Tight shale elastic properties using the soft‐porosity and single aspect ratio models

Ruiz, Franklin; Azizov, Ilgar

doi:10.1190/1.3627654

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Stiff pores could remain open at very high pressures, in agreement with Christensen (1974) who has shown that small amounts of high aspect ratio cracks may be preserved in rocks, even at pressures as high as 1 GPa. Ruiz & Azizov (2011) demonstrated, that the dependence of elastic wave velocities on depth (and on pressure) could be described with the same quality by considering a single set of pores with a depth-dependent aspect ratio. When anisotropic materials are considered, it is natural to expect that cracks with different aspect ratios may have different orientation distributions, and these orientation distributions do change with pressure as cracks close within the anisotropic crystalline matrix.…”

Section: Peculiarities and Limitations Of The Model Of Elastic Propermentioning

confidence: 93%

Elastic anisotropy of Tambo gneiss from Promontogno, Switzerland: a comparison of crystal orientation and microstructure-based modeling and experimental measurements

et al. 2017

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Felsic and mafic gneisses constitute large proportions of the upper and lower continental crust. Gneisses often display high anisotropy of elastic properties associated with preferred orientations of sheet silicates. Here we study the elastic anisotropy of a sample of Tambo gneiss from Promontogno in the Central Alps. We apply optical microscopy, time-of-flight neutron diffraction, neutron and X-ray tomography to quantify mineral composition and microstructures and use them to construct self-consistent models of elastic properties. They are compared to results of ultrasonic measurements on a cube sample in a multi-anvil apparatus and on a spherical sample in an apparatus that can measure velocities in multiple directions. Both methods provide similar results. It is shown that models of microstructure-derived elastic properties provide a good match with ultrasonic experiment results at pressures above 100 MPa. At a pressure of 0.1 MPa the correspondence between the model and the experiment is worse. This may be caused by an oversimplification of the model with respect to microfractures or uncertainties in the experimental determination of S-wave velocities and elastic tensor inversion. The study provides a basis to determine anisotropic elastic properties of rocks either by ultrasonic experiments or quantitative models based on microstructures. This information can then be used for interpretation of seismic data of the crust.

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Section: Peculiarities and Limitations Of The Model Of Elastic Propermentioning

confidence: 93%

Elastic anisotropy of Tambo gneiss from Promontogno, Switzerland: a comparison of crystal orientation and microstructure-based modeling and experimental measurements

et al. 2017

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“…Attempts were made to explain this trend by two types of pores -soft and stiff -existing in the material (Shapiro, 2003;Pervukhina et al, 2010). With respect to pore closure with pressure, low-aspect-ratio pores could be considered as "soft" and spherical pores as "stiff" (Ruiz and Azizov, 2011).…”

Section: Linking Orientations To Anisotropy C15mentioning

confidence: 99%

“…It is thus expected that for different shales with, e.g., different degrees of compaction, this ratio may be different. For example, in the study of Ruiz and Azizov (2011), the same two types of pores with aspect ratios f1∶1∶1g and f1∶1∶0.01g are used to describe elastic properties of a low-porosity Barnett Shale. However, in their models (featuring elastically isotropic mineral aggregates) the part of low-aspect-ratio pores in total porosity was less than 25%.…”

Section: Linking Orientations To Anisotropy C15mentioning

confidence: 99%

Linking preferred orientations to elastic anisotropy in Muderong Shale, Australia

et al. 2015

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The significance of shales for unconventional hydrocarbon reservoirs, nuclear waste repositories, and geologic carbon storage has opened new research frontiers in geophysics. Among many of its unique physical properties, elastic anisotropy had long been investigated by experimental and computational approaches. Here, we calculated elastic properties of Cretaceous Muderong Shale from Australia with a self-consistent averaging method based on microstructural information. The volume fraction and crystallographic preferred orientation distributions of constituent minerals were based on synchrotron x-ray diffraction experiments. Aspect ratios of minerals and pores, determined from scanning electron microscopy, were introduced in the self-consistent averaging. Our analysis suggested that phyllosilicates (i.e., illite-mica, illite-smectite, kaolinite, and chlorite) were dominant with [Formula: see text]. The shape of clay platelets displayed an average aspect ratio of 0.05. These platelets were aligned parallel to the bedding plane with a high degree of preferred orientation. The estimated porosity at ambient pressure was [Formula: see text] and was divided into equiaxial pores and flat pores with an average aspect ratio of 0.01. Our model gave results that compared satisfactorily with values derived from ultrasonic velocity measurements, confirming the validity and reliability of our approximations and averaging approach.

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“…By combining seismic and logging data and using different rock physics models, it is possible to estimate the elastic information of a target reservoir and predict the distribution of oil, gas, and water (Khoshdel et al, 2022) [30]. In general, petrophysical parameters (such as porosity and fluid saturation) can be predicted from elastic parameters using different rock physics models, such as sandstone (Keys and Xu, 2002;Mavko et al, 2009;Dvorkin and Nur, 1996) [31][32][33], shale (Ruiz and Azizo, 2011) [34], carbonate (Xu and White, 1995;Xu and Payne, 2009) [35,36], and fracture (Schoenberg and Sayers, 1995;Bakulin et al, 2000) [37,38] reservoir models. Elastic parameters are commonly obtained with seismic inversion, which uses elastic information contained in seismic waves.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

Zhou,

Yang,

Wang

et al. 2024

Minerals

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Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir.

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Tight shale elastic properties using the soft‐porosity and single aspect ratio models

Cited by 6 publications

References 7 publications

Elastic anisotropy of Tambo gneiss from Promontogno, Switzerland: a comparison of crystal orientation and microstructure-based modeling and experimental measurements

Elastic anisotropy of Tambo gneiss from Promontogno, Switzerland: a comparison of crystal orientation and microstructure-based modeling and experimental measurements

Linking preferred orientations to elastic anisotropy in Muderong Shale, Australia

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

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