Vp‐Vs relationship and amplitude variation with offset modelling of glauconitic greensand‡

Hossain, Zakir; Mukerji, Tapan; Fabricius, Ida Lykke

doi:10.1111/j.1365-2478.2011.00968.x

Cited by 33 publications

(4 citation statements)

References 39 publications

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“…The shale represents the cap-rock for the greensand. Shale data for AVO curves were obtained from the studied Nini 1A well (Hossain et al 2012).…”

Section: A Relationship Between Rock Physics and Nmrmentioning

confidence: 99%

A relationship between rock physics and nuclear magnetic resonance

Hossain¹

2016

SEG Technical Program Expanded Abstracts 2016

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Historically nuclear magnetic resonance (NMR) is used as a useful tool in petrophysical based reservoir evaluation. The objective of this study is to define a relationship between NMR T 2 distribution and seismic attributes for accurately rock properties prediction. To define rock properties, we used laboratory NMR and ultrasonic measured data, rock physics, and AVO analysis. From this study, we define the following relationship: where,  total is the total porosity, and are the Lame` parameters related with P-impedance and S-impedance, T 2i is the NMR T 2 distribution of each pore, V PFM is the volume of pore-filling mineral (PFM), Sg is the gas saturation, R  is the or S-reflection coefficient, R  is the (reflection coefficient. This study shows that NMR T 2 distributions are directly related with seismic attributes. Therefore, integrating NMR data with rock physics analysis provides a solid basis for quantitative seismic petrophysical interpretation by minimizing interpretation risk.

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“…The shale represents the cap-rock for the greensand. Shale data for AVO curves were obtained from the studied Nini 1A well (Hossain et al 2012).…”

Section: A Relationship Between Rock Physics and Nmrmentioning

confidence: 99%

A relationship between rock physics and nuclear magnetic resonance

Hossain¹

2016

SEG Technical Program Expanded Abstracts 2016

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“…Among these, the empirical formula methods have gained popularity among geophysicists due to their ease of use and faster application. Many researchers have put forth linear or nonlinear empirical relationships between compressional and shear wave velocities [4][5][6][7][8][9]. However, the accuracy of predictions with empirical formula methods tends to be limited and lacks generalizability, primarily due to variations in regions and lithologies.…”

Section: Introductionmentioning

confidence: 99%

Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities

Liu,

Gui,

Gao

et al. 2023

Processes

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As the exploration of unconventional reservoirs progresses, characterizing challenging formations like tight sandstone becomes increasingly complex. Anisotropic parameters play a vital role in accurately characterizing these unconventional reservoirs. In light of this, this paper introduces an approach that uses a dual-constraint anisotropic rock physics model based on compressional and shear wave velocities. The proposed method aims to enhance the precision of anisotropic parameter calculations, thus improving the overall accuracy of reservoir characterization. The paper begins by applying a convolutional neural network (CNN) to predict shear wave velocity, effectively resolving the issue of incomplete shear wave logging data. Subsequently, an anisotropic rock physics model is developed specifically for tight sandstone. A comprehensive analysis is conducted to examine the influence of quartz, clay porosity aspect ratio, and fracture density on compressional and shear wave velocities. Trial calculations using the anisotropic model data demonstrated that the accuracy of calculating anisotropic parameters significantly improved when both compressional and transverse wave velocity constraints were taken into account, as opposed to relying solely on the compressional wave velocity constraint. Furthermore, the rationality of predicting anisotropic parameters using both the shear wave velocity predicted by the convolutional neural network and the measured compressional wave velocity was confirmed using the example of deep tight sandstone in the Junggar Basin.

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“…In addition to lithology, porosity is also related to sonic velocities in shaly sandstone or carbonate rocks (Rafavich et al, 1984;Han et al, 1986). The relationship between compressional velocity and shear velocity were investigated in formations with different lithologies (Hossain et al, 2012). This relationship can be used to predict shear velocity for wells lack of shear velocity data.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of elastic properties of marl and limestone layers in the Eagle Ford formation

Jiang

Mokhtari²,

Song³

2023

Front. Earth Sci.

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Eagle Ford Formation has significant heterogeneity due to the existence of marl and interbedded limestone layers. The objective of this paper is to study the elastic properties of different layers in the Eagle Ford Formation. To achieve the goal, the relationships between compressional and shear velocities in marl and limestone layers were investigated in two representative Eagle Ford wells. These empirical equations can be used to estimate the shear velocity in Eagle Ford wells without sufficient well log data. Moreover, correlations between elastic properties and GR were obtained. Among all layers in the Eagle Ford Formation, marl layers of the lower Eagle Ford have the lowest averaged values of compressional velocity, shear velocity and dynamic Young’s modulus, while the limestone layers of the upper Eagle Ford have the highest averaged values of these three elastic parameters. In addition, the effect of elastic properties of shale layers on the aspect ratio of unconfined and confined fractures were evaluated. The influence of Young’s modulus contrast of shale layers on the aspect ratio of confined fractures was remarkable.

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V_p‐V_s relationship and amplitude variation with offset modelling of glauconitic greensand^‡

Cited by 33 publications

References 39 publications

A relationship between rock physics and nuclear magnetic resonance

A relationship between rock physics and nuclear magnetic resonance

Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities

Comparative study of elastic properties of marl and limestone layers in the Eagle Ford formation

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