Effective evaluation of shale gas reservoirs by means of an integrated approach to petrophysics and geomechanics for the optimization of hydraulic fracturing: A case study of the Permian Roseneath and Murteree Shale Gas reservoirs, Cooper Basin, Australia

Accepted Manuscript Effective Evaluation of Shale Gas reservoirs by Means of an Integrated Approach to Petrophysics and Geomechanics for the Optimization of Hydraulic Fracturing: A case Study of the Permian Roseneath and Murteree Shale Gas Reservoirs, Cooper Basin, Australia Omer Iqbal, Maqsood Ahmad, Askury abd Kadir

PII:

S1875-5100(18)30328-7

DOI:

10.1016/j.jngse.2018.07.017

Reference:

JNGSE 2662

To appear in:

Journal of Natural Gas Science and Engineering

Received Date: 6 March 2018 Revised Date:

29 May 2018

Accepted Date: 17 July 2018

Please cite this article as: Iqbal, O., Ahmad, M., Kadir, A.a., Effective Evaluation of Shale Gas reservoirs by Means of an Integrated Approach to Petrophysics and Geomechanics for the Optimization of Hydraulic Fracturing: A case Study of the Permian Roseneath and Murteree Shale Gas Reservoirs, Cooper Basin, Australia, Journal of Natural Gas Science & Engineering (2018), doi: 10.1016/ j.jngse.2018.07.017. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Effective Evaluation of Shale Gas reservoirs by Means of an Integrated Approach to Petrophysics and Geomechanics for the Optimization of Hydraulic Fracturing: A case Study of the

Basin, Australia. 1

Omer Iqbal, 2Maqsood Ahmad, 3Askury abd Kadir

1,2

Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Malaysia

Department of Petroleum Geoscience, Universiti Teknologi PETRONAS, Malaysia

*Corresponding author = [email protected]

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Abstract

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Permian Roseneath and Murteree Shale Gas Reservoirs, Cooper

Brittleness and in-situ stress states are known critical indicators for screening prospected layers during hydraulic fracturing in unconventional reservoirs. Brittleness can be inferred from mechanical parameters and mineralogical data, primarily using empirical relations, although an incomplete dataset limits their use. Therefore, a dataset with a systematic

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framework was designed based on well logs, and details core data spudded in the Permian Roseneath and Murteree shales from the Cooper Basin, Australia. Petrophysical and geomechanical models were designed to indicate shale mineralogy, total organic richness, porosity, in-situ stress conditions, brittleness index, pore pressure, and fracture pressure

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gradient. After a review of various definitions of brittleness index (BI) in recent literature, it will be argued that the definition of a brittleness index is with reference to either elastic

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parameters, mineralogical composition, or strength parameters. Consequently, a higher brittleness index is assigned to quartz and siderite rich rocks than to clay, organic matter, and porosity rich rocks. Some recent definitions of BI are therefore useful for indicating rock types, but brittle/ductile behavior is not necessarily any indicator of brittle/ductile failure during stimulation. It is therefore proposed that an accurate BI must be incorporated into a geomechanical model. This new model will comprise the following properties: elastic and strength parameters, in-situ stress state, fracture pressure gradient, and pore pressure. Such an integrated model can be used to find 1) Fracture barriers (the layers hindering fracture growth); 2) Potential layers that enhance fracture growth, and; 3) Direction of induced fractures on the bases of the stress regime.

ACCEPTED MANUSCRIPT Keywords; Petrophysical and mineral model, geomechanical model, brittleness index, hydraulic fracturing, shale gas, sweet spots. 1. Introduction Over the past decades, depletion in conventional reservoirs has led to unconventional

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reservoirs being focused upon for various aspects of exploitation. Unconventional reservoirs are characterized by ultra-low matrix permeability and are remarkably different to conventional reservoirs in terms of their geology, mechanical, and petrophysical properties (Dewhurst et al., 2015). The success of commercial production from these reservoirs depends upon both natural and artificial fractures (Nordeng, 2009). Moreover, field experience

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demonstrates that all fracturing targets cannot yield commercial production during hydraulic fracturing. Consequently, the identification of candidates for prospected fracturing is

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necessary before any stimulation treatment commences (King, 2010; Zhou et al., 2014). The layers with brittle characteristics are considered prospected for fracturing. Brittle layers are easier for fracturing than ductile layers in shale gas reservoirs because ductile rock tends to heal both natural and artificial fractures (Guo et al., 2013). Brittle and ductile layers can be located by knowing brittleness index of reservoirs, which is generally used in the industry as

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an indicator of perforation locations during the hydraulic fracturing process (Lai et al., 2015). High brittleness is attributed to brittle rock, which means that there is a high probability of there being a large stimulated reservoir volume for gas recovery during the fracturing (Sondergeld et al., 2010). It is of the utmost importance to understand the parameters directly

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affecting the development of unconventional reservoirs if there is to be better design of fracturing treatment (King, 2010; Boulis et al., 2013). Contrarily, field experience

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demonstrates that the selection of fracturing stages by dividing well bore equally into zones may lead to remarkably different production performances in the same formation. It has been observed that similar treatment efforts on the same formation may also yield remarkably different production performances due to the heterogeneity of reservoirs (Daniels et al., 2007). Heterogeneity of the reservoir must therefore be considered in terms of geomechanical and flow properties, to identify the prospected/sweet spots (Jahandideh & Jafarpour, 2016). Therefore, improvements in shale gas production are required through better understanding and characterization of reservoir properties in terms of their geomechanical and petrophysical attributes, specifically the distribution of rock brittleness and in-situ stress conditions are

ACCEPTED MANUSCRIPT particularly important factors here; they are, in fact, the main determinant for the selection of prospected layers in reservoirs. Brittleness/fracability are terms used to denote rock behavior; particularly to identify prospected fracturing candidates for stimulation in low-permeability reservoirs (Rickman et al., 2008). Many researchers have attempted to define brittleness, but there is no universal

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definition taken as a standard. Brittleness can be calculated using the following methods: stress-strain curves from multistage triaxial measurements; the ratio of elastic strain and total strain; the ratio of compressive strength to tensile strength; the angle of friction; Brinell hardness; and several empirical relations (Hucka and Das 1974; Altindag 2003; Heidari et al.,

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2014; Zhou et al., 2014). From a geomechanical perspective, brittleness is related to Young’s modulus and Poisson’s ratio. Young’s modulus is a ratio of stress to strain, while Poisson’s

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ratio is a ratio of transverse to axial strain, following Hook’s law. These elastic parameters measure rock’s ability to fail under stress (Poisson’s ratio) and maintain fracture (Young’s modulus). The combination of these two vital parameters is the main determinant of rock brittleness (Rickman et al., 2008). From a mineralogical perspective, there are different points of view in the literature. While Jarvie et al. (2007) concluded that quartz is the most brittle mineral; Wang et al., (2009) posited that quartz and dolomite are more brittle than organic

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matter and clay. Jin et al. (2014) argued that quartz and all carbonate minerals are brittle minerals, and Rybacki et al., 2016 deduced that quartz is brittle, while carbonate is partially a brittle mineral. Several studies indicated that brittle rock has a higher Young’s modulus and lower Poisson’s ratio because of the presence of a high amount of quartz and carbonate

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minerals. This brittle behavior tends to fracture rapidly, and induced fractures remain stable after treatment (King, 2010). On the contrary, ductile rock, with its high clay content, is less

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friendly to fracturing and to healing fractures after treatment. Ductile rock needs more energy/fracturing pressure to break, resulting in short fractures, which are undesirable and provide less contact with formation, in addition to needing more proppant. From a petrophysical point of view, rock brittleness is affected by porosity and organic richness (Wang et al., 2009). This refers to the concept that brittle rock failing under low strain and becoming more porous sees an increase in its strain rate. This increase deflects rock from brittle to ductile formations (Fjar et al., 2008). Moreover, a high TOC (total organic content) is associated with a higher clay content. Higher clay content makes rock ductile; meaning that it is less prone to fractures and less resistant to proppant embedment (Wang et al., 2009). Unfortunately, brittleness estimations from laboratories on core samples are not feasible due

ACCEPTED MANUSCRIPT to the problem of obtaining intact samples and due to expense. Brittleness estimation using geophysical wire-line logs to estimate elastic parameters and mineralogy are considered more applicable because obtaining information is easier using such methods than by conducting extensive laboratory tests on core. In spite of this, the laboratory tests carried out on core samples are considered a benchmark and have been used to calibrate geophysical wire-log

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data. Calibration is required because dynamic measurements from well logs are always greater than static measurements on core samples, because of differences in strain amplitude. Several researchers have in fact measured brittleness based on elastic properties, petrophysical properties, and strength properties (Grieser et al., 2007; Jarvie et al., 2007;

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Rickman et al., 2008; Wang et al., 2009; Herwanger et al., 2015). These attempts enjoyed a certain degree of success, but are now known to have some limitations. For recently, several researchers attempted to make a relationship between brittleness and well logs by means of a

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data-driven approach that could be universally applicable. For instance, Jin et al. (2014a, 2014b) derived a relationship between brittleness and porosity. Lai et al. 2015 developed a brittleness model using the ratio of gamma ray to photoelectric effect (GR/Pe), concluding that brittleness can be derived using well logs because Pe log can be used to identify brittle quartz mineral. Shi et al. 2017 estimated brittleness using an artificial neural network. Li et

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al. 2017 estimated brittleness based on a statistical damage model by evaluating damage during the loading process. The limitations of such correlations and novel methods were due to their success only for typical shale formations, not for other typical shale formations, where the applicability of these methods needed more verification. In addition, the lack of

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availability of complete data for each well further restricted the use of these methods. Furthermore, it is required to use a proper definition of brittleness index, specifically

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brittleness based on mineralogy, where quartz and carbonate must be treated as a brittle mineral and the effect of porosity and organic matter on brittleness index must be considered to select prospected layers for fracturing. There are certain examples known to assert the importance of brittleness for the optimization of hydraulic fracturing treatment. One example is from the BP CGU 13-17H horizontal well located in Carthage field, Panola County, Texas. In this case, there was undesirable production outcome when selecting fracturing candidates (ductile areas) based on equally spaced intervals (every 110ft) by assuming homogeneous properties of the reservoir (Buller et al., 2010). Another example is from Longmaxi black shale in South China, where fracturing candidates were selected based on the brittleness of the reservoir. In this case there was extensive initiation and propagation of fractures observed in brittle areas, with higher production yielded through fracture tracing and micro-seismic

ACCEPTED MANUSCRIPT monitoring (Li et al., 2013). In recent years, the significance of brittleness on fracturing efficiency has been recognized and suggested to select fracturing stages (Li et al., 2013). However, brittleness alone, nevertheless, is not sufficient by itself to select prospected layers, magnitude, and the direction of in-situ stresses, which are also required to locate fracture barriers and prospected/potential brittle layers (Jacobi et al., 2009). Minimum horizontal

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stress is very important in designing fracturing treatments (Willis, et al., 2005; Wrightet al., 1999) because induced fractures are not only propagated perpendicular to the minimum horizontal stress, but also because this stress attempts to close the fracture itself (Hubbert et al., 1957). Meanwhile, there are three types of stress regimes; normal faulting, strike-slip

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faulting, and the reverse faulting stress regime which affect the direction and propagation of fractures (Anderson 1957). As brittleness and in-situ earth stresses are the most important parameters during fracturing process, therefore they must be estimated accurately for better

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understanding of reservoir potential for fracturing.

There are two main challenges encountered during evaluation of shale gas reservoirs. First, estimation of reservoir properties with limited data at reservoir temperature and pressure conditions, because already proposed correlations are not applicable for all shale gas reservoirs due to difference in mineralogy, lithology and environment of deposition of

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reservoir. Secondly, the estimation of consistent volume of reservoir properties from to bottom of reservoir to get complete image of reservoir for better completion and production of reservoir. For this purpose, the systematic workflow has been established to estimate the reservoir properties at reservoir temperature and pressure conditions using geophysical wire-

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line logs and core data for Roseneath and Murteree shale formations, Cooper Basin, Australia. The practical application of reservoir properties to recommend the design of

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hydraulic fracturing through the identification of fracture barriers capable of stopping the propagation of fractures and prospected/potential layers that are brittle enough to initiate and propagate fractures. The motivation behind this work, then, is to include rock brittleness and in-situ earth stresses, because with its effect on pressures required to break rock, brittleness is a key factor in any design consideration. 1.1

Geology of the Cooper Basin

The area chosen for this study lies in the Cooper Basin (Figure 1), South Australia. The Cooper Basin is regarded as the most prospective basin in Australia for shale gas reservoirs. The area is prolific with onshore oil and gas. Natural gas (with small liquids) has been

ACCEPTED MANUSCRIPT produced there since 1963 (Hall et al., 2016). The basin has potential for both conventional and unconventional reservoirs. The most prolific intervals with hydrocarbons are located within the Late Paleozoic Gidgeapla Group (Figure 1). After successful recovery of natural gas from Moomba 191 in the form of shale gas reservoirs by the Santos company, exploration from these reservoirs has been expanded and various

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other companies started production in Cooper basin; Beach energy, Senex, Drill search, and Strike Energy with other joint ventures (Jadoon et al., 2016). This expansive exploration and production in the basin evinces the presence of a significant infrastructure. Among other troughs, most prospected troughs prolific with unconventional oil and gas are the

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Nappamerri, Patchawara and Tenappera troughs (PIRSA, 2007). Earlier exploration was focused on conventional clastic and carbonate reservoirs, with investigations based on

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conventional logs. These logs involved gamma-rays, resistivity, neutron-density, and sonic logs with limited core analyses (Valle, 2013). However, formation evaluation of shale gas reservoir requires geophysical wire-line logs with core analyses (Jadoon et al., 2016). Fortunately, the complete suite of wire-line logs with core data was made available for both the Roseneath and Murteree formations, provided by the DSD (Department of State Development).

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The Roseneath and Murteree shale, both proven shale gas reservoirs, were evaluated through the integration of well logs and core data represented in Table 1. The following parameters obtained through core-log integration were matched with each other to achieve a consistent

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volume of properties from top to bottom of intervals. The petrophysical properties included total porosity from density logs, total organic matter from pyrolysis and logs, mineralogy from powder X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy

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(FESM), thin section analysis and well logs to make mineral model. Geomechanical properties includes elastic, strength and failure parameters, and in-situ earth stresses. Brittleness index and fracturing pressures were measured through the calibration of core-logs and are presented in this study.

Properties Measured

Core data

Log data

ACCEPTED MANUSCRIPT Porosity

Crushed rock He porosimetry

Density ( mostly)

TOC (total organic

Rock Evaluation

GR,

carbon)

resistivity,

The aims of sonic

(Passey

approach) XRD, FESEM, Thin section

Density, sonic, neutron, PE, GR

Elastic properties

UCS

Sonic and density

Failure Parameters

Triaxial Compression tests,

Correlation with porosity (Jin et al.

Mohr circle

2015)

LOT, well completion reports

Density, sonic

fracturing pressure,

measuremen t

of petrophysic

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stress,

study

are: 1) the

Mineral contents

In-situ

this

al

and

geomechani

Brittleness Index

cal

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properties through core-log integration; 2) the exploration of the relationships between brittleness index and other petrophysical and geomechanical properties; 3) the identification of fracture barriers and prospected layers for hydraulic fracturing and validation through core

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data; 4) the development of simplistic workflow with correlations capable of being used with an absence of core data, particularly in the cases of the Roseneath and Murteree formations.

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Table. 1: Indicating use of well logs and core data (modified after Jadoon et al., 2016)

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ACCEPTED MANUSCRIPT Figure.1: Geological summary of Cooper Basin (PIRSA, 2007). Arrows indicated the studied area.

2. Methodology This is an integrated study of geophysical wire-line logs and drill core data. The following data were available for current study, including wire-reline logs (sonic (DT), density (Rhob),

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Photoelectric effect (PEF), neutron (NPHI), resistivity (MSFL, LLS, and LLD), gamma ray (GR), and core samples collected from five wells in the Nappameri troughs. The core samples and wire-line log data were provided by the DSD. The wire-line log data (Log ASCII Standard-Las files) was loaded into IP 4.3 (Interactive petrophysics) Synergy software and

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then integrated with core and cutting data to develop mineral modeling for the quantification of mineral content for both the Roseneath and Murteree shales in exploration wells. The

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methodology was designed through an integration of petrophysical and geomechanical properties to locate fracture barriers, and optimal/brittle zones that promote gas recovery. The workflow adopted was divided into two main phases; first the quantification of petrophysical properties and second the quantification of geomechanical properties (Figure 2). There are the steps involved in the selection of fracture barriers and potential layers for hydraulic

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fracturing in the Roseneath and Murteree shale gas reservoirs.

1. Quantification of total porosity (фT), total organic matter in wt % (TOC), and mineralogy using geophysical wire-line logs and core data. The mineral contents are to be quantified by XRD (X-ray diffraction) on core samples and visualized on

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FESEM images. The continuous volume of mineral content from top to bottom is to be obtained using the application called “Mineral Solver”, part of the package “Interactive Petrophysics Software” through the integration of geophysical wire-line

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logs and XRD data.

2. For the purpose of calculation, elastic parameters are divided into two halves; one being dynamic and the other static. Dynamic elastic properties like Young’s’ modulus and Poisson’s ratio are to be calculated using sonic and density logs. Shear slowness is to be calculated using measured slowness on core samples. Whereas, static parameters measured from unconfined compressive strength tests on core samples. The calibration carried out between static and dynamic and calibrated elastic parameters were used for further analysis. 3. Brittleness index is divided into two: one is from elastic parameters named BI_1, while the other is from mineralogy, named BI_2, BI_3, BI_4, BI_6, and BI_7 came

ACCEPTED MANUSCRIPT from combination of BI_4 and BI_6. A total brittleness index (BI_T) is derived from an average of BI_1 and BI_7. 4. The strength and failure parameters to be included are: unconfined compressive strength; tensile strength; cohesion and friction angle using empirical relations developed from a correlation of measured cohesion; and friction angle on core

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samples, with porosity from the density log. 5. In-situ stress profiles are to be considered, such as vertical/overburden stress and maximum/minimum horizontal stress. The identification of a stress regime is to be based on stress profiles.

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6. Fracturing pressure is to include; the reopening of open fractures (Popen), the reopening of closed fractures (Pclose), and newly induced fractures called breakdown

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pressure (Pb).

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The selection of fracture barriers and potential zones is to be based on petrophysical and geomechanical properties: specifically, brittleness index, and minimum horizontal stress,

Figure 2: Workflow adopted for 1-D integrated model for selection of fracture barriers and prospected/ brittle layers in Roseneath and Murteree formations, Cooper Basin, Australia.

2.1

Quantification of total porosity and total organic matter (TOC)

ACCEPTED MANUSCRIPT Porosity is to be computed using a density log. As in the case of organic rich shale, TOC has been overlooked from porosity on density logs (Cluff, 2012). A kerogen correction, therefore, needs to be applied to quantify porosity from a density log. Porosity was previously quantified following the convention of weight percent of mineral to volume percent density

φD =

(ρ ma − ρb )

( ρ ma − ρ f )

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log. The following equations are used for porosity (Krygowsk, 2003). ………………………………………………………………….Equation (1)

Where

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фD= Density porosity

ρb= Bulk density ρf= Fluid density Porosity correction for kerogen

φDKC = φD − (VK × φDK )

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Where,

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ρma=Matrix density

фDKC= Density porosity corrected for kerogen content

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фDK= Density porosity of kerogen

VK= Volume of kerogen in fraction.

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Total organic content (TOC) is defined as the organic richness or amount of organics within rock, and is recorded as a percentage (Jarvie et al., 2007). As total organic contents define organic richness within rock and can be accurately measured on core samples using rock pyrolysis, it can also be estimated using well logs. For this study, ∆logR methodology (Passey et al.,1990; Ahmad et al., 2017) is adopted to determine total organic carbon (TOC) and then compared with TOC based on borehole cuttings.

∆ log Rsonic = log10(

R Rbaseline

) + 0.02(∆t − ∆tbasline ) …………………………………Equation (2)

TOC= (∆ logR) *10(2.297−0.1688*LOM …………………………………………………...Equation (3)

ACCEPTED MANUSCRIPT Where, TOC represents total organic content, R and ∆t are resistivity log and travel time from sonic log, Rbaseline and ∆baseline are normal resistivity and travel time at overlay of resistivity and sonic log represent non-source rock interval. LOM is level of organic maturity which can be measured from vitrinite reflectance. ∆LogR is the separation between resistivity and sonic log represent organic rich interval.

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The potential drawback of this approach is an assumption that other rock constituents cannot influence logs used. For instance, the presence of pyrite in a significant amount can mask resistivity profile and show low resistivity in organic-rich rocks, giving the possibility of erroneous results for organic richness (Passey et al.,1990). For the Roseneath and Murteree

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formations there is the less-no volume of pyrite, therefore, Passey's approach can give meaningful results for a volume of TOC (Jadoon et al., 2016). Since for studied formations

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there is a no/minimum volume of pyrite indicated in XRD data and in mineral models, Passey's approach can be used to obtain TOC volumes for both the Roseneath and Murteree Formations. 2.2

Mineral modeling

A petrophysical and mineral model is necessary for the evaluation of shale gas reservoirs. A

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consistent volume of each mineral, porosity, organic richness, and other properties is required to evaluate shale gas reservoirs. This consistency can be achieved by the integration of XRD, wire-line logs and geochemical data (Bust et al., 2011, Jadoon et al., 2016). The mineral model can be developed using geophysical wire-line logs and XRD using the application

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Mineral solver in the package “Interactive Petrophysics Software”. There are certain steps involved in performing multiple mineral modeling of shale gas reservoirs. Firstly, re-

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normalizing the XRD and TOC weight percentage so that minerals and organic matter can be summed to 100%. After this, there is a conversion of weight percentage to volume percentage using the equation below;

Wet volume percent = (Dry Weight %) * (1- Porosity) * (Rock Grain Density)/ (Mineral Grain Density) Rock grain density and porosity are used from the routine core analysis. Porosity plays an important role in conversion, for core porosity is used to mitigate porosity issues related to the kerogen effect. This conversion is executed using Mineral Solver’s processing utility, in the software package. After several iterations, the exact mineral endpoints are obtained,

ACCEPTED MANUSCRIPT allowing for the correlation of the log-based mineral volumes with the XRD based mineral volumes. It is anticipated that a number of key assumptions, conditions, and potential issues will be encountered in the process of developing these models. For example, shale exhibiting a high GR value, and an elevated GR value in reducing environment, is a phenomenon caused by high uranium (ppm) content, which makes it hard to identify clays using GR

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logging. The second issue in the construction of the multiple mineral models for this study is the calculation of clay volume (VCL), for this depends upon the integrated response of input curves. For the number of input curves greatly affects the quality of the model produced (Jadoon et al., 2016). Mineral identification using only wire-line logging methods is

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ineffective, due to the presence of clay, kerogen and small grain. The mineral model may be helpful in evaluating challenging shale gas reservoirs because results from the models may be fine-tuned by adjusting input parameters to obtain a match between a core and log data

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(Jadoon et al., 2016). The differences between the mineral model definitions (mineral endpoint) and assumptions pertaining to tool physics of lugs used may give rise to a difference in interpretations (Ramirez et al., 2011). Each mineral has its own mineral endpoint for specific logs used as input data (e.g., 3.88 is an endpoint of siderite for density log). This mineral model may be used as an input in the Mineral Solver application of

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Interactive Petrophysics (IP), or can be used in other comparable packages like Elan and Satmin (Jadoon et al., 2016). It should be noted that the shale units in most studies consist of quartz, feldspar, carbonates, titanium-oxides, and clay minerals. Therefore, different models were constructed for each well separately in order to obtain the amount of each mineral

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constituents accurately. IP actually combines different models into one mineral model containing all minerals.

Determination of rock elastic parameters.

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2.3 2.3.1

Calculation of shear slowness

Shear slowness can be calculated using different petrophysical correlations (Fjar et al. 2008; Iqbal et al., 2017). In the absence of shear waves in all wells, shear slowness is estimated using correlations between static compressional and shear velocity measured on core samples as shown in Figure 3., as S- waves slowness is usually absent from conventional logging data. y = 1.0979x - 2.2577 R² = 0.9728

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Figure 3: Relationship between compressional velocity (Vp in km/sec) and shear velocity (Vs in km/sec) based on core samples. Note; the equation was used to estimate Vs from top to bottom of interval using Vp from each well.

Calculation of Dynamic elastic parameters

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2.3.2

Dynamic elastic parameters include Young’s modulus and Poisson’s ratio calculated using sonic and density logs. Dynamic Young’s modulus (YM_DYN. in GPa) and Poisson’s ratio

V p − 2Vs 2

PR _ DYN.(V ) =

2

2(V p − Vs ) 2

2

V p − 2Vs 2

………………………………………………….….Equation (4)

2

2(V p − Vs ) 2

2

…………………………………………………....Equation (5)

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YM _ DYN.( E ) =

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(PR_DYN.) are obtained using following equations (Fjaer et al., 2008).

Where Vp represents compressional velocity in km/sec, Vs is shear wave velocity in km/sec, and ρb is bulk density in gm/cc.

Conversion from Dynamic to static elastic parameters

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2.3.3

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Dynamic measurements from well logs are always different from static parameters from core samples. This difference is attributed to strain amplitude, frequencies, system type, and uncertainties in measurements (Fjar et al. 2008). Therefore, calibration is necessary between static and dynamic properties (Rickman et al. 2008). Figure 4 illustrates the experimental relation between static and dynamic parameters. The equations obtained from calibration were used to estimate calibrated Young’s modulus (YM_Cali.) and Poisson’s ratio (PR_Cali.).

y = -0.7281x + 41.969 R² = 0.8936

y = -4.7894x + 1.4769 R² = 0.9804

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2.4

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Figure 4: Calibration between dynamic-static elastic parameters, the relations indicated the that static parameter is far less than dynamic parameter that could be due to difference in strain amplitude. YM_core (static young’s modulus), YM_DYN (Dynamic young’s modulus), PR_core (static Poisson’s ratio), PR_DYN (Dynamic Poisson’s ratio). Note; the relations were used to find calibrated elastic parameters.

Review on methods for the calculation of brittleness index

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In previous decades, brittleness became a vitally important factor for hydraulic fracturing of unconventional reservoirs, but there is no precise concept, nor are there any methods for the calculation of brittleness (Altindag, 2003). Due to the lack of any standard definition and methods, the term brittleness has consequently been used differently for different practical uses (Altindag, 2003). The most acceptable definition proposed by Hucka et al.,1974 was that

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fractures in brittle rock are initiated at, or slightly beyond, yield stress. The equations used to calculate brittleness are empirical and several concepts exist to calculate brittleness (Jin et al., 2014a). Brittleness is generally calculated in two ways; one is by means of a physical measurement of rock properties. The other is to measure the energy consumption for

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cutting/drilling a rock (Goktan et al., 2005).

Brittleness is now calculated by what is known as the brittleness index (Rickman et al., 2008;

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Sondergeld et al., 2010). The concept of brittleness used in this study is from the point of view of petroleum reservoir rock drilling, where the aim is to evaluate the efficiency of reservoirs to make complex fractures during hydraulic fracturing. The most acceptable method for industries to calculate brittleness index is through an examination of both rock mineralogical composition and mechanical parameters (Rickman et al., 2008; Sondergeld et al., 2010). The mechanical parameters include Young’s modulus and Poisson’s ratio. A higher Young’s modulus coupled with a lower Poisson’s ratio corresponds to a more brittle rock, which is easy to break and is susceptible to complex fractures (Wang et al., 2009). In contrast, rock with a high Poisson’s ratio and a lower Young’s modulus is more ductile and less susceptible to complex fractures. Brittle minerals play a key role in stimulation,

ACCEPTED MANUSCRIPT increasing the interface between the reservoir and the well bore (Jarvie et al.,2007). However, no standard criteria exist for brittle minerals (Guo et al., 2013). Usually, quartz, feldspar, and carbonate minerals are treated as brittle minerals, with quartz and carbonate being considered more brittle than feldspar (Jin et al., 2014a; Wang et al., 2009). Some researchers, nevertheless, point out that quartz is more brittle than carbonate, so carbonate should be

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treated as only partially brittle (Rybacki et al., 2016). In fact, previous studies showed that quartz and carbonate are critical and favorable for both natural and induced fractures during hydraulic fracturing (Wang et al., 2009, Jin et al 2015). The higher magnitude of brittleness index makes rock brittle and suitable for fracturing (Goktan et al., 2005).

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There are several methods for calculating brittleness index (Guo et al., 2013). In industry, two approaches exist (Wang et al., 2009). One method is to calculate brittleness index is by using mechanical parameters, including Young’s modulus and Poisson’s ratio (Guo et al., 2013).

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100(YM.Cali. − YM _ Calimin ) 100( PR _ Cali − PR _ Calimax ) + (YM _ Calimax − YM _ Calimin ) ( PR _ Calimin − PR _ Calimax ) BI _ 1 = ………..Equation (6) 2 Where YM_Cali is calibrated Young’s modulus (GPa) and PR-Cali is calibrated Poisson’s ratio, YM_cali.min and YM_Cali.max represent the minimum and maximum

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Young’s modulus. Similarly, PR_Cali.min and PR_Cali.max are alternatively the minimum and maximum Poisson’s ratio (Guo et al., 2013). Normalization of these two elastic parameters are necessary due to variation in the units of Young’s modulus (GPa) and Poisson’s ratio

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(dimensionless).

The other method of calculation of brittleness index involves an examination of mineral

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contents. The following equations are commonly used to find brittleness index (Jarvie et al., 2007; Wang et al., 2009; Jin et al., 2014a, 2014b; Alzahabi et al 2015; Rybacki et al., 2016).

BI _ 2 =

Q 100 ……………………………………………………..Equation (7) Q + Car + Clay

BI _ 3 =

Q + Dol 100 ………………………………………Equation (8) Q + Dol + Cal + TOC + Clay

BI _ 4 =

Q + Car 100 ………………………………………………..…..Equation (9) Q + Car + Clay

ACCEPTED MANUSCRIPT BI _ 5 = 1.09

Q + HM 1 × ……………………………………....Equation (10) Q + HM + Car + Clay 8.8

Wsb Fsb ……………………………………….Equation (11) wsb Fsb + wCb FCb + wwd Fwd + wφφ

BI _ 7 =

W sb Fsb ……………………………....……. Equation (12) w sb Fsb + wCb FCb + w wd Fwd + wφ φ

BI _ T =

BI _ 1 + BI _ 7 ……………………………………………………….Equation (13) 2

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BI _ 6 =

where Q = quartz, Car = carbonates, Dol = dolomite, TOC = Total organic content, Wsb =

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weighing factor (0-1) for strong brittle minerals, Wwd = weighing factor (0-1) for weak ductile minerals, WCb= weighing factor (0-1) for carbonate minerals, Fsb = fraction of strong minerals like quartz, feldspar and Pyrite, Fwd = fraction of weak/ductile minerals like clays and TOC, FCb = fraction of carbonates, Wsb = Wwd =Wθ= 1 and WCb=0.5 in case of BI6, while it is WCb= 1 in BI_7. The rock with BI > 0.4 (40%) is considered brittle and suitable for fracturing (Guo

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et al., 2013). After a comparative study of brittleness, the total brittleness index is calculated by taking the average of BI_1 and BI_7. The total brittleness index (BI_T) can be used to classify the Roseneath and Murteree formations into mechanical behavior like Brittle (>0.48), less brittle (0.32-0.48), less ductile (0.16-0.32), and ductile (0-0.16) as proposed by Perez et

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al., 2013.

Calculation of compressive strength and tensile strength

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Compressive strength and tensile strength increase with rock density and decrease with rock porosity under formation conditions. Compressive and tensile strength are also closely related to mineral composition and porosity. Rocks are less resistant to tension than compression: in fact, tensile strength is about 8-10% of compressive strength. The following equations are commonly used, as expressed below (Zoback et al., 2003).

UCS =

T =

2C ……………………………………………….(Equation 14) (cosθ − (1− Sinθ ) tanθ )

UCS ……………………………………………………………………….(Equation 15) 10

ACCEPTED MANUSCRIPT The mechanical strength of rocks directly from the core is highly sensitive due to certain individual characteristics of experiments and sample conditions (Fjar et al., 2008), differences reflected in the different descriptors of mechanical strength found in the literature (Zoback, 2003). Generally, compressive strength is inversely proportional to porosity and directly proportional to confining pressure and Young's modulus. From the literature, it has been clear y = -156.18x + 22.645

y = -279x + 34.223 that failure mechanism (cohesion R² = and 0.72friction angle) is inversely proportional to porosity and

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R² = 0.6917

can be correlated to obtain a continued profile of failure parameters (Jin et al., 2014a). Therefore, the cohesion and friction angles estimated on core samples by triaxial compression tests from well completion reports were found to correlate with porosity in the literature

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strength and failure properties for all wells.

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(Figure 5). Then empirical relations were used to obtain a continuous profile of mechanical

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Figure. 5: Correlation between failure parameters and porosity with general concept that cohesion and failure parameter decreased with an increase in porosity. Note; the failure parameters measured on core samples from triaxial compression tests and porosity from density log. Obtained relation used to find consistent volume of failure parameters.

2.6

In-situ Earth Stresses

In-situ stress plays a vital role in different aspects of hydraulic fracturing treatment. A hydraulic fracture simulator requires in-situ stress profile as an input (Mohaghegh et al., 2004). The absence of in-situ stress during the design of hydraulic fracture can give 50% error in fracture length (Vonieff et al., 1992). There are two methods used to calculate in-situ stress. The first method is physical measurement and the second is by using geophysical wire-line logs (Mohaghegh et al., 2004). In this study, wire-line logs will be used to reveal the in-situ stress profile.

ACCEPTED MANUSCRIPT There are three main stresses, namely; vertical, maximum, and minimum horizontal stresses. Vertical stress can be calculated through the integration of rock density from the top to the bottom of depth (Zoback et al., 2003). z

−

σ v = ∫ ρ (Z ) gdz ≅ ρ gz ………………………………………………………..(Equation 16)

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0

Where ρ(Z) represents rock density and function of depth, g represents a gravity constant. In this study, horizontal stress will be calculated using poroelastic theory. In tectonically active basins, tectonic plate movement creates tectonic stress and strain. This strain adds stress components to elastic rock. This poroelastic horizontal strain takes tectonic strain into

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account. Horizontal stresses can be calculated from overburden stress, Poisson’s ratio, Biot’s

formulation (Zoback et al., 2003).

σh = σH =

ν 1 −ν

ν 1 −ν

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parameter, and pore pressure. These stresses can be calculated by using the following

(σ v − αPp ) + αPp +

Eνε ………………………………………..…..(Equation 17) 1 −ν 2

(σ v − αPp ) + α Pp +

Eε ………………………………………..…..(Equation 18) 1 −ν 2

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Where ρ(z) is a bulk density of rock and function of depth. Vertical stress is calculated by integrating all available density logs, σh = minimum horizontal stress, σH = maximum horizontal stress, v = Poisson's ratio, α= alpha, Pp= pore pressure, E= Young's modulus, σv = vertical tress and "Epsilon" Ɛ =strain factor. Pore pressure is calculated using Eaton's

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approach as follows (Eaton, 1972): the Eaton’s approach involves picking a shale point using gamma ray logging and then calculating the normal compaction trend line (∆Tn) .

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 ∆T n P = σ v − (σ v − Phyd )  ∆T  log

3

  ……………………………………………………(Equation 19)  

Where P = Pore pressure, σv = Overburden, ∆Tn = Normal trend of Sonic transit time (µs/ft), ∆Tlog = Sonic log observed values, Phyd = Normal hydrostatic pressure. 2.7

Hydraulic fracturing in the Roseneath and Murteree formations

Induced hydraulic fractures in stimulation treatments are created by pumping fracturing fluid into perforation intervals at pressure, enough to cause ‘tensile failure’ of the rock. The pressure required to break the rock is known as breakdown pressure. The formation splits due to induced tensile stress created by high-pressure fracturing fluid, and the fracture propagates

ACCEPTED MANUSCRIPT on further increase in pressure. Usually, sand is used as a propping material to keep the induced fractures open after pumping is done. Generally, there are three types of processes involved during hydraulic fracturing stimulation; reactivation of open natural fractures that exist in formation (Popen), re-opening of closed natural fractures (either uncemented, partially cemented, or totally cemented fractures) (Pclose)

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and newly-induced fractures by the breakdown of intact rock (Pb) (Jaocbi et al 2009). Therefore, the following pressures were calculated for the Roseneath and Murteree Formations;

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Popen = σ h,min ……………………………………..…………………………….…(Equation 20)

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PClose = 3σ h,min −σ H ,max − Pp ……………..…………………………………….…(Equation 21)

Pb = 3σ h,min − σ H ,max − Pp + To ……..…………………………………….……...(Equation 22)

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3. Results

Five wells located in the same field were used as a case study. The Roseneath and Murteree formations in terms of lithology contain mainly shale with minor siltstone beds. In terms of mineralogy, both formations contain mainly quartz, carbonate (siderite, minor calcite), clay

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(illite, kaolinite, minor chlorite), and heavy minerals (minor pyrite, anatase, rutile). The methods described above were used to calculate petrophysical properties, including lithology,

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mineralogy, total organic richness, and total porosity. The other geomechanical properties include elasticity, strength and failure parameters, along with an in-situ stress profile, a brittleness index, and fracturing pressure. The mineralogy, porosity, and organic richness varies for all wells. The integrated model was developed in order to find the fracture barriers and optimal/potential layers for fracturing. The effect of petrophysical properties on geomechanical properties was examined. The average petrophysical and geomechanical properties for the Roseneath and Murteree formations are presented in Table 2 and Table 3. 3.1

Petrophysical characterization

ACCEPTED MANUSCRIPT The petrophysical properties for both formations varies in all wells. The average properties measured for each well are shown in Table 2 and Table 3. The TOC content of Roseneath shale ranges from 1.38-3.84 wt%, whereas the same content of Murteree shale ranges from 1.32-2.8 wt%. The average total porosity of Roseneath shale ranges from 0.03-0.12 (fraction), whereas the total porosity of Murteree shale ranges from 0.02-0.17 (fraction). The mineral

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composition of the Roseneath and Murteree shales is dominated by quartz, clay (illite, kaolinite minor chlorite), carbonates (siderite, minor limestone/dolomite), ti-oxies, and minor

Avg ФT Avg ФT (fraction) (fraction) 0.07

Moonta

0.03

Holdfast 1

0.08

KINGSTON

0.06

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RULE 1 SASONOF 1

0.12

Avg Clay Avg Clay (fraction) (fraction)

Table.2: Key

Avg Quartz Avg Avg Quartz Avg Carbonates (fraction) Carbonates (fraction) (fraction) (fraction)

1.38

0.55

0.36

0.06

3.85

0.54

0.34

0.04

1.73

0.49

0.38

0.07

2.54

0.59

0.30

0.06

2.80

0.46

0.44

0.07

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Avg TOC Avg TOC (wt%) (wt%)

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Well name Well name

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pyrite. The mineral content is also visualized in FESEM images as shown in Figure 6.

information for the petrophysical properties of the Roseneath formation

Table.3: Key information for the petrophysical properties of the Murteree formation

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1.32

0.42

0.53

0.03

Moonta

0.02

2.8

0.60

0.26

0.06

Holdfast 1

0.05

1.85

0.61

0.29

0.08

KINGSTON

0.08

2.31

0.59

0.28

0.09

0.17

2.68

0.48

0.44

0.04

RULE 1 SASONOF

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1

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Encounter 1

a)

Figure. 6: FESEM image showing mineral contents, illite, siderite, quartz and kaolinite with kerogen for a) Roseneath b) Murteree formation, Encounter 1 well.

3.2

Comparative analyses of brittleness

b)

ACCEPTED MANUSCRIPT The identification of layers/intervals where rock is brittle enough is of great importance in creating pathways for gas recovery. Brittleness index is one of key factors for identifying and selecting fracturing intervals and spots. Brittleness index from elastic parameters (BI_1) is, in fact, comparable with other models of brittleness index from mineralogy. The figures 8 , 9 and 10 show that BI_1 and BI_7 are closely related to each other, with the examples of quartz

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and carbonates being brittle and possessing a porosity affecting rock brittleness. The total brittleness index (BI_T), which is actually an average of BI_1 and BI_7, is also used for further analyses. A reservoir is classified into brittle, less brittle, less ductile, and ductile zones, based on BI_T, as shown in Figure 7. The cut-off value for the brittleness index can be

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set by means of an examination of the relationship between Young’s modulus and Poisson’s ratio (Grieser & Bray, 2007). The cross plot between calibrated Young’s modulus and

brittle behavior.

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Poisson’s ratio indicates that a high Young’s modulus and a powerful Poisson’s ratio show

Figure..7: ) a) Classification of rock into brittle , less brittle, less ductile and ductile based on brittleness index in z-axis, Young’s modulus on Y-axis and Poisson’s ratio on x-axis. . b) Same classification of rock by Perez & Marfurt (2013). Note; the brittle rock have high brittleness index, Young’s modulus and lower poisson’s ratio.

R2= 0.356

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Figure..8: ) Comparison of brittleness index based on elastic parameters and mineral contents. Note there is good correlation exist between BI_1 and BI_7.

Calibrated Young’s modulus (Gpa)

Calibrated Poisson’s ratio

Mineral Brittleness Index (BI_2- BI_7)

Elastic Brittleness Index (BI_1)

BI_2 Figure.9: Figure.2: Comparison Plots between of Brittleness brittlenessindex indexfrom frommineralogy mineralogyand andelastic elasticparameters parameters. and Note their that variation good correlations accordinglyexist withbetween elastic parameters. BI_1 Note; BI_7 andand BI_7, BI_1 which are consistent indicate the with pre-brittle elastic parameters nature of quartz for Roseneath and siderite, Formation, with a negative Encounter influence 1 well. of porosity.

BI_3

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Calibrated Young’s modulus (Gpa)

Calibrated Poisson’s ratio

Mineral Brittleness Index (BI_2- BI_7)

Elastic Brittleness Index (BI_1)

BI_2 BI_3 BI_4 Upper Murteree

BI_5 BI_6

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Figure.10: Comparison of Brittleness index from mineralogy and elastic parameters and their variation accordingly with elastic parameters. Note; BI_7 and BI_1 showed good correlation with elastic parameters for Murteree Formation, Encounter 1 well.

The relationship between brittleness index (BI_T), TOC, porosity.

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3.3

Brittleness index is a complex function of lithology, composition, effective stress, diagenesis, temperature, porosity, TOC, and geomechanical properties (Atingdag, 2003; Rickman et al., 2008; Wang et al., 2009). The cross plots are developed between brittleness index (BI_T)

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from mineralogy and elastic parameters with organic richness and porosity (Figures 11,12,13 and 14). It clearly indicates that TOC >1 wt % falls in brittle regions, TOC > 3 wt% falls in

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less brittle regions and TOC > 4 wt% falls in less ductile regions for the Roseneath and Murteree shales in the Encounter 1 well. Similar clusters are observed for other wells. Likewise, there is a фT > 0.09 fall in brittle region, фT > 0.18 fall in less brittle region and, фT > 0.27 fall in less ductile region for the Roseneath shale, whereas there is a фT > 0.09 fall in brittle region, фT > 0.27 fall in less brittle region, and фT > 0.36 fall in the Murteree shale, in the Encounter 1 well. These relationships also indicate that porosity, TOC and BI from core samples have a negative relation to each other. The relationships, in fact, indicate that brittle minerals like quartz and carbonate from XRD increase, with an increase in BI_T, whereas decrease with an increase in clay contents (Figure 15). The cross plots indicate that significant amounts of TOC, ductile clay, brittle minerals, and porosity influence rock

ACCEPTED MANUSCRIPT brittleness. The concentration of TOC contents can mark brittle, less brittle, and less ductile zones. As BI_T is from both mineral contents and elastic parameters, so the concentration of both TOC and porosity does effect elastic parameters, including Young’s modulus and Poisson’s ratio. Based on this, the Roseneath and Murteree shale fall in less brittle zones, being the maximum point fall in this whole region. Where there is evidence of TOC >1% fall,

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there is a significant decrease in BI, occurring alongside an increase in TOC and porosity. These cross plots are reliable and consistent with previous findings; i.e. TOC and porosity increasing alongside a decrease in brittleness index. These outcomes are consistent with

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previous studies, including Wang et al. 2009; Haeidri et al. (2014), and Liu et al. (2016).

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Figure.11: Cross plot between total organic content (TOC) and brittleness Total (B-Total) for the Roseneath and Murteree Formations from Encounter 1. Note; there is a decreasing trend of BI_T with an increase in TOC content.

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Figure..12: Cross plot between total organic content (TOC) and brittleness total (B-Total) based on XRD and Rock pyrolysis for the Roseneath and Murteree formations from Encounter 1. Note; BI decrease with an increase in TOC contents.

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Figure.13: Cross plot between total porosity) and brittleness total (B-Total) for the Roseneath and Murteree formations from Encounter 1 well. Note; there is a decreasing trend of BI_T with an increase in total porosity.

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Figure..14: Cross plot between brittleness Index from XRD and elastic parameters) and porosity from core samples for the Roseneath and Murteree formations, Encounter 1 well. Note: BI_T decrease with an increase in porosity.

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Figure.15: Cross plot between brittleness Index (BI-1) based on geomechanical properties and amount of a) quartz and siderite b) clays (illite and Kaolinite) from XRD for the Roseneath and Murteree formations from Encounter 1 well. BI_T increase with an increase in quartz and siderite, whereas decrease with an increase in clay contents.

3.4

The relationship between pore pressure and brittleness index (BI_T)

ACCEPTED MANUSCRIPT In addition to TOC, porosity, and brittle minerals, pore pressure could be another parameter affecting fracturing treatment (Zhang et al., 2016). Pore pressure does affect the height and vertical growth of fractures in shale reservoirs. In order to see its effect on the brittleness index, their relationship is plotted in Figure 16. This Figure indicates that the brittleness index does not significantly contribute to pore pressure in the Roseneath and Murteree shale, except

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for some layers where pressure decreases alongside an increase in BI_T. This decrease in pore pressure could be due to the presence of ductile clay minerals. This unusual decrease in BI_T is more prominent in the Encounter 1 well. This Figure displays the frequency of each output. Based on frequency, the BI_T for the Roseneath and Murteree shale mostly fall

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between 0.4-0.45 (Less brittle region), whereas, pore pressure falls mostly into the range

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from 4000-4800 Psi. This outcome is consistent with the previous study of Yasin et al., 2017.

Figure.16: Relationship between pore pressure and total brittleness index. Note that no significant relationship exists between them, except for a few zones where pore pressure increases alongside a decrease in brittleness.

ACCEPTED MANUSCRIPT 3.5

Relationship between brittleness index, strength, and failure parameters

In order to find a relationship between stiffness, strength, and brittleness of reservoirs, calculated properties are compared with the brittleness index (BI_T). A multi-well cross plot is made to reveal a relationship between unconfined compressive strength and angle of friction with brittleness index (BI_T), as shown in Figures 17. There are no significant effects

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observed between these parameters, except some layers where BI_T increases along with an increase in compressive strength and angle of friction. The positive relations in a few layers are consistent with proposed definitions of brittleness index; i.e., brittle rock has a high angle of friction and unconfined compressive strength (Atindag, 2003). Nevertheless, this is not

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always true because strength and stiffness are two different properties of materials. Stiff material may or may not be strong because strength refers to load carrying capability related

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to material failure properties, whereas stiffness actually refers to deflection capability related to material property. Moreover, brittleness index measures the stiffness and ability of rock to make fractures (Bai 2016). Therefore, the bottom line is that brittle rock may or may not

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possess high strength or any angle of friction.

Figure.17: Relationship between strength and failure parameters with brittleness index. Note that no significant relation exists. Brittle rock may/may not have high compressive strength and angle of friction.

3.6

Relationship between fracturing pressure and brittleness index

A key challenge in any hydraulic fracturing is, of course, the selection of the most suitable working parameters, including the selection of fracturing fluid, injection flow rate, and fracturing intervals for better gas recovery. Therefore, it is necessary to possess a

ACCEPTED MANUSCRIPT comprehensive knowledge of each phase of the fracturing process, including treating pressure (pressure at which rocks break), direction of fracture, size and width of fractures, and fracturing intervals (Wang, 2016). The figures 18, 19 and 20 clearly indicate that there is a significant decrease in fracturing pressure where there is an increase in brittleness index. It can be realized that there is a significant decrease in brittleness index (BI_T), with an

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increase in pressure for open fractures (Popen), pressure for closed fractures (Pclose), and breakdown pressure (Pb). In Figure 19, the upper Roseneath formation has less fracturing pressure than the middle Roseneath formation, while the lower Roseneath Formation has more fracturing pressure, this being consistent with a comparative analysis of brittleness

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index (Figure 19). Similarly, in Figure 20 the upper Murteree formation has less fracturing pressure than the lower Murteree formation. Moreover, the multi-well cross plots are made to uncover the relationship between treating/fracturing pressure and brittleness index (BI_T) .

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Generally, each fracturing pressure decreases with an increase in brittleness index (BI_T), which could be due to the presence of quartz and siderite and the stiff behavior of the rock. Many researchers observed that less fracturing pressure is required to open fractures in brittle layers due to the presence of quartz and carbonate contents (Jaocbi et al. 2009). If this is the case, then these two minerals also contributed to high brittleness index and increased Young's

0.6

0.55

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modulus. Hence, less fracturing pressure is due to higher brittleness index (BI_T).

0.6

0.55

0.5

Brittleness Index (BI_T)

0.4

0.35

BI_Total

Brittleness Index (BI_T)

0.45

0.3

0.25

0.2

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0 4000

0

6000

8000

P_Open

10000

Pressure for open fracture (Popen in Psi)

12000

14000

10000

15000

20000 25000 P_Close

30000

35000

Pressure for closed fracture (Pclose in Psi)

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0.6

0.55

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0.45

0.4

0.35

BI_Total

Brittleness Index (BI_T)

0.5

0.3

0.25

0.2

0.15

0.05

0

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0.1

10000

15000

20000 25000 P_Breakdown

30000

35000

EP

Breakdown Pressure (Pb in Psi)

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Figure..18: Relationship between fracturing pressure and brittleness index. Note there is a significant decrease in fracturing pressure with an increase in brittleness Index. This suggest the less fracturing pressure is required to induce fracture in brittle layers.

Roseneath Formation

BI_T

BI_T

BI_T

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Figure. 19: Relationship between fracturing pressure and brittleness index. Note; Significant decrease in fracturing pressure with an increase in brittleness index.

Encounter 1

Scale : 1 : 600 DB : Encounter 1 las files (2)

Zonation

DEPT (M)

Zone 10 Zone 11

Zone 12

3500

DEPTH (3492M -Formation 3568.8M) Murteree

23/9/2017 01:41

GM:σ-hmin (Psi) GM:P- close GM:P- breakdown 3550. 16500. 6000. 48000. 6000. 48000. BI:B-Total BI:B-Total BI:B-Total 0. 1. 0. 1. 0. 1.

BI_T

BI_T

BI_T Upper Murteree

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Figure. 20: Relationship between fracturing pressure and brittleness index. Note; Significant decrease in fracturing pressure with an increase in brittleness index.

3.7

Stress regime

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It is very helpful to be able to obtain a measure of the magnitude of in-situ stresses. For the magnitude of stresses helps in defining the stress regime in a well or field. For instance, there

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are three stress regimes; namely normal, strike slip, and reverse regimes. In the normal stress regime, vertical stress is higher

(σV ≻ σ H ≻ σ h ) and a fault slip occurs when stress, least

horizontal stress, is very low. In the strike slip regime, there exists an intermediate stress state and horizontal stress is at a maximum level (σ H

≻ σV ≻ σ h ) . In the reverse stress regime,

maximum horizontal stress is greater than vertical stress

(σ H ≻ σ h ≻ σV ) and the stress

field is compressive. The comparison of in-situ stress conducted in this study indicates that Encounter 1, Moonta 1, and Holdfast 1 have a normal stress regime because vertical stress is

ACCEPTED MANUSCRIPT higher (σV

≻ σ H ≻ σ h ) . This implies that hydraulic fracture propagates in a vertical plane

and the fracture plane will be perpendicular to minimum horizontal stress. 4. Discussion The average porosities, organic richness, and amount of mineral contents are all tabulated in

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Tables 2 and 3. The average values indicate that both the Roseneath and Murteree formations have better potential to become a shale gas reservoir. Shale gas evaluation requires a consistent volume of mineral contents and this can be achieved by means of a thorough integration of geophysical wire-line logs and XRD data. Both the Roseneath and Murteree

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formations have more quartz, clay (illite, kaolinite, and minor chlorite), carbonates (siderite, minor limestone), and heavy minerals (rutile, anatase and minor pyrite).

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It has become common practice in the industry to find reservoir responses from brittleness index. It goes without saying that brittle rock has a high brittleness index, but it also has a high Young’s modulus, a higher amount of quartz and carbonate, and a lower Poisson’s ratio ( Guo et al. 2013; Rickman et al. 2008). Brittleness indices described in the literature show different characteristics of rock (Hucka et al., 1974; Jarvie et al., 2007; Wang et al., 2009). Any brittleness index is usually estimated using elastic parameters (Young’s modulus and

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Poisson’s ratio) and mineral contents. Nevertheless, a measure of brittleness from these two approaches does not yield a consistent picture of brittleness, even when measurements are taken on the same reservoir. Different researchers define the term brittleness in their own terms, making any attempt at a standard definition more conspicuous. Generally, brittleness is

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used as a descriptor of brittle failure during hydraulic fracturing. Brittleness; a property estimated from elastic parameters and mineral contents, could also be applied to delineate

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brittle layers of rock for fracturing. Well documented evidence indicates that brittleness from elastic parameters, high quartz and carbonate contents could be applied to brittle behavior of reservoir sites (Grieser et al., 2007). Naturally, different empirical relations exist when attempting to define brittleness from a mineralogical perspective. Some researchers believed that only quartz is brittle (Jarvie et al, 2007; Rybacki et al.,2016), while others believed that quartz and carbonates are brittle (Jin et al., 2014a), and that porosity also influences the brittleness of rock (Raybacki et al., 2016). BI_2 to BI_6, for example, are proposed relations from different researchers. BI_7 is computed from a combination of BI_4 and BI_6, with the general conclusion that quartz and carbonate are brittle, with the added influence of porosity on rock brittleness. Brittleness index from conventional well logs is of great importance for

ACCEPTED MANUSCRIPT fracturing ability. In practice, BI_1 serves as the most representative definition, and it is also considered important that brittleness from mineralogy should be closely related to brittleness from elastic parameters (Jin et al., 2014b). As discussed earlier, elastic properties (Young’s modulus and Poisson’s ratio) and brittleness index from elastic parameters (BI_1) may be calculated from sonic logs and density logs.

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Static parameters on core samples offer only limited values, and due to the difference between static and dynamic parameters, calibration is considered necessary between these two parameters to acquire actual values of elastic parameters. Therefore, calibrated Young’s modulus and Poisson’s ratio are both used to find brittleness index (BI_1). BI_1 is, as

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mentioned, traditionally treated as the most accurate way of measuring brittleness index, and dipole sonic logs and density logs can be used to find BI_1 and serve as standard methods.

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However, BI_1 cannot be calculated for wells, which are missing of any one of these logs. In this case, brittleness index from mineralogy from XRD serves as standard. But mineralogy from XRD is not continuous, so mineralogy from the Mineral Model may be used to find brittleness index (BI_7). Hence, in the presence of both geomechanical properties and a consistent volume of minerals, the average of both BIs may be most representative of brittleness index (Guo et al., 2013). Therefore, comparative analyses were carried out

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between the brittleness indices from all approaches. BI_1 compared favorably with other models and there were very good correlations found between BI_1 and BI_7, clearly implying that quartz and carbonates are brittle minerals and that porosity does effect rock brittleness. Hence, a total brittleness index (BI_T) was estimated from an average of BI_1

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and BI_7 and was used to classify rock into brittle, less brittle, less ductile, and ductile types. The model BI_7 is therefore recommended to find brittleness from mineral contents.

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Petrophysical properties, then, do effect the geomechanical properties of reservoirs. In the literature, some researchers argued that brittleness index could be calculated from the ratio of compressive strength to tensile strength (Atindag, 2003) and angle of friction (Jin et al., 2014a). However, this study argues that brittle rock may or may not have a high compressive strength and friction angle. There is, in fact, no direct relationship between brittleness index and other mechanical properties such as uncompressive strength, tensile strength, friction angle, cohesion, and pore pressure. Petrophysical properties, including TOC and porosity, do indeed effect brittleness index (Wang et al., 2009; Haeidri et al., 2014). There is a decreasing trend of brittleness index observed alongside an increase in TOC and porosity. Brittleness index is best represented by elastic parameters (Young’s modulus and Poisson’s ratio) and

ACCEPTED MANUSCRIPT mineral contents. Furthermore, high and low TOC content and porosity can mark brittle, less brittle and less ductile zones. 4.1

Identification of fracture barriers and potential layers for hydraulic fracturing

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The main purpose of hydraulic fracturing is to achieve favorable linear extension, optimum width, and vertical height growth of hydraulic fractures, while at the same time gaining the opportunity to identify brittle layers with less stress concentration for better production. To achieve these goals, reservoir characterization is needed from the different aspects, which are directly controlling efficiency of the hydraulic fractures. Therefore, it is important in fracture

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design to locate fracture barriers controlling vertical growth and to locate those zones, which are brittle enough for favorable lateral extension of fractures (Jacobi et al., 2009). Brittleness

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index by itself would not be sufficient to find barriers and potential layers. Therefore, stress concentration around well bore must be accessed in order to detect barriers and potential layers. As it is clearly understood in the field, hydraulic fractures propagate perpendicular to the least-principal stress and in deep formations, due to considerable overburden stress. Minimum horizontal stress is, in fact, the least-principal stress (Zoback et al., 2003). In-situ minimum horizontal stress is the most vital pre-fracture stimulation parameter, also taken as

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closure pressure. For instance, changes in minimum horizontal stress can only extend laterally with difficulty to create a pathway for gas recovery (Jacobi et al., 2009). In this study, fracture barriers and potential layers are identified based on variation in brittleness

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index and in-situ minimum horizontal stress. Brittleness index (BI_T), which is a function of both mineralogy and elastic parameters, was classified at the Roseneath and Murteree formations into brittle, less brittle, less ductile, and ductile behavior, as previously noted.

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These behaviors were further investigated with in-situ minimum horizontal stress. The layers possessing high BI_T (usually greater than 0.4) showed less minimum horizontal stress and were considered the best potential layers. Layers with less BI_T (less than 0.4), on the other hand, and a high minimum horizontal stress corresponded to fracture barriers. An integrated model, developed for the Roseneath Formations Encounter 1 well, is shown in Figures 21 and 22. This model illustrates a comparison of fracturing pressure, mineralogy, brittleness index and other petrophysical and geomechanical properties. In this well, there is a strong lateral correlation observed between brittleness index (BI_T), minimum horizontal stress, and fracturing pressures. At the top of the Roseneath formation, quartz and siderite contents increase corresponding to a reduction in clay content. The minimum horizontal stress is also

ACCEPTED MANUSCRIPT relatively less than in the middle and lower parts of the formation. In this upper part, there are a number of blue-colored layers, considered favorable for fracturing because their organic richness and total porosity are also relatively less compared to the lower parts. However, there are certain visible fracture barriers which are black in color, possibly representing fracture attenuators. There is relatively high magnitude of minimum horizontal stress and low

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BI_T (<0.4) was observed in fracture barriers (Black zones), where the less magnitude of minimum horizontal stresses and high BI_T(>0.4) was observed in optimal/prospected/brittle layers (Blue zones). There few zones where BI_T is very close to 0.4 and relatively medium minimum horizontal stresses are considered as partial brittle layers (while zones). The

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fracturing pressure profile is also relatively less than the middle and lower parts of the formation. There is a definite trend where minimum horizontal stress increases from the top section towards the bottom of the formation, while the middle part has less favorable layers

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for fracturing due to relatively high minimum horizontal stress, organic richness, total porosity, and less brittleness index. In comparison with the upper and middle parts, the lower part almost behaves as a fracture barrier and increases along with the increase in clay content. The fracturing pressure is clearly higher than in the above layers. These finding are also consistent with those previously discussed in Figures 19 and 20. There is a definite pattern of

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an increase in fracturing pressure, minimum horizontal stress, and clay contents, along with a decrease in brittleness index (BI_T).

For the Murteree formations in Encounter 1 well, a similar trend was observed. The upper part of the formation reveals relatively more favorable zones than the lower part. In

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comparison, the Roseneath formation is seen as more favorable for fracturing than the Murteree formation in Encounter 1 well. The brittle quartz and siderite within these

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formations clearly marks a difference in fracture performance between the Roseneath and Murteree formations.

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ACCEPTED MANUSCRIPT

Figure.21 Integrated Model for identification of potential zones and fracture barriers for optimizing hydraulic fracturing treatment in Roseneath Formation, Encounter 1

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Murteree Formation

3530

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EP

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3540

a)

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3520

3560

3550

Breakdown Pressure (Psi) 6000 44000 Reopening Pressure for Closed fracture (wt.%) 17000 6000 48000

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3510

3550

(σh) (Psi)

b)

Figure.22: a) Integrated Model for the identification of potential zones and fracture barriers for optimizing hydraulic fracturing treatment in Murteree Formation, Encounter 1. b) Legends of mineral contents and lithology.

Mineralogy

3500

1

Clusters

Zonation

Lithology

Depth (M)

B_T C (Mpa) T T.PHI 0 50 Mpa) 0 1 0 0 100 θ UCS TOC (Degree) (Mpa) (wt.%) 0 90 0 300 0 12

Fracture zones and barrier

Failure Strength Porosity Total Minimum Fracturing Property Properties and Brittlenesshorizontal pressure stress organic Index richness

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The Roseneath formation in Holdfast 1 well shows a greater variation in properties, as illustrated in Figures 23 and 24. The Roseneath formation certainly has more favorable layers in this well. These favorable layers also correspond to the presence of more brittle quartz and siderite than in the Encounter 1 well. In comparison, the Murteree formation possesses

Roseneath Formation

Minimum horizontal stress in Psi

Fracturing pressures for closed fractures (P_Close) and new induced fractures (P_Breakdown ) in Psi

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Total Brittleness Index (BI_T) in fraction

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Organic richness (TOC) in wt % and total porosity (T.PHI) in fraction

Mineralogy

Strength Parameter s UCS in Mpa and Tensile strength (T) in Mpa

Classification Fracture barrier and potential layers

Lithology Failure Parameter s (Friction angle (θ) in degree and

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more potential than the Murteree formation.

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relatively fewer prospected layers. In both wells, the Roseneath formation definitely exhibits

Figure.23: Integrated Model for the identification of potential zones and fracture barriers for optimizing hydraulic fracturing treatment in Roseneath formation, Holdfast 1.

ACCEPTED MANUSCRIPT The relatively minimum horizontal stress calculated for favorable potential layers of the Roseneath formation is less than that of the Murteree formation. Treating pressure for stimulating the rock in the lower part of the Roseneath formation is higher than in the lower part of the Murteree formation. Higher treating pressure contributes to the presence of less quartz, while a lower treating pressure contributes to a higher amount of quartz, indicating a

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high brittleness index (BI_T) (Jaocbi et al., 2009). Potentially favorable layers are thereby identified and recommended for fracturing, as there will probably be good fracture initiation and propagation in these layers. On the other hand, fracture barriers will cause vertical fracture confinement and in many previous cases poor confinement of fractures had a very

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negative consequence on production, especially when fractures communicate with a waterbearing level (Fjar et al., 2008).

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Brittle quartz and carbonate minerals, as mentioned previously, create a significant difference in fracture performance between both formations. These brittle minerals may have natural fractures that contribute to the growth of hydraulic fractures. Thin sections of Roseneath and Murteree shale evince the mineralized fractures in quartz and siderite rich intervals, as shown in Figures 25 and 26. The presence of fractures in these formations indicate that there would less fracturing pressure required to break the rock and that these

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fractures may connect with induced fractures to enhance permeability.

ACCEPTED MANUSCRIPT Murteree Formation

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Minimum horizontal stress in Psi

Fracturing pressures for closed fractures (P_Close) and new induced fractures (P_Breakdown) in Psi

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Total Brittleness Index (BI_T) in fraction

Figure 24: Integrated model for the identification of potential zones and fracture barriers for optimizing hydraulic fracturing treatment in Murteree formation, Holdfast 1.

Mineralogy

Organic richness (TOC) in wt % and total porosity (T.PHI) in fraction

Classification

Strength Parameters UCS in Mpa and Tensile strength (T) in Mpa

Fracture barrier and potential layers

Lithology Failure Parameters (Friction angle (θ) in degree and cohesion (C) in Mpa

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3.25mm

1.30mm

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0.65mm

Siderite mineralized fracture

a) b)

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Quartz mineralized fracture

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b) b) Quartz mineralized fracture Figure 25 a) Siderite mineralized fractures in Murteree shale (Red arrow) in clay lamina in Murteree shale (Red arrow). Note: Y shaped branching (Red arrow) represent fracture pattern where thinner fracture is connecting with wider fractures, Encounter-1. Figure 26 a) Quartz mineralized fracture (red arrow) and open fracture (blue arrow) in Roseneath shale b) showing brittle crystal of siderite ( red arrow), organic matter ( opaque, blue arrow), quartz ( green arrow) in Roseneath shale. Note: Less fracture pressures are required to open these fractured rocks, Encounter-1.

When gas escapes from the

3.25m m

a

thermal source from beneath (Jaocbi et

al.,

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matrix, brine fluid flows, probably from

2009). This fluid passes through the

matrix and minerals like quartz and

mineralization

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carbonates precipitate from the solution, occurs

due

to

Quartz mineralized fracture and

the

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precipitation of these minerals. Some mineralized fractures remain as open

a)

fractures in the matrix. Hence, brittle minerals contribute to the development of natural fractures. These natural fractures lower down the fracturing pressure required to break the rock. The type of fluid required, and the proppant type and size depend upon brittleness and the minimum horizontal stress. For high brittleness the best fluid type is slick water with sand as a proppant (Rickman et al. 2008). The practical use of an integrated model helped to analyze and recommend a stimulation design for both the Roseneath and Murteree formations, as shown in Table 4 and 5.

ACCEPTED MANUSCRIPT

Zone

Brittleness

Thickness

Minimum

P_b (Psi)

Recommendation

horizontal stress m

Fluid type

Proppant

Psi 0.4

type

5628

13478

23 2

0.41

5905

0.4

6592

0.41

6953 8234 10098 7

7

0.305 0.27

Y

-

-

Y

-

-

N

-

-

N

-

-

N

-

-

N

-

-

N

28997

11189 20

9

sand

29202

9889 20

8

Slick water

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0.32

Y

22942

56 6

sand

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0.34

Slick water

18567

24 5

Y

17228

23 4

sand

14599

8 3

Slick water

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1

0.28

32550

10952 4

Frac. ?

13478

Zone

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Table 4: Recommendation for stimulation design in Roseneath Formation, Encounter-1 well.

Brittleness

Thickness m

Minimum

P_b (Psi)

horizontal stress Psi

10

0.35

11830 4

Recommendation

Fluid type

Proppant type

Frac. ?

Slick water

sand

N

31430

ACCEPTED MANUSCRIPT 11

0.44

9034

22527

3 12

0.46

7900

0.43

8870

0.36

11347

Y

Slick water

sand

Y

Slick water

sand

Y

-

-

N

23765

21 14

-

19984

9 13

-

39350

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40

Table 5: Recommendation for stimulation design in the Murteree formation, Encounter-1 well.

The design for hydraulic fracturing is recommended based on identified fracture

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barriers and brittle layers. It has been seen that relatively low fracturing pressure is required to induce fracture at layer with high brittleness index (>0.4) and the minimum horizontal

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stress is also low in brittle layers. Whereas, at fracture barriers, the low brittleness index (<0.4) and high magnitude of minimum horizontal stress is observed with high fracturing pressure required to induce fractures. The zones marked with “Y” are recommended for fracturing, whereas, zones marked with “N” would not be potential to make efficient network of fractures.

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Conclusion

Based on the results, analyses and discussion in this study, it is concluded that: •

The design of hydraulic fracturing stimulation can be optimized through identification

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of fracture barriers and prospected/optimal/brittle layers through reservoir characterization, in terms of its petrophysical and geomechanical properties, which

•

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can be estimated /measured from core-log integration. The consistent volume of reservoir parameters can be estimated through correlations based on calibration of wire-line logs and core data.

•

Brittleness index and in-situ earth stresses are factors that can be used to select most productive/brittle layers and fracture barriers to optimize hydraulic fracturing in Roseneath and Murteree shale gas formations

•

Brittleness index is a function of mineralogy, porosity and elastic parameters and can be used to classify reservoirs into brittle, less brittle, less ductile, and ductile layers;

ACCEPTED MANUSCRIPT where quartz and siderite are considered brittle minerals and clay minerals are the most ductile minerals with the addition of porosity on rock brittleness. •

The comparative analysis of brittleness index indicated that B_I based on calibrated elastic parameters and BI_7 based on mineralogy are most representative of brittleness, in such case, average of BI_1 and BI_7 could be used to represent the

•

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brittleness of rock. The most productive/suitable layers possess a high BI_T (>0.4) and low magnitude of minimum horizontal stress associated with both brittle layers and less brittle layers. Nevertheless, layers act as fracture barriers with low BI_T (<0.4) and high magnitude •

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of minimum horizontal stresses can hinder the growth of induced fractures.

The proposed fracture barriers are considered barriers for fracture growth, which could be used to control the fracture growth into an unwanted adjacent faulted and

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water rich zones areas. Whereas, brittle/optimal layers are amenable for fracturing, where fracture would be initiated and propagated more efficiently. •

The practical application of an integrated study lies in the recommendation of design of hydraulic fracturing stimulation in Roseneath and Murteree shale formation, which may/may not be applicable to other shale formation depending upon the petrophysical

•

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and geomechanical properties.

There is a normal stress regime marked in Encounter-1, Holdfast-1 and Moonta-1, where hydraulic fractures will be propagating in perpendicular direction to minimum

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horizontal stress with shorter width, but longer fractures. While in KINGSTON RULE -1 and SASONOF -1 wells, the stress regime is strike slip, hydraulic fractures will be perpendicular to minimum horizontal stress with shorter length but with longer

•

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width.

When compared, Roseneath shale is found to be brittle to less brittle in nature, whereas Murteree shale is less brittle in nature. This classification makes Roseneath more suitable for hydraulic fracturing.

5. Acknowledgments The authors are thankful to University Teknologi PETRONAS and PETRONAS for providing us funding for this research. The authors are also thankful to the DSD (Department

ACCEPTED MANUSCRIPT of State Development) Australia for their generosity in providing us with a generous amount of freely available data. 6. References

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Highlights •

An integrated study has been carried out through reservoir characterization in terms of petrophysical and geomechanical properties using geophysical wire-line logs and core

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analysis.

The fracture barriers and potential/prospected/brittle layers are located based on brittleness index comes from mineralogy and elastic parameters, and in-situ earth stresses

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The design for hydraulic fracturing is recommended in studied formations and establish

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several correlations that can be used in the absence of core data.

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in Roseneath and Murteree Shale Gas formations, Cooper Basin, Australia.