Automatic classification of carbonate rocks permeability from 1H NMR relaxation data

Silva, Pablo Nascimento da; Gonçalves, Eduardo Corrêa; Rios, Edmilson Helton; Asif, Muhammad; Moss, Adam Keith; Pritchard, Tim; Glassborow, Brent; Plastino, Alexandre; Azeredo, Rodrigo Bagueira de Vasconcellos

doi:10.1016/j.eswa.2015.01.034

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…However, two samples (122.45 and 187.95) had nearly the same porosity but showed a large difference in their permeability (to be discussed later). Figure 5 shows the pore size distributions as a function of the relaxation time using five size partitions as employed by Silva et al (2015). The curves show considerable heterogeneity among the samples, consistent with the variability in the permeability versus porosity relationships shown in Fig.…”

Section: Resultssupporting

confidence: 69%

“…4. The pore partitions of Silva et al (2015), consisted of micropores (T 2 values up to 1 ms), a transition from micropores and mesopores (from 1 to 10 ms), mesopores (10 to 100 ms), a transition from mesopores to macropores (100 to 1000 ms), and macropores (above 1000 ms). As indicated by Gonçalves et al (2017), it is not surprising that most partitions actually contributed to the flow process in our study The MICP measurements produced curves of the capillary pressure as a function of mercury saturation, from which pore size distributions could be extracted (Moctezuma Fig.…”

Section: Resultsmentioning

confidence: 99%

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Petrophysical Correlations for the Permeability of Coquinas (Carbonate Rocks)

et al. 2020

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The pore structure of many carbonate formations is known to be very complex and heterogeneous. Heterogeneity is manifested by the presence of different types, sizes, and shapes of pores resulting from sedimentation and diagenetic actions. These complexities greatly increase uncertainties in estimated rock hydraulic properties in that different permeability values may occur for samples having similar porosities. In order to understand the effects of pore structure and heterogeneity, petrophysical analyses were performed on coquina samples from the Morro do Chaves Formation (Barremian, Sergipe-Alagoas Basin), which is an analogue of Brazilian Pre-salt oil reservoirs of Itapema Formation in the Santos Basin. Routine core analyses, and NMR and MICP measurements were carried out to obtain pore body and pore throat distributions. Obtained T2 relaxation times were converted to pore size radii by matching the NMR and MICP curves. Pore-scale imaging and pore network modelling were performed using microCT scans and the PoreFlow software, respectively. Calculated permeabilities using PoreFlow showed excellent agreement with the routine laboratory measurements. Samples having pore bodies with a higher coordination number showed much larger permeabilities at similar porosities. This study includes a statistical analysis of various features that caused the observed differences in permeability of the coquinas, including the role of connectivity of the entire porous system. Limitations and challenges of the various techniques, and the imaging and pore-scale flow simulations, are discussed.

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Section: Resultssupporting

confidence: 69%

Section: Resultsmentioning

confidence: 99%

Petrophysical Correlations for the Permeability of Coquinas (Carbonate Rocks)

et al. 2020

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“…The similarity among the samples of each group was confirmed based on the shape of the T2 distributions and their logarithmic mean values T2LM (Table 1). The classification of pores was based on qualitative partitioning of the T2 distributions as used by Silva et al (2015). The heterogeneity of the samples is well evidenced by the variation of the pore families within the set, thus reinforcing the T2 partitioning ideas presented by Silva (2015): T2 up to 1 ms is considered to be micropores; between 1 and 10 ms is a transition region between micro and mesopores; from 10 to 100 ms consists of mesopores; from 100 to 1000 ms is a transition zone between meso and macropores, and over 1000 ms are macropores.…”

Section: Resultsmentioning

confidence: 99%

“…The classification of pores was based on qualitative partitioning of the T2 distributions as used by Silva et al (2015). The heterogeneity of the samples is well evidenced by the variation of the pore families within the set, thus reinforcing the T2 partitioning ideas presented by Silva (2015): T2 up to 1 ms is considered to be micropores; between 1 and 10 ms is a transition region between micro and mesopores; from 10 to 100 ms consists of mesopores; from 100 to 1000 ms is a transition zone between meso and macropores, and over 1000 ms are macropores. Figures 2a, 3a, 4a, 5a and 6a show the distributions of T2 for rock types RT1, RT2, RT3, RT4 and RT5, respectively.…”

Section: Resultsmentioning

confidence: 99%

Rock typing of coquinas from the Morro do Chaves Formation

Lima¹,

Martins²,

Rios³

et al. 2019

Proceedings of the 16th International Congress of the Brazilian Geophysical Society&Expogef

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“…Os trabalhos de Flach (2012) e Japkowicz and Shah (2011) são indicados para aqueles que desejam se aprofundar no tema. Adicionalmente, para obter um exemplo de utilização da MC em um problema de classificação monorrótulo multiclasse, consulte [da Silva et al, 2015].…”

Section: Especificidade = Vn ÷ (Vn + Fp)unclassified

Introdução à Classificação Multirrótulo

Gonçalves

2018

Minicursos Da ERSI-RJ 2018 - V Escola Regional De Sistemas De Informação Do Rio De Janeiro

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Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. There are many important and modern applications of MLC, such as text categorization (associating documents to various subjects) and semantic scene classification (categorizing images into concepts).This chapter is intended to those who wish to work with MLC in information systems. We present the basic approaches for the construction and evaluation of multi-label classifiers as well as examples of software libraries that support the development of MLC applications in Java and R.

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Automatic classification of carbonate rocks permeability from 1H NMR relaxation data

Cited by 23 publications

References 21 publications

Petrophysical Correlations for the Permeability of Coquinas (Carbonate Rocks)

Petrophysical Correlations for the Permeability of Coquinas (Carbonate Rocks)

Rock typing of coquinas from the Morro do Chaves Formation

Introdução à Classificação Multirrótulo

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