Micromorphology of soil structure - Description, quantification, application

Soil Research ◽  
1991 ◽  
Vol 29 (6) ◽  
pp. 777 ◽  
Author(s):  
AJ Ringrose-Voase
Keyword(s):  
Soil Structure ◽  
Pore Space ◽  
Structural Parameters ◽  
Thin Sections ◽  
Physical Measurements ◽  
Undisturbed Samples ◽  
Structure Description ◽  
Management Techniques ◽  
Pore Types ◽  
Soil Behaviour

Micromorphological observation can provide insights into soil structure and aid interpretation of soil behaviour. Undisturbed samples are taken in the field and impregnated. They are used to prepare thin sections or images of the macropore structure using fluorescent photography. Sections can also be obtained at macro, meso and submicroscopic scales. The various elements of soil structure observed micromorphologically can be classified into pore space, physical, distribution and orientation fabrics, and associated structures. Examples of the importance of features in each category are given. Image analysis, especially when computerized, provides a way of parameterizing micromorphological observations. To date it has been used primarily on images of macropore space at the meso and microscopic scales. Such images can be digitized and segmented to show pore space and solid. The pore space can be allocated to pore types. This aids the estimation of 3-D parameters from I-D and 2-D measurements made on the image using stereology. Various ways of using structural parameters to compare structures are discussed. Applications for micromorphological observations, especially when quantitative, include comparison of structures formed by different management techniques. Structural measurements can aid interpretation of soil behaviour as described by physical measurements. They also have a role in estimating the representative elementary volume, on which physical measurements should be made, and in calibrating field estimates of soil structure.

Movable Fluid Saturation of Low-Permeability and Tight Sandstone Oil Reservoir: Taking Chang 6 Reservoir in Banqiao-Heshui Area as an Example

2015 ◽  
Vol 1092-1093 ◽  
pp. 1420-1423
Author(s):  
Lei Cao ◽  
Wei Sun ◽  
Rui Niu ◽  
Lei Huo ◽  
Fu Tao Qu ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Pore Space ◽  
Physical Property ◽  
Low Permeability ◽  
Oil Reservoir ◽  
Distribution Characteristics ◽  
Tight Sandstone ◽  
Thin Sections ◽  
Fluid Saturation ◽  
Pore Types

By analyzing the thin sections, physical property and nuclear magnetic resonance, the petrology character, pore types and the movable fluid saturation distribution characteristics of Chang 6 reservoir in Banqiao-Heshui area were tested. The research shows that the main lithology of Chang 6 reservoir in study area is lithic arkose. The pore space of reservoir mainly include the solution pores and the primary residual intergranular pores. Fine pore-tiny throat is the main pore configuration. The physical properties of Chang 6 reservoir in Banqiao-Heshui area are weak and it belongs to low-permeability and porosity oil reservoir. There are two models of T2 spectrum, including bimodal and unimodal modes. Movable fluid saturation and fluid porosity varies a lot, the reservoir can be classified as many types by movable fluid saturation and for each of them, its movable fluid saturation differs obviously.

Effect of soil structure on wheat germination in a red-brown earth

1955 ◽  
Vol 6 (6) ◽  
pp. 797 ◽  
Author(s):  
DS McIntyre
Keyword(s):  
Physical Properties ◽  
Soil Structure ◽  
Pore Space ◽  
Oxygen Deficiency ◽  
Heavy Rain ◽  
Physical Measurements ◽  
Wheat Germination ◽  
Permanent Rotation ◽  
Formed Part ◽  
Properties Of Soil

Physical properties of soil have been measured on two experimental plots which showed high compaction and poor germination of wheat under very wet conditions. The plot treatments were a two-course and a four-course rotation, and formed part of a permanent rotation trial at the Waite Institute. Similar physical properties were measured for thick crusts formed on these cultivated soils. Under the wet conditions pore space available for air was low in both plots, and within the crust itself was almost zero. Significant differences were found in bulk density and water-stable aggregation between treatments. Waterstable aggregation was very low under both treatments compared with that in virgin soil. All physical measurements point to an oxygen deficiency for a period of 2-3 weeks, and this could account for the poor germination and poor early growth, particularly in the two-course rotation plots. The bad structure conditions leading to this are probably due to pulverizing by implements and dispersion over the years by heavy rain, rather than to decrease in organic matter.

Quantitation in thin Sections Within the Electron Microscope

1976 ◽  
Vol 34 ◽  
pp. 336-337
Author(s):  
J. Edie
Keyword(s):  
Electron Beam ◽  
Electron Microscope ◽  
Electron Scattering ◽  
Volume Density ◽  
Thin Sections ◽  
Faraday Cage ◽  
Local Mass ◽  
Physical Measurements ◽  
Mass Volume ◽  
Varying Mass

In TEM image formation, the observed contrast variations within thin sections result from differential electron scattering within microregions of varying mass thickness. It is possible to utilize these electron scattering properties to obtain objective information regarding various specimen parameters (1, 2, 3).A pragmatic, empirical approach is described which enables a microscopist to perform physical measurements of thickness of thin sections and estimates of local mass, volume, density and, possibly, molecular configurations within thin sections directly in the microscope. A Faraday cage monitors the transmitted electron beam and permits measurements of electron beam intensities.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Permeability-porosity transforms from small sandstone fragments

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. N11-N19 ◽  
Author(s):  
Ayako Kameda ◽  
Jack Dvorkin ◽  
Youngseuk Keehm ◽  
Amos Nur ◽  
William Bosl
Keyword(s):  
Pore Space ◽  
Petroleum Industry ◽  
Rock Physics ◽  
Three Dimensions ◽  
Absolute Permeability ◽  
High Porosity ◽  
Drill Cuttings ◽  
Thin Sections ◽  
Numerical Experimentation ◽  
The Absolute

Numerical simulation of laboratory experiments on rocks, or digital rock physics, is an emerging field that may eventually benefit the petroleum industry. For numerical experimentation to find its way into the mainstream, it must be practical and easily repeatable — i.e., implemented on standard hardware and in real time. This condition reduces the size of a digital sample to just a few grains across. Also, small physical fragments of rock, such as cuttings, may be the only material available to produce digital images. Will the results be meaningful for a larger rock volume? To address this question, we use a number of natural and artificial medium- to high-porosity, well-sorted sandstones. The 3D microtomography volumes are obtained from each physical sample. Then, analogous to making thin sections of drill cuttings, we select a large number of small 2D slices from a 3D scan. As a result, a single physical sample produces hundreds of 2D virtual-drill-cuttings images. Corresponding 3D pore-space realizations are generated statistically from these 2D images; fluid flow is simulated in three dimensions, and the absolute permeability is computed. The results show that small fragments of medium– to high-porosity sandstones that are statistically subrepresentative of a larger sample will not yield the exact porosity and permeability of the sample. However, a significant number of small fragments will yield a site-specific permeability-porosity trend that can then be used to estimate the absolute permeability from independent porosity data obtained in the well or inferred from seismic techniques.

Modelling mutual interactions between soil structure and soil biological communities.

2021 ◽  
Author(s):  
Tancredi Caruso
Keyword(s):  
Habitat Structure ◽  
Soil Structure ◽  
Pore Space ◽  
Point Of View ◽  
Species Number ◽  
Ecological Communities ◽  
Soil Organisms ◽  
Biological Communities ◽  
Key Factor ◽  
Complex Habitat

<p>Habitat structure is a key factor controlling the structure of ecological communities. For example, complex habitat structure may increase species number, minimise competition and facilitate the retention of nutrients. Alteration and disturbance of habitat structure may thus negatively affect biodiversity. Soil is an extremely complex and highly structured environmental matrix. Soil structure, defined as a distribution of aggregate/pore space of different sizes, can thus be a major control of soil biological communities, which are for example highly structured in their size distribution. Soil organisms, however, also affect and modify soil structure, and for many organisms the soil habitat structure is thus not just a condition to which they have to adapt but, rather, an environmental feature they also affect. In this talk, I discuss all these aspects from a community ecology point of view and with an emphasis on statistical and dynamical models that soil ecologists are trying to develop to describe and predict the mutual interactions between soil structure and biological communities. I will focus on the different rates at which soil structure affects soil organisms and vice versa, to emphasise that the temporal scales at which we have to measure the two parts of this mutual feedback (i.e. soil structure -> biota vs. biota -> soil structure) are very different, and also variable in space and time. </p>

Obvious and less obvious processes of aggregate formation in soil

2021 ◽  
Author(s):  
Hans-Jörg Vogel ◽  
Mar­ia Balseiro-Romero ◽  
Philippe C. Baveye ◽  
Alexandra Kravchenko ◽  
Wilfred Otten ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Structure ◽  
Solid Phase ◽  
Pore Space ◽  
Imaging Techniques ◽  
Mineral Soil ◽  
Aggregate Formation ◽  
Conceptual Approach ◽  
Soil Matrix ◽  
Biophysical Processes

<p>Soil structure, lately referred to as the ''architecture'' is a key to explain and understand all soil functions. The development of sophisticated imaging techniques over the last decades has led to significant progress in the description of this architecture and in particular of the geometry of the hierarchically-branched pore space in which transport of water, gases, solutes and particles occurs and where myriads of organisms live. Moreover, there are sophisticated tools available today to also visualize the spatial structure of the solid phase including mineral grains and organic matter. Hence, we do have access to virtually all components of soil architecture.</p><p>Unfortunately, it has so far proven very challenging to study the dynamics of soil architecture over time, which is of critical importance for soil as habitat and the turnover of organic matter. Several largely conflicting theories have been proposed to account for this dynamics, especially the formation of aggregates. We review these theories, and we propose a conceptual approach to reconcile them based on a consistent interpretation of experimental observations and by integrating known physical and biogeochemical processes. A key conclusion is that rather than concentrating on aggregate formation in the sense of how particles and organic matter reorganize to form aggregates as distinct functional units we should focus on biophysical processes that produce a porous, heterogeneous organo-mineral soil matrix that breaks into fragments of different size and stability when exposed to mechanical stress.  The unified vision we propose for soil architecture and the mechanisms that determine its temporal evolution, should pave the way towards a better understanding of soil processes and functions.</p>

Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition

2021 ◽  
Author(s):  
Hesham Talaat Shebl ◽  
Mohamed Ali Al Tamimi ◽  
Douglas Alexander Boyd ◽  
Hani Abdulla Nehaid
Keyword(s):  
Thin Section ◽  
Image Recognition ◽  
Oil Recovery ◽  
Reservoir Rock ◽  
Supervised Machine Learning ◽  
Neural Net ◽  
Data Set ◽  
Thin Sections ◽  
Rock Quality ◽  
Pore Types

Abstract Simulation Engineers and Geomodelers rely on reservoir rock geological descriptions to help identify baffles, barriers and pathways to fluid flow critical to accurate reservoir performance predictions. Part of the reservoir modelling process involves Petrographers laboriously describing rock thin sections to interpret the depositional environment and diagenetic processes controlling rock quality, which along with pressure differences, controls fluid movement and influences ultimate oil recovery. Supervised Machine Learning and a rock fabric labelled data set was used to train a neural net to recognize Modified Durham classification reservoir rock thin section images and their individual components (fossils and pore types) plus predict rock quality. The image recognition program's accuracy was tested on an unseen thin section image database.

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