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Seismic Brittleness Index Volume Estimation From Well Logs in Unconventional Reservoirs (Part III) – Brittleness From Well Logs




Shales, or mudstones, are described as organic-rich, fine grained reservoirs (Bustin, 2006) typically dominated (but not exclusively) by clays. The mineral composition and the presence of organic matter can influence not only the distribution of pores and fluid saturation (Sondergeld et al., 2010a), but also the effectiveness of hydraulic stimulation. Subsequently, differentiating brittle from ductile rocks is key to efficient well location and completion.

In my previous post (Seismic Brittleness Index Volume Estimation From Well Logs in Unconventional Reservoirs (Part II) – Brittleness Definitions Review) I introduced the term “unconventional reservoir” in a geological sense, as well as reviewed some of most common definitions of “brittleness”.

At this time I would like to define unconventional reservoirs (shale / mudstone) in terms of mineralogical composition, describing some of the most common techniques used to discriminate the mineralogical composition of a rock sample, and how brittleness index (BI) is calculated, using specialized well logs. Finally, and more importantly, what are the implications of the BI at the time to perform a hydraulic fracturing job.


Seismic Brittleness III-1
Figure 1. Ternary plot corresponding to the most common unconventional shales (mudstones) reservoirs in the U.S. (Grieser).

Sedimentary rock are formed by several processes during the geological history, such as: burial, compression, and chemical modification of deposited weathered rock debris or sediments at the Earth’s surface. This rock type is composed of minerals, which are inorganic solids that have a crystalline structure and a distinct chemical composition.

Figure 1 shows the mineral composition of some of the unconventional plays in the U.S. Notice that even when the unconventional reservoirs are usually embedded into the same container (“shales / mudstones”) their mineralogical composition varies. This is important to keep in mind because these are the properties that influence the pore distribution and fluid saturation, as well as the effectiveness of hydraulic stimulation.

Lithologically, Montgomery (2005) defines the Barnett Shale as a black siliceous shale with limestone and minor dolomite lithologies. Based on four cored wells, Singh (2008) identified and described nine lithofacies in the Barnett Shale: non calcareous mudstone, calcareous mudstone, calcareous laminae deposits, concretion zones, fossil-shell intervals, phosphatic deposits, flaser to hummocky cross-bedded deposits, dolomitic mudstone, and micrite facies flows.

Bowker (2003) reports that the majority of the production in the Barnett Shale comes from zones with 45% quartz content and only 27% clay. In general, the average porosity is 6% with pore throats typically less than 100 nm (Bowker, 2003).

Figure 2 shows the mineralogy ternary plot corresponding to a representative well in the area of study where the sum of all the clay, quartz, and calcite minerals are displayed on each individual vertex of the ternary plot, indicating that the mineral distribution along the wellbore agrees with Karastathis’s (2007) and Kale’s (2009) previous core based findings. The ECS data indicate that the Lower Barnett Shale has higher quartz content than the Upper Barnett Shale, agreeing with core measurements made by Kale (2009) and Karasthatis (2007).

Based on the pattern outlined by the gamma ray log, Singh (2008) and Perez (2009) described three distinctive gamma ray (GR) log patterns: upward-increasing, upward-decreasing, and constant (Figure 3). These GR log patterns were correlated to lithofacies representing unique depositional environment using cored wells in the Barnett Shale. Kale (2009) and Gao (2011) combined Singh’s (2008) ten petro-facies into three petro-types exhibiting similar petrophysical properties. Kale (2009) based his classification on core measurement of porosity, total organic content (TOC), and total carbonate measurements. These petro-types were then ranked in terms of cumulative production data from three vertically cored wells.

Seismic Brittleness III-2
Figure 2. Rock mineralogical composition ternary plot corresponding to the Barnett Shale.

Analysis to differentiate brittle from ductile rocks has been key to stimulation success in shale gas reservoirs, especially in the Barnett Shale where brittleness is mainly controlled by quartz content. Ductility (the opposite of brittleness) is controlled by clay, calcite, and total organic content. Jarvie et al.’s (2007) and Wang and Gale’s (2009) brittleness index defines ductile and brittle regions in terms of mineralogical content, generating a smooth transition between both regions. In contrast, Grieser and Bray (2007) defined an empirical brittleness cut-off based on Poisson’s ratio and Young’s modulus. The cut-off depends on the shale play in which it is applied and on the expertise of the interpreter.

Seismic Brittleness III-3
Figure 3. Typical set of well logs corresponding to the Barnett Shale. Track 2 shows the GR log, and black arrows indicate the GRP defined by Singh (2008) and Perez (2009).


Rock mineralogy can be obtained from core samples, as well as specialized logging tools designed for this purpose. Several methods exist to quantify rock mineralogy, including X-Ray diffraction (XRD) (Till and Spears, 1969), Fourier Transform Infrared Transmission Spectroscopy (FTIR) (Sondergeld and Rai, 1993; Ballard, 2007), reconstruction from elemental abundances obtained through X-Ray fluorescence (XRF), and elemental capture tools (ECS) (Barson et al., 2005). Sondergeld et al. (2010b) describes in detail the advantages and disadvantages of each technique.

From Core Samples

A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons (from Wikipedia), and is used in rock physic laboratories to analyze the rock composition, revealing the rock structure.

Figure 4 shows a backscattered SEM image of an ion-milled Barnett Shale sample imaged and ion-milled in a dual beam SEM (modified from Sondergeld et al. (2010) and courtesy of OU MPGE Integrated Core Characterization Center). Larger silt (orange) and calcite (yellow) grains are mixed with clay (magenta) particles while intragranular dark objects are interpreted as kerogen (white). The smaller circular darker objects within the kerogen are pores (cyan). Perez (2013) showed that given the granular support and the correlation of TOC with quartz in my study area, the effect of TOC is minimized such that high TOC rocks are “brittle” stratigraphically.

This image from a Barnett Shale core shows that the kerogen is located inside the grains in the sample, and exhibits quasi circular pores (Sondergeld et al., 2010a). At high pressure and temperature I do not expect to find quasi circular pores. This circular structure suggests that the grains around the intragranular kerogen support the stress. In this example high TOC does not significantly imply high ductility and the kerogen does not affect the elastic properties of the rock. Slatt (2011) described and classified a variety of pore types that exist in the Barnett and the Woodford Shale, while Slatt and Abousleiman (2011) concluded that different pore types significantly influence the geomechanical properties.

Seismic Brittleness III-4
Figure 4. Backscattered SEM image of an ion-milled Barnett Shale sample imaged and ion-milled in a dual beam SEM (modified from Sondergeld et al. (2010) and courtesy of OU MPGE Integrated Core Characterization Center).

Other common techniques to analyze rock mineralogy from core samples are:

  • Powder X-Ray Diffraction (XRD) is one of the most common techniques used to examine the physic-chemical composition of rocks. In this technique a rock sample is powdered and illuminated with X-rays of a fixed wave-length. Intensity of the reflected radiation is recorded using a goniometer and guides the discrimination of the (possible) composition of the rock.
  • Fourier Transform Infrared (FTIR) spectroscopy is a technique for determining qualitative mineral identification, and is currently being developed for quantitative mineralogy. Mineral identification is possible because minerals have characteristic absorption bands in the mid-range of the infrared (4000 to 400 cm-1). The concentration of a mineral in a sample can be extracted from the FTIR spectrum because the absorbance of the mixture is proportional to the concentration of each mineral. (Xu, 1993).

Finally, Sondergeld et al. (2010), suggested that the determination of mineralogy from logs requires the following inputs: a) lab mineralogy from XRD, FTIR, etc… Based on several rock sample analysis, he noticed that XRD (X-Ray Diffraction) is usually used to determine mineralogy. However, it generally over predicts quartz in clay rich systems when clay separation is not performed (Sondergeld, et al., 2010).

Mineralogy from well logs

Conventional logs such as gamma ray, neutron porosity, and resistivity are useful to stratigraphically characterize the reservoir. However, these logs do not fully provide the information needed to characterize organic shales in terms of their geomechanical behavior. This additional information can come from the integration of sequence stratigraphy, special analysis techniques, specialized logging tools, and core lab measurements. Recent availability of mineralogy logs such as elemental capture spectroscopy (ECS) and dipole sonic logs enable characterization of a reservoir in terms of its mineral content and elastic properties, providing a means to differentiate lithology types by their completion response.

Elemental capture spectroscopy (ECS) logs were acquired in the area of the study, revealing important vertical and lateral mineralogy variations. The ECS technique measures relative elemental yields based on neutron-induced capture gamma ray spectroscopy, detecting silicon (Si), iron, (Fe), calcium (Ca), sulfur (S), titanium (Ti), gadolinium (Gd), chlorine (Cl), barium (Ba), and hydrogen (H), but not magnesium (Mg). The ECS tool sends neutrons into the wellbore wall while a detector measures the counts and energy spectrum from the released gamma rays. The algorithm combines the resulting spectrum with other logs such as bulk density and photoelectric factor, among others, to interpret the most likely mineral composition of the rock. Track 4 in Figure 4 shows the ECS results, which have been calibrated with several cored wells in the area.


Brittleness index (BI) is a relative measurement that depends on the field and the purpose of the investigation (Altindag and Guney, 2010). One common BI measure is the ratio of compressive strength, c, to tensile strength, t (Baron, 1992; Coates and Parsons, 1966; Aubertin and Gill, 1988; Aubertin et al., 1994; Ribacchi, 2000; Hajiabdolmajid and Kaiser, 2003):
Seismic Brittleness Formula III-1
Since tensile strength and compressive strength are measured only in the laboratory, it is difficult to extend this definition to the reservoir scale. The higher the magnitude of BI, the more brittle the rock. Many rocks have no well-defined yield point because they exhibit non-linear elasticity.

Jarvie et al. (2007) and Wang and Gale (2009) proposed BI definitions based on the mineral composition of the rock, dividing the most brittle minerals by the sum of the constituent minerals in the rock sample, considering quartz (and dolomite, in the case of Wang and Gale, 2009) as the more brittle minerals:
Seismic Brittleness Formula III-2
Seismic Brittleness Formula III-3
where Qz is the fractional quartz content, Dol the dolomite content, Ca the calcite content, TOC the total organic carbon content, and Cly the clay content by weight in the rock.

Jarvie et al.’s (2007) equation estimates BI using quartz in the numerator, and the sum of quartz, clay and calcite in the denominator. In contrast, Wang and Gale’s (2009) equation extends this formulation by including dolomite as a contributor to brittleness in the numerator, and TOC and dolomite in the sum of constituent minerals of the rock in the denominator.

I calculate the brittleness index using Jarvie et al.’s (2007) and Wang and Gale’s (2009) equations using the ECS log data points and show the results in Figure 3 tracks 9 and 10, respectively. Comparing both BI indexes with the mineralogy logs, (Figure 3 track 4), I observe that the zones with high quartz and calcite content are more brittle than the regions with high clay content, which are less brittle (ductile).

In the absence of dolomite, Jarvie et al.’s (2007) and Wang and Gale’s (2009) BI results differ subtly because Wang and Gale (2009) includes TOC in the equation. Since TOC in some zones is close to 10% (wgt.), I will use Wang and Gale’s (2009) equation in the remaining analyses. Notice that I did not apply any well log corrections for quality control because I did not have access to these data.

Similar approach can be used to calculate the BI of each rock formation. Figure 5, shows the BHN (similar to BI) of some of the unconventional shales in the U.S. Remember that these type of index varies upon the mineralogical composition of the rock.

Seismic Brittleness III-5
Figure 5. Brittleness corresponding to several shales in the U.S. (Griser)


When a rock is subjected to increasing stress it passes through three successive stages of deformation: elastic, ductile, and fracture. Based on these behaviors it is possible to classify the rocks into two classes: ductile and brittle.

The measurement of stored energy before failure is known as brittleness, and is a complex function of rock strength, lithology, texture, effective stress, temperature, fluid type (Handin and Hager, 1957; 1958; Handin et al., 1963; Davis and Reynold, 1996), diagenesis, and TOC (Wells, 2004).

Figure 6, shows that, in the case of the Barnett Shale (core area), the GR has a positive relationship with brittleness index. Based on core description, and core lab analysis Singh (2008) described 8 lithofacies in the Barnett Shale. Those lithofacies with high TOC also exhibit high silica content.

Seismic Brittleness III-6
Figure 6. Singh (2008) Barnett Shale lithofacies definition ranked in relation to interpreted relative bottom oxygenation and organic richness (left). Gamma Ray versus. Brittleness Index crossplot classification proposed by Perez (2013); indicating four levels of brittleness: brittle (red), less brittle (orange), less ductile (yellow), and ductile (green) (right).


Sedimentary rocks are composed by several minerals, which will affect the rock mechanics. It is important to know and understand the mineralogical composition of the rock in order using rock samples, and/or log information.

Core and outcrop studies show the Barnett Shale to be dominated by clay- and silt-size sediment with occasional beds of skeletal debris. Organic and biogenic constituents were deposited at the time of the sedimentation of the Barnett Shale and include algal remains, spores, plant remains, sponges, and radiolarians, among others (Slatt, 2011). After sedimentation, chemical reactions lead to the generation of secondary minerals including authigenic clays, calcite, dolomite, quartz, pyrite, and hydrothermal minerals (Slatt and O’Brien, 2011).

Perez (2013) demonstrated that brittle rocks are unable to withstand significant strain before failure and fracture quickly, giving rise to microseismic events when they fail. If the rocks are brittle the injected proppant will keep these fractures open. In contrast, ductile rocks deform plastically and can undergo significant strain prior to fracture. Fractures in more plastic ductile rocks are thought to close about the proppant, thus sealing pathways to fluid flow. Rocks with high brittleness exhibit both naturally occurring and hydraulically induced fractures, although in the Barnett Shale most natural fractures are cemented.


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