for investigating the physical properties of porous rocks. While calculated porosities determined by this method typically match experimental measurements, computed permeabilities are often overestimated by more than 1 order of magnitude. This effect increases towards smaller pore sizes, as shown in this study, in which nanostructural features related to clay minerals reduce the permeability of tight reservoir sandstone samples. Focussed ion beam scanning electron microscopy (FIB-SEM) tomography was applied to determine the permeability effects of illites at the nanometre scale, and Navier–Stokes equations were applied to calculate the permeability of these domains. With these data, microporous domains (porous voxels) were defined using microtomography images of a tight reservoir sample. The distribution of these domains could be extrapolated by calibration against size distributions measured in FIB-SEM images. For this, we assumed a mean permeability for the dominant clay mineral (illite) in the rock and assigned it to the microporous domains within the structure. The results prove the applicability of our novel approach by combining FIB-SEM with X-ray tomographic rock core scans to achieve a good correspondence between measured and simulated permeabilities. This methodology results in a more accurate representation of reservoir rock permeability in comparison to that estimated purely based on µXCT images.

show abstract

“…Rock typing can be used in various industry disciplines, including finding thief zones in drilling, managing zones with high productivity index in the production phase, detecting zone of interest and building robust numerical reservoir models 4 – 6 . One of the most important usages of rock typing is predicting unknown reservoir properties, specifically permeability in uncored intervals 7 . Coring from several wells is an often unavoidable, essential task in order to obtain basic data on the field.…”

Section: Introductionmentioning

confidence: 99%

A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran

Mohammadian

Kheirollahi

Liu

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Petrophysical rock typing (PRT) and permeability prediction are of great significance for various disciplines of oil and gas industry. This study offers a novel, explainable data-driven approach to enhance the accuracy of petrophysical rock typing via a combination of supervised and unsupervised machine learning methods. 128 core data, including porosity, permeability, connate water saturation (Swc), and radius of pore throats at 35% mercury injection (R35) were obtained from a heterogeneous carbonate reservoir in Iran and used to train a supervised machine learning algorithm called Extreme Gradient Boosting (XGB). The algorithm output was a modified formation zone index (FZIM*), which was used to accurately estimate permeability (R2 = 0.97) and R35 (R2 = 0.95). Moreover, FZIM* was combined with an unsupervised machine learning algorithm (K-means clustering) to find the optimum number of PRTs. 4 petrophysical rock types (PRTs) were identified via this method, and the range of their properties was discussed. Lastly, shapely values and parameter importance analysis were conducted to explain the correlation between each input parameter and the output and the contribution of each parameter on the value of FZIM*. Permeability and R35 were found to be most influential parameters, where Swc had the lowest impact on FZIM*.

show abstract

Petrophysical rock typing and permeability prediction in tight sandstone reservoir

Cited by 29 publications

References 22 publications

Estimation of Pore Volume Compressibility in Carbonate Reservoir Rocks Based on a Classification

Estimation of Pore Volume Compressibility in Carbonate Reservoir Rocks Based on a Classification

Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging

A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran

Contact Info

Product

Resources

About