• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
The use of image analysis software to quantify porosity. Medina (2013)

The use of image analysis software to quantify porosity. Medina (2013)







Total Views
Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment
  • “what these techniques can tell us about…..
  • Sandstone (sometimes known as arenite) is a clastic sedimentary rock composed mainly of sand-sized minerals or rock grains.Most sandstone is composed of quartz and/or feldspar because these are the most common minerals in the Earth's crust. Like sand, sandstone may be any colour, but the most common colours are tan, brown, yellow, red, gray, pink, white and black. Since sandstone beds often form highly visible cliffs and other topographic features, certain colors of sandstone have been strongly identified with certain regions.Porosity or void fraction is a measure of the void (i.e., "empty") spaces in a material, and is a fraction of the volume of voids over the total volume, between 0–1, or as a percentage between 0–100%. The term is used in multiple fields including pharmaceutics, ceramics, metallurgy,materials, manufacturing, earth sciences and soil mechanics.A sandstone classification provides a single name, perhaps with a couple of modifiers, that conveys important information about the rock. Classifications can be based on descriptive, genetic, or textural parameters (or a combination of these). Classifications can be designed to describe the major framework
  • Grain, porosity, and authigenic minerals
  • An authigenic mineral or sedimentary rock deposit is one that was generated where it is found or observed. Authigenic sedimentary minerals form during sedimentation by precipitation or recrystallization[1] instead of being transported from elsewhere (allogenic) by water or wind.[2] Authigenic sediments are the main constituents of deep sea sedimentation. Authigenic clays tend to reduce the porosity of sediments, thus reducing permeability.Common sedimentary authigenic minerals include calcium carbonate,[3] apatite,[4] and clays.[5]In metamorphic petrology an authigenic mineral is one formed in situ during metamorphism, again by precipitation from fluids or recrystalisation.For any mineral to be precipitated, the water must be oversaturated with respect to that mineral. For calcite, this means that the area of deposition must be above the carbonate compensation depth, or that the pore waters are sufficiently saturated due to dissolution of other grains that precipitation can begin. The alkalinity can also be reduced by microbial sulphate reduction.[6]
  • This is what we see in the microscope. For now I will analyze porosity ONLY. More analysis including grain shape, overgrowth proportions will be also part of this study at a later time.
  • 2.2 Controls on PorosityThe initial (pre-diagenesis) porosity is affected by three major microstructural parameters. These aregrain size, grain packing, particle shape, and the distribution of grain sizes. However, the initialporosity is rarely that found in real rocks, as these have subsequently been affected by secondarycontrols on porosity such as compaction and geochemical diagenetic processes. This section brieflyreviews these controls.2.2.1 Grain PackingThe theoretical porosities for various grain packing arrangements can be calculated. The theoreticalmaximum porosity for a cubic packed rock made of spherical grains of a uniform size is 0.4764, and isindependent of grain size. The maximum porosity of other packing arrangements is shown in Table 2.1and Figure 2.1.
  • 2.2.2 Grain SizeIt was noted above that the ordered cubic packing of identical sphere leads to a porosity that is grainsize independent. This is also true for the other ordered packing lattices, but not true for the randomarrangement of spheres. In real depositional environments, ordered packings are not formed becausethey are energetically unstable, and the grains become randomly distributed.The equilibrium porosity of a porous material composed of a random packing of spherical grains isdependent upon the stability given to the rock by frictional and cohesive forces operating betweenindividual grains. These forces are proportional to the exposed surface area of the grains. The specificsurface area (exposed grain surface area per unit solid volume) is inversely proportional to grain size.This indicates that, when all other factors are equal, a given weight of coarse grains will be stabilizedat a lower porosity than the same weight of finer grains. For a sedimentary rock composed of a givensingle grain size this general rule is borne out in Figure 2.3. It can be seen that the increase in porosityonly becomes significant at grain sizes lower than 100 mm, and for some recent sediments porositiesup to 0.8 have been measured. As grain size increases past 100 mm, the frictional forces decrease andthe porosity decreases until a limit is reached that represents random frictionless packing, whichoccurs at 0.399 porosity, and is independent of grain size. No further loss of porosity is possible forrandomly packed spheres, unless the grains undergo irreversible deformation due to dissolution-recrystallisation,fracture, or plastic flow, and all such decreases in porosity are termed compaction.Rarely, in borehole petrophysics do we need to look at accurate grain size determinations as many ofthe tools that we use have minimum vertical resolutions of the order of tens of centimetres. However,as well logs are correlated to core logging it is well to bear in mind the agreed semi-quantitativeclassifications for grain size in siliclastic and carbonate rocks (Tables 2.2 and 2.3).2.2.3 Grain ShapeThis parameter is not widely understood. Several studies have been carried out on random packings ofnon-spherical grains, and in all cases the resulting porosities are larger than those for spheres. Table2.4 shows data for various shapes, where the porosity is for the frictionless limit. Figure 2.3 showsdata comparing rounded and angular grains, again showing that the porosity for more angular grains islarger than those that are sub-spherical.2.2.4 Grain Size DistributionReal rocks contain a distribution of grain sizes, and often the grain size distribution is multi-modal.The best way of understanding the effect is to consider the variable admixture of grains of two sizes(Figure 2.4). The porosity of the mixture of grain sizes is reduced below that for 100% of each size.There are two mechanisms at work here. First imagine a rock with two grain sizes, one of which has1/100th the diameter of the other. The first mechanism applies when there are sufficient of the largergrains to make up the broad skeleton of the rock matrix. Here, the addition of the smaller particlesreduces the porosity of the rock because they can fit into the interstices between the larger particles.The second mechanism is valid when the broad skeleton of the rock matrix is composed of the smallergrains. There small grains will have a pore space between them. Clearly, if some volume of thesegrains are removed and replaced with a single solid larger grain, the porosity will be reduced becauseboth the small grains and their associated porosity have been replaced with solid material. The solidlines GR and RF or RM in Figure 2.4 represent the theoretical curves for both processes. Note that asthe disparity between the grain sizes increases from 6:3 to 50:5 the actual porosity approaches thetheoretical lines. Note also that the position of the minimum porosity is not sensitive to the graindiameter ratio. This minimum occurs at approximately 20 to 30% of the smaller particle diameter. Inreal rocks we have a continuous spectrum of grain sizes, and these can give rise to a complex scenario,where fractal concepts become useful.2.2.5 Secondary Controls on PorosityPorosity is also controlled by a huge range of secondary processes that result in compaction anddilatation. These can be categorized into (i) mechanical processes, such as stress compaction, plasticdeformation, brittle deformation, fracture evolution etc., and (ii) geochemical processes, such asdissolution, repreciptation, volume reductions concomitant upon mineralogical changes etc. Thedescription of these process will be examined in the sedimentary techniques courses.2.2.6 The Range of Porosity Values in NatureIn recently deposited, unconsolidated sediments, such as those that you might find on the floor of alake, porosity may be very high (values up to 80% have been recorded). However, more commonmaterials, such as loose sands, can have porosities as high as 45% that are either extremely unstable orstabilized by cements. High porosities can also occur when the porosity is due to dissolution(secondary porosity), particularly in carbonates. In the case of carbonates the total porosity may bevery high, but their permeability can be very low as the pores and vugs that make up the pore structureare unconnected. Similarly, porosities can be very low. In massive fractured carbonates it cancommonly be as low as 1%, and igneous and metamorphic rocks almost always have porosities lessthat 1%. Sandstones, generally, lie in the range 5% to 20%. Table 2.6 gives approximate ranges ofporosities for some common lithologies.
  • Talk about limitations of this as it’s a 2D measurement and fluid/rock properties are 3D in nature
  • 2.3 Porosity DeterminationThe best way of determining porosity is to carry out experiments on core extracted from the well.These techniques will be examined in detail in the Formation Evaluation course later in the MSc.However, the basic techniques will be described here. It should be noted that core determinedporosities have a much higher degree of accuracy than porosities determined from down-hole tools,but suffer from sampling problems. Taken together core and borehole determined porosities optimizeaccuracy and high resolution sampling.There are at least 4 common methods of measuring the porosity of a core. These are:· Buoyancy· Helium porisimetry· Fluid saturation· Mercury porosimetry

The use of image analysis software to quantify porosity. Medina (2013) The use of image analysis software to quantify porosity. Medina (2013) Presentation Transcript

  • Cristian Medina IGS Spring Seminar, March 2013
  • Overview • Introduction • Study Area • Methodology • Results • Conclusions • Future Work Special thanks to the Patton award, Maria Mastalerz, Enrique Merino, and John Rupp.
  • Introduction to Sandstones Architecture & Classification Source: Folk, 1980. Sandstone (0.0625 – 2 mm) (Rock Fragments) (Feldspar) (Quartz)
  • Sandstone’s Main Components: Grains, Porosity, and Authigenic Minerals Source: Milliken et al., 2002.
  • Authigenic Minerals (red) Source: Milliken et al., 2002. Source: Mount Simon, 3,597 ft. depth.
  • Porosity Source: Milliken et al., 2002.
  • Idealized pore network, with cubic packing in three-dimensional (3-D). Source:Glover,PetrophysicsMScCourseNotes Grain Arrangement and Porosity
  • Source:Glover,PetrophysicsMScCourseNotes Relation between Porosity, Grain Size, and Grain Shape
  • Source: Glover, Petrophysics MSc Course Notes Porosity Calculation
  • Range of Porosity Values for Rocks Source: Glover, Petrophysics MSc Course Notes
  • Other geologic aspects where porosity is relevant: • Oil and gas reservoir • Water flow and storability • Injection of waste fluids • Gas storage
  • Kozeny-Carman Equations Source: Koltermann and Gorelick, 1995
  • Study Area
  • Cross Section and Location of Samples Northern Indiana
  • Depositional Environment: Eolian Sands Source: Werner and Merino, 1997 3,433 ft.
  • Depositional Environment: Eolian Sands 3,160 ft.
  • Eolian Sands 3,190 ft. 3,165 ft. Source: Milliken et al., 2002.
  • The largest gypsum dune field in the world is located at White Sands National Monument in south-central New Mexico (Tularosa Basin). Source: AAPG Bulletin Cover, Vol. 96. n. 11., November 2012. Source: Wilkens, Personal Communication Depositional Environment
  • Methodology ImageJ is a public domain image processing program developed at the National Institutes of Health. The source code for ImageJ is freely available. Source: Rasband, 1997 Porosity ( ) • Core Analysis/Core • Two-Dimensional porosity (2D Porosity)
  • Scale Considerations 6,090 ft.
  • Effect of Area Selection: Problem = 12 % = 6 % 5,315 ft.
  • Effect of Area Selection: Mitigation ~ 5 mm ~10mm 3,361 ft.
  • Methodology: Thin Sections, Photomicrographs, and Pre-processing Thin Section (30 m) 1mm=103 microns ( m)
  • Saturation: 0-255 Saturation: 20-255 Saturation: 40-255 Saturation: 60-255 Saturation: 80-255 Saturation: 100-255 Saturation: 120-255 Adjusting Saturation
  • Adjusting Saturation 3,465 ft.
  • What can we Measure?
  • A Few Shape Descriptors
  • Adjusting Pore Size
  • Results
  • 0.00 5.00 10.00 15.00 20.00 25.00 1 10 100 1000 10000 2DPorosity Pore Size (microns2) ImageJ Porosity vs. Pore Size (All Samples) Sample 1 Sample ‘n’ Sample 15
  • 2D Porosity versus Minimum Pore Size* *1, 10, 20, 100, 200, 500, 1000, 2000, and 10000 µm
  • 2D Porosity versus Minimum Pore Size
  • 2D Porosity versus Minimum Pore Size
  • 2D Porosity versus Minimum Pore Size
  • 2D Porosity versus Minimum Pore Size
  • 2D Porosity versus Minimum Pore Size
  • 2D Porosity vs. Core Porosity (pores larger than 0, 1, and 10 µm2) Y = 1.13 * X R2 = 0.55 Y = 1.15 * X R2 = 0.53 Y = 1.13 * X R2 = 0.55
  • 2D Porosity vs. Core Porosity (pores larger than 20, 100, and 200 µm2) Y = 0.97 * X R2 = 0.55 Y = 1.05 * X R2 = 0.58 Y = 1.01 * X R2 = 0.54
  • 2D Porosity vs. Core Porosity (pores larger than 500, 1000, and 2000 µm2) Y = 0.86 * X R2 = 0.47 Y = 0.94 * X R2 = 0.53 Y = 0.91 * X R2 = 0.50
  • 2D Porosity vs. Core Porosity (best 1:1 and correlation obtained at 100 µm2) y = 1.009x R² = 0.544 0 5 10 15 20 25 0 5 10 15 20 25 2DPorosity(>100µm2) Core Porosity (%)
  • 1.00 10.00 100.00 1000.00 10000.00 10 100 Permeability(md) k=d2*phi3/(1-phi)2 1.00 10.00 100.00 1000.00 10000.00 10 100 Permeability(md) k=d2*phi3/(1-phi)2 Other use: Predictability SAMPLE ID Permeability (md) k=d2 *phi3 /(1-phi)2 133540_3160 21.20 55.80 143816_3085-90 3.94 53.15 133540_3190 3.95 76.09 142097_3289a 23.00 82.99 133708_3391 1.40 28.39 133708_3465 7.24 61.15 133540_3165 7.55 20.70 133708_3361 320.00 51.69 133708_3418 31.80 31.23 133708_3433 106.00 29.12 142098_3897.5 53.00 39.02 142098_3898a 35.00 31.70 142098_3933 28.00 27.71 142098_3937 27.00 26.05 142098_3947a 1310.00 45.08 144409_4450.5 943.00 62.75
  • Conclusions • Image J provides a cheap and powerful tool to calculate porosity. • Differences observed from 2-D porosity and core porosity might be associated with specific location of samples, adjustment of saturation, and/or pore size. • Pore and grain shape analysis, along with estimated porosity can shed light on permeability prediction. • Optical continuity between grains and overgrowth makes quantifying the reduction of porosity a challenge. • These techniques can be applied to other fields in geology (like point- counting, grain-size analysis, pore classification, and quantifying mineral phases).
  • Future Work • Include samples with clay. • Measure/quantify overgrowth. • Incorporate grain shape analysis. • Add other lithologies/facies (ie., silty sandstone, arkoses) to analysis.
  • References Cited • AAPG Bulletin Cover, Vol. 96, n. 11, November 2012. • Folk, R.L., 1980, Petrology of sedimentary rocks. Hemphill Publishing Co., Austin, Texas. 182 p. • Glover, 2012, Petrophysics. MSc Course Notes. Online document: http://www.scribd.com/doc/26882986/Petrophysics-MSc-Course-Notes Last Accessed: March 2013. • Koltermann, C.E., and Gorelick, S.M., 1995, Fractional packing model for hydraulic conductivity derived from sediment mixtures: Water Resources Research, v. 31, p. 3283-3297. • Milliken, K., Choh, S., McBride, E. F., 2002, Sandstone Petrology (v. 1.0): A Tutorial Petrographic Image Atlas. AAPG/Datapages Discovery Series –No. 6. • Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997-2012. • Werner, B.T., and Merino, E., 1997, Concave sand grains in eolian environments; evidence, mechanism, and modeling: Journal of Sedimentary Research, v. 67, p. 754-762.