Nanopores to megafractures: Current challenges and methods for shale gas reservoir and hydraulic fracture characterization

Accepted Manuscript Nanopores to Megafractures: Current Challenges and Methods for Shale Gas Reservoir and Hydraulic Fracture Characterization C.R. Clarkson, B. Haghshenas, A. Ghanizadeh, F. Qanbari, J.D. Williams-Kovacs, N. Riazi, C. Debuhr, H.J. Deglint PII:

S1875-5100(16)30040-3

DOI:

10.1016/j.jngse.2016.01.041

Reference:

JNGSE 1245

To appear in:

Journal of Natural Gas Science and Engineering

Received Date: 25 December 2015 Revised Date:

27 January 2016

Accepted Date: 28 January 2016

Please cite this article as: Clarkson, C.R., Haghshenas, B., Ghanizadeh, A., Qanbari, F., WilliamsKovacs, J.D., Riazi, N., Debuhr, C., Deglint, H.J., Nanopores to Megafractures: Current Challenges and Methods for Shale Gas Reservoir and Hydraulic Fracture Characterization, Journal of Natural Gas Science & Engineering (2016), doi: 10.1016/j.jngse.2016.01.041. 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.

ACCEPTED Nanopores to Megafractures: CurrentMANUSCRIPT Challenges and Methods for Shale Gas Reservoir and Hydraulic Fracture Characterization *C.R. Clarksona, B. Haghshenasb, A. Ghanizadeha, F. Qanbarib, J.D. Williams-Kovacsb, N. Riazia, C. Debuhra, and H.J. Deglinta b

a Department of Geoscience, University of Calgary, 2500 University Drive NW Calgary, Alberta, Canada T2N 1N4 Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW Calgary, Alberta, Canada T2N 1N4

*Corresponding author – Ph. (403) 220-6445, e-mail: [email protected]

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ABSTRACT The past two decades have seen a tremendous focus by energy companies on the development of shale gas resources in North America, resulting in an over-supply of natural gas to the North American market in recent years. This shale gas “revolution” was made possible primarily through the application of drilling and completion technologies, particularly horizontal wells completed in multiple hydraulic fracturing stages (multi-fractured horizontal wells, or MFHWs). While these technologies have proven successful in commercializing the resource, imperfect understanding of basic shale gas reservoir properties, and methods used to characterize them, has perhaps led to inefficiencies in shale gas resource development and recovery that can be improved over time with further research. The purpose of the current work is to 1) provide an overview of shale gas storage and transport mechanisms 2) summarize the challenges associated with evaluating key reservoir and hydraulic fracture properties and 3) discuss recent advances by the authors in the area of shale gas reservoir and hydraulic fracture characterization. For the latter topic, advances in multi-scale characterization techniques, from reservoir sample evaluation to production data analysis will be addressed, however an emphasis will be placed specifically on methods to evaluate reservoir and rock properties along the length of the horizontal well to enable selection of hydraulic fracture stage placement and improved well forecasting. Rock cuttings retrieved during drilling are typically the only reservoir samples obtained from horizontal wells, and therefore methods for quantitative assessment of pore structure, gas content, gas-in-place, permeability, fluid-rock interaction, and rock mechanical property assessment will be discussed. In particular, the following recent innovations by the authors are highlighted: 1) use of the Simplified Local Density (SLD) model to account for fluid property alteration from pore confinement, and to predict high pressure gas adsorption from low-pressure adsorption data collected for small amounts of cuttings samples 2) extraction of permeability/diffusivity from low-pressure adsorption rate data, also collected for small amounts of cuttings samples 3) use of a variable pressure, environmental SEM to assess fluid distribution and micro-wettability to support pore-scale modeling studies 4) estimation of unpropped hydraulic fracture permeability through generation of fractures in coreplugs under stress 5) use of microhardness tests to evaluate fine scale changes in “mechanical stratigraphy” and 6) use of sonic core-holders to provide measurements of dynamic rock mechanical properties for shale samples subject to in-situ stress. Modification of diagnostic fracture injection tests (DFITs), a common well-testing technique performed on shales to derive reservoir property and stress information but performed usually only at the toe of horizontal wells, to enable tests to be performed at multiple points along a horizontal well, will be proposed. Finally, advances in production analysis methods to account for effects such as pore confinement, relative permeability, and stress-dependent permeability will be reviewed, as will techniques for extracting hydraulic fracture properties through analysis of flowback data. It is hoped that this summary will provide geoscientists and engineers with a comprehensive overview of shale gas reservoir and hydraulic fracture evaluation challenges and potential solutions, with a view to enabling more efficient shale gas extraction.

Keywords: shale gas; fluid storage; fluid transport; reservoir characterization; hydraulic fracture characterization

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ACCEPTED MANUSCRIPT 1. Introduction

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Shale gas, and more recently liquid-rich shale gas, reservoirs are the target of intense development focus and activity in North America and have sparked interest in unconventional reservoirs globally. Shales in the context of petroleum systems have traditionally been thought of as non-reservoirs, often forming the seals or caprock for conventional hydrocarbon systems. Organic-rich shales, given the right combination of organic matter concentration, type, and thermal maturity (amongst other factors), have further fulfilled the critical role as source rocks for the world’s largest hydrocarbon accumulations. Therefore, much of the prior shale characterization efforts have been tailored to study their source and seal potential. However, the potential of shales as hydrocarbon reservoirs has recently been realized through the application of modern drilling, completion and stimulation technologies, creating a new list of characterization challenges that must be addressed. In particular, due to the ultra-fine pore structure of shales, quantifying the basic properties that affect fluid storage and transport has proven difficult. Further, while it is generally acknowledged that hydraulic fracturing is necessary to yield commercial rates from shale gas wells, the complexity of the created hydraulic fractures and their flow properties have also been challenging to characterize. The typical stages used in shale reservoir development by operators are illustrated in Fig. 1. Not shown is an initial screening phase during which the prospect is selected through the mapping of key properties affecting resource size. The first exploration well (Phase 1), which is typically a vertical well that is drilled through the shale section of interest, and may be selected with the aid of 2D seismic, is usually cored and logged with the specific objectives of evaluating the following over multiple vertical intervals 1) hydrocarbon and non-hydrocarbon in place and fluid saturations 2) rock compositional variations (inorganic and organic matter) 3) organic matter, thermal maturity and hydrocarbon generation potential 4) permeability variations and 5) geomechanical property variations. Additional vertical appraisal wells (Phase 2) may be drilled to determine lateral continuity of the intervals of interest and structural controls, and to collect additional data consistent with the first exploration well. Note that pre-stimulation testing, such as diagnostic fracture injection tests (DFITs), may be performed on some vertical intervals in these wells to obtain closure pressure, initial reservoir pressure, reservoir and leakoff characteristics used in the design of stimulations in future wells. Further, experimental hydraulic fracturing and production testing may be undertaken on select intervals. Phase 3 consists of drilling horizontal wells in one or more reservoir intervals selected using data collected from the vertical wells for the purpose of 1) obtaining additional pre-stimulation data (e.g. DFITs), 2) optimizing drilling, completion and stimulation practices, and 3) demonstration of commercial production. The horizontal wells used in shale development are often completed in multiple hydraulic fracturing stages (multi-fractured horizontal wells or MFHWs) to maximize well performance. Phase 3, in terms of go-forward decision making, is probably the most critical because ultimately, if the project proceeds, then the ultimate well spacing, completion strategy and hydraulic fracture stimulation design (the most costly components of shale resource development) are often decided during this Phase. Additional data will be collected during this Phase to supplement that collected during Phases 1-2. However, important new sources of information will be collected to guide completions/stimulations decisions including 1) microseismic data, to assist with understanding fracture complexity, height growth and “stimulated reservoir volume” (SRV), 2) flowback and production data, the former of which can be used to judge stimulation efficiency, and the later being important to determine the commerciality of the play and 3) additional surveillance data such as production logs (PLTs), distributed temperature and acoustic sensing (DTS and DAS, respectively) to assist with determining individual hydraulic fracture stage contribution. Phase 4 implements the well spacing/completion/stimulation concepts developed during Phase 3 and determines whether the project should progress to full development – similar data to that collected in Phase 3 will be collected during this stage. Finally, Phase 5 consists of drilling development wells from pads, completed in a single interval, or multiple intervals, as selected using data collected and interpreted from the previous Phases. During this Phase, additional data collection will be minimal, except for additional DFITs performed (ex. one well/pad, with the test usually performed on the toe of the well in advance of stimulation operations), and possible microseismic data to interpret effectiveness of stimulations. The primary goals of this Phase are to 1) decrease cost and 2) maximize well performance and hydrocarbon recovery.

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ACCEPTED MANUSCRIPT Horizontal Pilot Well (Phase 3)

Demonstration Project (Phase 4)

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Discovery Well (Phase 1)

Development Wells from Pads (Phase 5)

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Delineation Well (Phase 2)

Fig. 1. Illustration of possible phases used for exploration and development of a shale reservoir. Modified from B. Kalynchuk (2015).

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In all Phases of shale project development discussed above, laboratory and field testing techniques are used to derive properties of the reservoir necessary for understanding shale hydrocarbon resource, recovery, and well production performance, amongst other factors, in order to devise the optimal development plan for a field. In a previous summary paper, Clarkson et al. (2012a) provided a proposed workflow for achieving this development plan. In Fig. 2, a critical step in this plan, reservoir and hydraulic fracture characterization, is illustrated. The boxes highlighted in green refer to analysis methods that will be discussed in this work, due to space limitations. Each of these methods (column 3), performed at various stages of well drilling and completion (column 2), are designed to derive reservoir and other data required to evaluate shale hydrocarbon resource, recovery or well performance (column 4). However, the amount of reservoir sampled with each analysis type varies, providing a significant challenge for data integration. The end goal is to use such data in predictive mode (predict future well production, evaluate scenarios for future development etc.) with the use of a model (e.g. a reservoir simulator), but upscaling of reservoir properties from core (or finer) to field scale remains one of the greatest challenges in unconventional reservoir evaluation. Indeed, processes that occur at the nano-scale (e.g. in nanopores of the shale matrix) ultimately affect flow at the macro-scale (10s – 100s of meters or greater) to a well completed in a shale reservoir (Javadpour, 2007). The focus of the current work is a discussion of challenges, and current progress, in evaluating reservoir and hydraulic properties at the various scales, according to analysis type. Evidence of recent research progress is provided using examples from the authors’ research.

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Development Stage

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Properties Derived

Seismic, 2D, 3D

Pre-Drill

Reservoir Sample Analysis

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km , kf, So, Sg, Sw, kro, krg, krw, ρm, ρb, øm, PSD, PTD, Pc, a, m, n, OM, IOM, Ro, Gc, Es, νs

km , kf, h, So, Sg, Sw, ρb, øm, øf, PSD, OM, IOM, ED, νD

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Pre-Frac Welltest (DFIT)

khsys, Pclosure , Preservoir

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Post-Frac Welltest (F/BU) Production Analysis

khf , khsys, Pbreakthrough, xf xf, A c, FcD khsys, xf, A c, FcD

khsys, OGIP/OOIP, CGIP/COIP, xf, Ac, FcD

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Frac geometry, SRV

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Fig. 2. Summary of reservoir and hydraulic fracture property characterization methods used for shale. The development stage for each technique is given in column 2; analysis type is given in column 3 (techniques discussed in this work are highlighted in green); reservoir and hydraulic fracture properties derived from each technique are given in column 4 (see Nomenclature section for definitions). 2. Overview of storage and transport mechanisms in shale Unconventional reservoirs are distinguished from their conventional counterparts by the complexity of storage and flow mechanisms. In conventional reservoirs, fluid storage can often be adequately modeled through knowledge of pore volume and (bulk) fluid compressibility, fluid distribution is dictated primarily by buoyancy and gravitational forces, and fluid flow can mainly be described by Darcy’s Law. In unconventional reservoirs such as shales, fluid storage may be due to a combination of adsorption, bulk fluid compression and solution processes. Fluid distribution is affected by capillary and adsorption forces in addition to gravity and buoyancy, and flow in some instances may not be adequately described by Darcy’s Law. Further, conventional and unconventional reservoirs may be distinguished by ease of hydrocarbon extraction; unconventional reservoirs may require advanced technology such as multi-fractured horizontal wells to achieve commercial production. Finally, and very importantly, storage and transport processes in unconventional reservoirs are scale- and locationdependent (Fig. 3). For example, fluid transport in larger scale natural and induced fractures in shale may be described by Darcy’s Law, while, depending on pressure, temperature and fluid properties, flow in nanopores in the shale organic and inorganic matrix may be affected by gas slippage and/or occurs by diffusion. Similary, storage in nanopores may be affected

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ACCEPTED MANUSCRIPT by adsorption processes and by the effects of pore confinement, causing storage to be greater than that achieved by bulk fluid compression, whereas storage in larger matrix pores and fractures is primarily dicated by bulk fluid compression. Extent of Contacted Reservoir Area

Induced Hydraulic Fracture Network

Horizontal Well

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1000 m+ Reactivated Natural Fractures

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Induced Hydraulic Fracture meters

Matrix with interspersed organic and inorganic matter

Matrix with fine-scale laminations and fractures

Matrix

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Nanopore structure of organic and inorganic matter

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Fig. 3. Illustration of the scales of flow that occur to a multi-fractured horizontal well completed in a shale reservoir. In the top image, the green ellipse illustrates a drainage area around a single fracture stage on initial flowback of that stage. On the left hand side of the image the red box is zoomed in progressively to illustrate the different scales at which flow occurs within shale reservoirs (meter to nanometer scale).

Fluid storage mechanisms: summary and associated challenges for characterization and fluid-in-place estimation.

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The following is a brief overview of storage and transport processes that occur in shales, providing the starting point for a discussion of characterization methods. In these sections, the challenges in characterizing reservoir properties affecting storage and transport addressed in this paper will also be highlighted. The emphasis of this work will be on shales producing dry gas, but some complexities associated with shale gas-condensate systems (i.e. liquid-rich shales) will also be discussed.

Characterization of pore structure (porosity, pore size distribution, surface area) in unconventional oil/gas reservoirs is a key step for commercial evaluation (Ross and Bustin, 2008, 2009; Loucks et al., 2009; Tian et al., 2013). Shales have a complex pore structure that is associated with organic and inorganic components and various fabrics with a broad pore size ranging from sub-nanometer to several hundreds of nanometers (Ross and Bustin, 2008, 2009; Loucks et al., 2009, 2012; Nelson, 2009; Tian et al., 2013; Chalmers et al., 2012a,b; Milliken et al., 2013; Chen and Xiao, 2014). The exact pore network makeup therefore depends heavily on the organic and inorganic content and composition, as well as thermal maturity of the shale. The primary constituents of organic-rich shales are quartz, clays, carbonates, feldspars, apatite, pyrite, and organic matter (Sondergeld et al., 2010; Curtis et al., 2012). The clay and organic matter contents of organic-rich shales, in particular, impart an isotropic fabric to these complex systems which affect their gas storage, fluid transport and mechanical properties (Sondergeld et al., 2010). In comparison to micrometer-size pores in conventional (sandstone and carbonate) reservoirs, the pores within the matrix system of unconventional oil/gas reservoirs are usually smaller than one micrometer. According to the classification of International Union of Pure and Applied Chemistry (IUPAC, 1994), the pores can be divided into three primary groups; micropores (pore width < 2 nm), mesopores (pore width between 2–50 nm) and macropores (pore width > 50 nm). The location of the pores can be subdivided into two groups: 1) pores in the matrix of minerals or between mineral

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Intercrystalline pores within pyrite framboids

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Pores between grains

Intraparticle Pores

Interplatelet pores within clay aggregates

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Mineral Matrix Pores Pores between or within mineral particles

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particles (Loucks et al., 2012; Tian et al., 2013), and, 2) pores in the matrix of organic matter. The inorganic matrix of shales is generally characterized by relatively large, irregularly-shaped pores and fractures. The organic matrix, which is often finely dispersed within the inorganic matrix, is generally composed of micro- and meso-pores depending on maturity and organic matter type (Bernard et al., 2011, 2012; Curtis et al., 2012). The pores within the inorganic matrix are typically slit-like (Curtis et al., 2012), whereas the pores within the organic matrix are predominately spherical. A significant difference in the location of pores can be seen in the matrix systems of different shale gas plays. While the porosity in the Barnett, Kimmeridge, and Horn River Shales is dominantly within the organic matter matrix, according to Curtis et al. (2012), the porosity in the Haynesville Shale is most prevalent in the inorganic matrix. The inorganic-associated porosity may play a significant role in the gas storage and production characteristics of organic-rich shales. In particular, from a commercial perspective, the slit-like pores within the inorganic matrix of shales are more prone to collapse due to an increase in effective pressure during shale oil/gas production. The closure/collapse of these pores during production can reduce matrix permeability, and consequently, the rate at which hydrocarbons are produced (Curtis et al., 2012). In recent years, there have been excellent attempts to characterize shale pore structure using imaging techniques, primarily with the assistance of scanning electron microscopy (SEM) combined with focused ion beam (FIB) milling techniques. Field emission scanning electron microscopy/transmission electron microscopy (FE-SEM/TEM), Scanning Transmission Electron Microscopy (STEM), Broad Ion Beam Scanning Electron Microscopy (BIB-SEM) and Synchrotron-based scanning Transmission X-ray Microscopy (STXM) have also been successfully utilized to visually characterize the shape/morphology, size and distribution of pores in shales (Loucks et al., 2009; Klaver et al., 2012; Bernard et al., 2012; Chalmers et al., 2012a; Milliken et al., 2013; Tian et al., 2013). Loucks et al. (2012) provided a classification of pore types within the matrix of mudrocks that illustrates the association of pores with the organic and inorganic components as well as fabrics (Fig. 4). Within the mineral matrix, pores can generally be classified as inter (between)- or intra (within)-particle of the inorganic components. Because shales may be complex mineralogically, with a variety of components including (but not limited to) quartz, clays, carbonates, feldspars, and pyrite, the pore structure and size range within the inorganic matrix will vary substantially. Organic matter characterization has similarly led to the conclusion that pore types vary with organic matter type and according to thermal maturity (Bernard et al., 2011, 2012; Curtis et al., 2012). It is not the intent of the current article to detail these associations and resulting pore structure - an upcoming summary by Ghanizadeh et al. (2016; in preparation) provides a more detailed description of pore association with various shale components.

Pores between crystals

Pores within peloids or pellets

Dissolution-rim pores

Pores between clay platelets

Pores within fossil bodies

Moldic pores after a crystal

Pores at the edges of rigid grains

Moldic pores after a fossil

Organic-Matter Pores Pores within organic matter

Fracture Pores Pores not controlled by individual particles

Organic-Matter Pores

Fracture Pores

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Fig. 4. (a) Pore type-classification developed by Loucks et al. (2012) for shales. (b) Real examples of pore types occurring in shales/mudrocks. The real images are equivalent to the schematic images shown in Fig. 4a (same order). The readers are referred to the works of Loucks et al. (2012) and Fishman et al. (2012) for a detailed description of pore types and each image (images A-F, H-M: Loucks et al., 2012; image G: Fishman et al., 2012).

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Not only is shale composition complex, with multi-mineral and organic matter components, this composition commonly varies substantially vertically and horizontally in producing shale gas reservoirs. Long horizontal wells used to produce from shales often encounter 1000+ m of shale interval and rock composition can vary substantially within this interval (Fig. 5). As a result, due to the multi-pore associations illustrated in Fig. 4, fluid storage is similarly expected to vary substantially along the well. However, normally the only reservoir samples that can be used to quantify these variations are rock cuttings. Therefore a primary challenge in shale reservoir characterization is the estimation of this variability along a horizontal well using these cuttings (Fig. 5). Techniques are currently being developed and tested for extracting porosity, pore size distribution, fluid saturation and adsorbed gas content from cuttings and will be reviewed in this work.

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φ5 , PSD5 , Sg5 , Ga5 φ4 , PSD4 , Sg4 , Ga4

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Fig. 5. Conceptual diagram showing the variation of properties affecting fluid storage (porosity, pore size distributions, saturations, adsorbed gas content) along the length of a horizontal well. Due to the complexity in shale composition, there are also multiple mechanisms of fluid storage that are possible. The storage mechanisms, which are partly dependent on pore size, are therefore quite diverse. Using the pore-types as reference (Fig. 4), the possible gas storage mechanisms in shales are as follows:

4. 5. 6. 7.

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Adsorption on the internal surface area of organic matter (Ross and Bustin, 2008, 2009; Chalmers and Bustin, 2008; Zhang et al., 2012; Gasparik et al., 2012, 2014; Rexer et al., 2014; Tan et al., 2014) - Depending on thermal maturity and organic matter type, the organic matter content could be a dominant controlling factor on the sorption capacity in shales. The sorbing capacity of the organic matter is dependent on its nano-scale porous nature. The strong sorption capacity of the organic matter has been verified by studies of organic matter (kerogen) isolated from shales (Rexer et al., 2014) Adsorption on the internal surface area of clays (Cheng and Huang, 2004; Ross and Bustin, 2008; Chalmers and Bustin, 2008; Gasaprik et al., 2012, 2014; Ji et al., 2012; Tan et al., 2014; Heller and Zoback, 2014) - Clay minerals have a small grain size and therefore may also contribute significant surface area for gas adsorption. Comparing various clay minerals typically found in marine/lacustrine shales, the smectite (group) has the largest specific surface area (200 – 800 m²/g) followed by illite (30 – 100 m²/g), kaolinite and chlorite (<30 m²/g) (Chalmers and Bustin, 2008). Due to the complexity of adsorption phenomenon on clay minerals, the relationships reported in literature between sorption capacity of shales and clay mineral content/composition are not fully in agreement (Ross and Bustin, 2008; Chalmers and Bustin, 2008; Gasaprik et al., 2012, 2014; Ji et al., 2012; Tan et al., 2014; Heller and Zoback, 2014). For pure clay minerals, CH4 and CO2 sortion capacities measured by Ross and Bustin (2008), Ji et al. (2012) and Busch et al. (2008) were reported to increase in the order kaolinite < illite < illite/smectite mixed layer < smectite. Free-gas storage in matrix inorganic matter inter- and intra-granular pore space (Ross and Bustin, 2008, 2009; Chalmers and Bustin, 2008; Gasparik et al., 2014; Tan et al., 2014) Free-gas storage in organic matter intra-granular pores (Ross and Bustin, 2008, 2009; Chalmers and Bustin, 2008; Gasparik et al., 2014; Tan et al., 2014) Free-gas storage in fractures (microfractures, larger-scale natural fractures, induced and hydraulic fractures) Solution gas storage (absorption) in entrained fluids (i.e. water, residual oil) Solution gas storage (absorption) in bitumen

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For the above, it should be noted that free- and adsorbed-gas storage may occur simultaneously in certain pore size ranges, which has been discussed in association with organic matter pores in shales (Ambrose et al., 2012). However, this is an oversimplifaction in many cases because a fluid density gradient will occur from the pore-wall to pore-center due to the influence of the forces of adsorption on fluid-rock interaction. This is illustrated in Fig. 6, using the Simplified Local Density (SLD) model (Rangarajan et al., 1995; Fitzgerald et al., 2006; Mohammad et al., 2009; Chen, 1997) to calculate methane densities in slot-shaped pores. Two different pore sizes are assumed: 10 nm and 2 nm. In the 10 nm pore, the bulk phase density is ~ 0.36 g/cm3 and transitions quite quickly to adsorbed-phase density at around 0.44 g/cm3. The density profile is quite constant from z/L = 0.2 to 0.8. In contrast, for the 2 nm pore size, the bulk phase density is constant for only a much smaller range of z/L (0.4 to 0.6), and there is a more gradual transition from bulk to sorbed-phase density. This system could be modeled in a simplistic way as having 1) an adsorbed gas layer and 2) bulk-phase pore center, as illustrated in Fig. 7. However, particularly in the case of the 2 nm pore where the gas density changes gradually, distinguishing adsorbed and freegas phases is clearly more difficult. In still smaller pores (i.e. < 2 nm) the system can be modelled accurately with the

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ACCEPTED MANUSCRIPT assumption of a single-adsorbed phase, as is the case with the Langmuir model or other popular adsorption models applied to microporous systems. The preceding highlights one of the primary challenges in shale gas reservoir fluid-in-place estimation: accurately modeling gas storage in certain pore size ranges. Pore diameter=10nm , P=50MPa

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Fig. 6. Adsorbed phase density profile in organic matrix-pores using SLD model, (a) pore diameter = 2 nm and (b) pore diameter = 10 nm. L is the width of the pore, while z is the distance from one of the pore walls.

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Fig. 7. Adsorbed phase density profile in organic matrix-pores using SLD model, (a) pore diameter = 2 nm and (b) pore diameter = 10 nm. L is the width of the pore, while z is the distance from one of the pore walls. Gas storage may be approximated by distinguishing an average sorbed phase density (highlighted in purple) and bulk phase density (center of pore where density is constant). Referring again to Fig. 7, it should be noted that if organic matter in shale does contain free- and adsorbed-gas phases, and they can be distinguished, then conventional methods for volumetric gas-in-place and material balance calculations, as applied to shales, will be in error, as noted by Ambrose et al. (2012). For example, the conventional approach for shale volumetric gas-in-place calculation assumes that the free- and adsorbed-phase components can be isolated experimentally as follows:

Gt ,i = G f ,i + Ga ,i ......................................................................................................................................................................... (1) Where Gt,i is total initial gas-in-place, and Gf,i is the free-gas component and Ga,i is the adsorbed gas component, which are calculated as follows:

G f ,i =

Ahφ ( 1 − S wi ) .................................................................................................................................................................. (2) Bgi

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Ga ,i = Ahρ b

VL pi ................................................................................................................................................................... (3) PL + pi

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Ambrose et al. noted that use of Eq. (2) ignores the volume in the pore space occupied by the sorbed phase (assumed to be modeled using the Langmuir equation in Eq. (3)) and hence is in error. Both the Ambrose et al. (2012) and newer approaches for correcting for this issue will be discussed in a later section. It should be noted that this error would also exist in static- and dynamic material balance calculations of gas-in-place, which will also be discussed. During production, the adsorbed phase volume is expected to change with pressure, as will the density, and therefore these changes must also be considered for production analysis. Liquids-rich shale gas systems (i.e. shale petroleum systems with gas condensate) are receiving a significant amount of attention in North America and fluid storage mechanisms are even more complex than dry gas shale reservoirs discussed above. As with dry shale gas systems, pore-confinement effects can alter fluid properties, resulting in anomalous production trends observed in the field. For example, shale gas-condensate wells may exhibit long periods of constant condendate/gas ratios which cannot be explained with conventional PVT data collected for bulk fluids. As will be demonstrated using the SLD model, significant shifts in the phase envelope and fluid properties can be affected by pore confinement, resulting in a delay in condensate dropout in the reservoir, consistent with observed production trends. The challenges associated with characterizing properties affecting fluid storage and with fluid-in-place calculations that are discussed in this paper are summarized in Table 1. Table 1 Summary of challenges for characterization of fluid storage mechanisms and calculation of fluid-in-place. Challenge Estimating fluid storage properties along a horizontal well Modeling gas storage FIP calculations: accounting for effects of pore confinement

2.2

Data Source Reservoir samples Reservoir samples Reservoir samples, production data

Sample Volume Cuttings Core plugs or cuttings Core plugs, cuttings, well drainage

Fluid transport mechanisms: summary and associated challenges for characterization and production forecasting.

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Referring to Fig. 3, it is clear that transport processes occur at multiple scales from the nano- (nanometers) to macro(meters) scale and through conduits that are nanometer (nanopores) to micrometer (fractures) in width. As a result, transport mechanisms, which are driven not only by scale, but also reservoir pressure, temperature, and fluid type and composition, are variable. Further, multi-phase flow may occur in the reservoir at various stages of production from initial flowback, which is flow immediately after the well has been hydraulically-fracture stimulated, to long-term (online) production where liquids dropout of the gas in the case of liquids-rich shales, and could become mobile along with the gas. During flowback, multiphase flow may occur due to the simultaneous flow of hydraulic fracturing fluids and reservoir fluids, while during long-term production, single- or multi-phase flow may occur in the reservoir, depending on fluid PVT properties and water mobility. As with storage properties, properties affecting fluid transport are expected to vary along the length of a horizontal well. Therefore characterization methods to extract these properties are of great value for shale reservoir assessment. As noted above, the only reservoir samples typically retrieved from horizontal wells are cuttings, and therefore a primary challenge for characterization is the extraction of permeability/diffusivity information from (typically small volume) cuttings (Fig. 8). However, assuming that this information can be extracted, it is often not clear how it relates to permeability estimates from large-scale cores or core plugs that are typically only retrieved from verical wells.

11

1000 m+

k5 ,ν 5 , E5 , Pi5 ,σ h5

k4 ,ν 4 , E4 , Pi4 ,σ h4

k3,ν 3, E3, Pi3,σ h3

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k2 ,ν 2 , E2 , Pi2 ,σ h2

k1,ν1, E1, Pi1,σ h1

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Fig. 8. Conceptual diagram showing the variation of properties affecting fluid transport (permeability) and hydraulic fracturing (rock mechanical properties, pressure and stress) along the length of a horizontal well.

Kn =

λ rh

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In order to extract permeability/diffusivity data from cuttings, the compex transport mechanisms that operate at multiple scales within different pore structures must be contended with. Because the different components of shale, simplified into organic and inorganic components, are expected to have different pore structures (Fig. 9), transport processes at multiple scales must be modeled. In the matrix of shales, because of the typically broad pore size distribution, the ability to model multiple flow mechanisms including Darcy (laminar) and Non-Darcy flow (slip-flow, diffusion) is critical. As has been noted in numerous previous publications (Klinkenberg, 1941; Beskok and Karniadakis, 1999; Sakhaee-Pour and Bryant, 2012; Javadpour, 2009; Roy et al., 2003; Civan, 2010), flow mechanisms may be predicted using the Knudsen number: ...................................................................................................................................................................................... (4)

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Where λ is the mean-free path of the gas molecule (a function of pressure, temperature and gas composition) and rh is the hydraulic radius of flow. Referring to Fig. 9, it should be evident that Kn and hence transport mechanism, would be expected to vary according to the pore structures and pore size distributions of the various shale components. For components of shale that have significant adsorption (e.g. kerogen), the effect of the adsorbed phase on hydraulic radius (Fig. 7) must also be taken into account. For multi-phase flow, wettability of the various shale components may also vary, complicating our ability to make predictions using pore-scale models.

Fig. 9. SEM image illustrating various components of a Montney silstone/shale sample including inorganic matter (quartz, illite) and organic matter (bitumen). From Wood et al. (2015).

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Mechanical properties, such as Poisson’s Ratio (ν) and Young’s Modulus (E), may also be inferred from cuttings using rock compositional data (Weedmark, 2014; Spencer and Weedmark, 2015), and possibly through correlations with permeability (Ghanizadeh et al. 2015a) but again, must be calibrated with larger scale rock samples that can be subject to triaxial testing or other methods for direct mechanical property estimation. While cuttings are useful for matrix transport property evaluations, it is difficult extract fracture property estimates from them because the particle size of cuttings may actually be smaller than the scale of fractures. Both propped (i.e. induced fractures containing proppant) and unpropped (i.e. induced or natural fractures not containing proppant) are important for dictating flow to a shale gas well (Fig. 3) and may be estimated using lab-based techniques (on cores or coreplugs) or fieldbased techniques (diagnostic fracture injection test, rate-transient analysis of flowback and long-term production). Importantly, for lab-based methods, it is a challenge to reproduce in-situ stress conditions to which the fractures are subjected. For field-based methods, the dynamics of fracture closure, fluid leakoff/imbibition and fracture property changes during flowback complicate our ability to extract quanititative information about fractures from this flow-period. In order to properly model fracture dynamics, fracture models are employed, but these models require not only rock mechanical properties but also in-situ stress information. A very popular technique to derive in-situ stress (fracture closure pressure, which is related to minimum in-situ stress) information from shales is the diagnostic fracture injection test (DFIT) (Barree et al., 2009). While this test has gained popularity for this purpose in recent years, it can also be used to extract reservoir pressure and permeability information under certain conditions. However, typically operators only execute this test in the toe section of a horizontal well (Fig. 10); a significant challenge will be to make the test short enough and cheap enough to allow the extraction of reservoir pressure, closure pressure, and permeability at multiple points along the horizontal lateral (Fig. 8). This information is critical for not only allowing for better stage-by-stage fracture design, but also for generating forecasts for multi-fractured horizontal wells with significant reservoir property variation along the lateral.

Horizontal Lateral Before Fracture Stimulation

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Fig. 10. Illustration if a DFIT test performed on the toe of a horizontal well prior to the main stimulation treatment.

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Rate-transient analysis methods have recently been adapted to analyze the transient linear period of shales to extract critical reservoir and hydraulic fracture property information from model inversion. However these methods, which utlize analytical solutions mostly derived for conventional reservoirs, must also be adapted to account for the complexities of shale gas transport, including multi-phase flow, non-Darcy flow (slip-flow, diffusion) etc. (Qanbari and Clarkson, 2013a, 2013b, 2014; Behmanesh et al., 2015). Finally, a topic of importance to fluid storage and transport characterization is fluid property modeling. As noted above, PVT property characterization in the lab is potentially in error because pore confinement effects are ignored. A conventional approach for in-situ fluid property estimation is to use fluid production ratios and composition gathered at the surface (i.e. separater data) and recombine fluids to obtain a representative in-situ fluid (Fig. 11). However, for liquid-rich shales, flowing pressures will typically be below dewpoint pressure, causing condensate to drop out in the reservoir, which in turn leads to produced fluid composition changes. Further, as noted by Whitson and Sunjerga (2012), the permeability of the formation and hydraulic fracture properties also affect fluid production (ratios and composition). A solution to this problem is to use compositional numerical simulation to history-match well flowing pressures rates AND fluid composition over time; this enables the extraction of in-situ fluid composition, however it is a complex multi-dimensional problem requiring sophisticated assisted history-matching routines (Hamdi et al., 2015).

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Gas

Separator p = 215 psi T = 150 F

Oil

p = 14.7 psi T = 60 F

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Stock Tank p = 15 psi T = 80 F

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Fig. 11. Illustration of the procedure for obtaining fluid production ratios and compositions gathered at the surface (i.e. separater data) and recombine fluids to obtain a representative in-situ fluid. Image courtesy of Mohammed Kanfar.

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The challenges associated with characterizing properties affecting fluid transport that are discussed in this paper are summarized in Table 2. Table 2 Summary of challenges for characterization of fluid transport mechanisms and production forecasting. Data Source Reservoir samples

Sample Volume Core

Reservoir samples/ DFIT DFIT Flowback/online production data Surface fluid samples, compositional simulation/assisted history-matching

Cuttings/Investigated by pressure transient Investigated by pressure transient Well drainage volume Well drainage volume

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Challenge Estimating matrix and fracture permeability and rock mechanical properties from core Estimating fluid transport properties along a horizontal well Estimating in-situ stresses along a horizontal well Extracting fracture and reservoir properties from production data In-situ fluid property estimation

3. Advances in fluid-in-place estimation for multi-fractured horizontal wells completed in shales

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The focus of this section is on lab-based estimation of reservoir properties affecting fluid-in-place estimation, as well as production-based estimates of fluid-in-place from multi-fractured horizontal wells. A reservoir sample analysis workflow, which includes slabbed core, core plug, core-plug sub-samples and cuttings analysis, is provided below (Fig. 12). Previous work by Ghanizadeh et al. (2015a,b) has discussed the use of this workflow including profile permeability and mechanical harness testing measurements performed on core slabs and plugs to characterize fine-scale permeability and mechanical property variation, helium pycnometry analysis performed on core plugs to derive porosity estimates, pulse-decay measurements to obtain matrix permeability and steady-state permeability measurements performed on cracked core plug samples to obtain estimates of unpropped fracture permeability. The various sample types that are analyzed in this workflow are illustrated in Fig. 13. A focus of those authors in recent work has been the development of methods for characterizing crushed sample properties, with a view to applying these methods on core cuttings (see workflow item “Sub-sampling Plugs & Crushing/Sieving”). Use of these techniques for fluid-in-place estimation from cuttings will be discussed in the following section.

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Helium Pycnometry (plugs) grain density, porosity

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Pulse-decay Permeability, Steady-state Unpropped Frac Permeability (plugs) matrix permeability, fracture (unpropped) permeability: under “in situ” effective stress Sub-sampling Plugs & Crushing/Sieving

Crushed-rock Permeability matrix perm

Petrography grain size,%Ro

Helium Pycnometry grain density, porosity

Low-pressure Gas Adsorption (LPA) PSD, pore volume, surface area

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Wettability SEM XRF

Fig. 12. Reservoir sample analysis workflow used by the Clarkson research group to analyze slabbed core, core plugs, coreplug sub-samples and cuttings.

Core Plugs

Cuttings

Crushed-Rock

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Slabbed Cores

Fig. 13. The various sample types that are analyzed using the workflow shown in Fig. 12. 3.1

Estimating fluid storage properties along the length of a horizontal well from cuttings.

As mentioned above, with multi-fractured horizontal wells, usually the only reservoir samples available for analysis are small volumes of cuttings collected periodically through the vertical section, through the bend in the well, as well as the lateral section, the later samples being most important for characterizing reservoir property changes along the well. The challenge for determination of fluid storage properties such as porosity, fluid saturations, and adsorbed gas content is primarily associated with sample sizes typically collected, which are on the order of only a few grams. Most commercial techniques that have been developed for analysis of rock samples require much larger volumes of rock (coreplug scale or 30+ g of crushed rock samples). Instruments are therefore required that can operate on small samples (1-2 g); because volumetric teachniques are often used for measuring porosity and pore size distributions, and the bases of these techniques is mass balance calculations that require gas pressure measurements, the transducer accuracy and precision must be very high. In our work we have found

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that low-pressure gas adsorption (LPA) equipment used for surface area and pore size distribution estimation is ideally-suited for analyzing cuttings. This is because only small sample sizes are required (1-2 g). Use of this equipment for determining porosity, surface areas and PSDs for simulated cuttings of shale samples is described below. PSDs are a critical component of shale storage estimation, and the readers are referred to the works of Chalmers et al. (2012a), Mastalerz et al. (2012, 2013) and Clarkson et al., (2013) for a review of methods utilized for this purpose. In order to test the use of low-pressure adsorption equipment for this purpose, Duvernay shale core plugs previously used for permeability/porosity measurement (Ghanizadeh et al. 2015c) were crushed down to 20/35 mesh size to create “artificial cuttings”. Geochemical and other petrophsyical (porosity/permeability) analyses were performed on these samples and reported by Ghanizadeh et al. (2015c) (Table 3). 1-2 g splits of these crushed samples were then taken to simulate cuttings. By sub-sampling rock previously tested, the properties derived from core plugs and crushed samples could be compared. The following outlines the use of these small, cuttings-scale samples for the determination of parameters critical for the determination of gas storage including pore size distributions, and adsorbed gas content. Details of this full study are provided by Haghshenas and Clarkson (2016a, in preparation).

TOC Fraction [wt %]

2

7

3244.21

4

3249.36

3.5

5

3251.49

3.6

8 1

3257.06

4.5

Weight Clay [%] content [%]

Bulk fraction 98.43

15

Clay fraction 1.57

47

Bulk fraction 97.41

27

Clay fraction 2.59

45

Bulk fraction 93.93

28

Clay fraction 6.07

60

Bulk fraction 98.74

22

Clay fraction 1.26

47

Quartz feldspars content [%]

+

Carbonate content [%]

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Sample No. Depth [m]

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Table 3 Geochemical and other petrophysical data obtained for Duvernay shale coreplugs used to derive artificial cuttings (crushedrock) samples.

Pyrite [%]

54

25

6

38

25

8

51

17

5

37

17

0

47

16

7

33

17

0

41

33

4

32

33

6

Cleaned/dried1 Grain density [g/cm3]

Total porosity [vol%]

2.61

5.9

2.61

5.7

2.58

5.4

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Dean-Stark extraction was performed using Toluene-Methanol (10 days) to remove residual fluid, and the samples were dried in a vacuum oven at 110oC (10 days).

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3.1.1 Pore size distributions Following the procedures outlined by Haghshenas and Clarkson (2016a, in preparation), the 1-2 g splits were evacuated and dried overnight prior to gathering low pressure adsorption data. CO2 and N2 adsorption isotherm data were then collected at 273 K (ice/water bath) and 77 K (liquid nitrogen at atmospheric pressure), respectively, using the Micromeritics 3Flex™ automated volumetric low-pressure adsorption device. The combination of CO2 and N2 adsorption is used estimate pore volume in the micropore to macropore range (Gregg and Sing, 1982; Clarkson and Bustin, R. M. 1999; Chalmers and Bustin, 2007; Ross and Bustin, 2009; Bustin et al., 2008). Measured CO2 isotherms were interpreted for pore size distribution using Density Functional Theory (DFT), and CO2 isotherms using both DFT and Barrett-Joyner-Halenda (BJH) Theory (Barret et al., 1951). From Fig. 14 we can see that while the PSDs from all the shale samples are reasonably similar in shape (but differ somewhat in the amount of pore volume at each pore size), the pore sizes range from ~ 20 nm (mesopores) to less than 1 nm. It should be noted that in order to convert pore volumes to porosities, the bulk density of the artificial cuttings must be estimated, which was done using the Archimedes method (API, 1998). Because the maximum pore size investigated using LPA is only ~ 100 nm, and the smallest accessible pores for CO2 (kinematic diameter: 0.33 nm; Mehio et al., 2014) and N2 (kinematic diameter: 0.36 nm; Mehio et al., 2014) will be greater than for helium (kinematic diameter: 0.26 nm; Mehio et al., 2014), the total porosity calculated from LPA will typically be less than that derived from a combination of helium-derived grain density and bulk-density. For free-gas storage estimates, it would be important to add the additional pore volume calculated from helium in the larger pore size ranges (> 50 nm). Referring again to Fig. 14, the broad pore size range observed means that the storage mechanisms would be expected to differ substanstially in the pore structure of these shales. Referring to the conceptual models in Fig. 7 created using SLD simulation, pores in the < 2 nm range would be expected to have limited to no free-gas storage and strong gas density gradients from pore wall to center, while pores in the 10 nm range or greater have a distinct bulk phase at the center of the pore. A model that accurately accounts for these storage mechanisms is therefore required to quantify gas storage in the full pore-size spectrum. In recent work (Tolbert et al., 2015), mercury intrusion-derived PSDs combined with the SLD model

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0.01 0.008 0.006

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Fig. 14. Pore size distributions extracted from CO2 and N2 isotherms using DFT and SLD models. From Haghshenas and Clarkson (2016a, in preparation). 3.1.2

Adsorption modeling

n Gibbs =

ASlit 2

L −σ ff / 2 [ρ ( z) ∫ σ ff / 2

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There are a number of analytical, empirical and semi-empirical models that have been used to model high-pressure adsorption in shale – a recent review was provided by Clarkson and Haghshenas (2013). Of the more recent models to be applied to shale, the Simplified Local Density (SLD) model is one of the more promising in terms of its accuracy in correlating adsorption data and its ability to predict adsorption from pore structure/gas-solid interaction information. Gibbs (Excess) adsorption is calculated from the following equation: − ρ bulk ]dz ......................................................................................................................................... (5)

ln f bulk =

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Where, for a slit geometry, the lower limit of integration σff /2 is the location of the center of an adsorbed molecule in contact with the left planar surface, and the upper limit, L- σff /2, is the location of the center of an adsorbed molecule in contact with the right plane surface. z is distance from the slit surface (Fig. 6). The three physical parameters: pore width (L), surface area (ASlit) and fluid–solid interaction energy parameter (εfs/kB), are obtainable through regression of experimental data using the SLD model. The PR-EOS is used to estimate the bulk (ρbulk) and sorbed phase density (ρ(z)) as discussed in Haghshenas and Clarkson (2016a, in preparation) and given below: 1 + (1 + 2)bρ bulk bρ bulk aρ bulk 1 − bρ bulk a − − ln[ ]− ln[ ] ........................................ (6) 2 2 1 − bρ bulk RT (1 + 2bρ bulk − b ρ bulk ) RTρ bulk 2 2bRT 1 + (1 − 2) bρ bulk

ln f ff ( z ) =

bρ ( z )

1 − bρ ( z )

−

a (T , z ) ρ ( z )

RT (1 + 2bρ ( z ) − b ρ ( z ) ) 2

2

− ln[

1 − bρ ( z ) RT ρ ( z )

]−

a( z ) 2 2bRT

ln[

1 + (1 + 2) bρ ( z ) 1 + (1 − 2)bρ ( z )

] ............................................... (7)

An example application of the SLD model for correlating New Albany Shale CH4 and CO2 data collected by Chareonsuppanimit et al. (2012) is shown in Fig. 15.

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20 10 0 5 CO2 Exp. data SLD model

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Fig. 15. Match of SLD model to high pressure CH4 and CO2 Gibbs excess adsorption data. Data from Chareonsuppanimit et al. (2012) for the New Albany shale.

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While the high-pressure adsorption data are required to estimate the adsorbed gas content of shales in-situ (Eq. (1)), it is difficult to obtain such data for small amounts of cuttings. As noted above, commercial devices that utilize the low-pressure adsorption technique for extracting sample surface areas and pore size distributions can generate adsorption isotherms for gases such as N2 and CO2 on small cuttings sample volumes, but at very low pressures (< 1 atmosphere). One solution for deriving high-pressure natural gas isotherms from such data is the use of the SLD model – this model can be used to extract pore width (L) and surface area (ASlit) by fitting Eq. (5) to low-pressure adsorption data, and when combined with an estimate of the fluid–solid interaction energy parameter (εfs/kB) for the gas of interest, can be used to predict hydrocarbon isotherms at high pressure. Following this approach for the artificial Duvernay shale cuttings samples, the first step in the analysis is the correlation of the low-pressure N2 and CO2 data using the SLD model (Fig. 16). Not unexpectedly, the pore sizes and surface areas obtained from the SLD model match of each gas are quite different; for example, the average pore size is 1.1 nm for CO2 (micropores) and 2.9 nm for N2. These values are similar to the results of DFT and BJH model fits for each gas, however.

0

0.1 0.2 Relative pressure (p/p0) CC2 CC4 CC5 CC8 CC2 CC4 CC5 CC8

0.3

N2, model fit to low pressure exp. data

7.E+01

b)

6.E+01 5.E+01 4.E+01 3.E+01 2.E+01 1.E+01 0.E+00 0

0.01 0.02 0.03 Relative pressure (p/p0) CC2 CC4 CC5 CC8 CC2 CC4 CC5 CC8

0.04

Fig. 16. Match of SLD model to adsorption branch of low-pressure CO2 (a) and N2 (b) adsorption isotherms collected for crushed Duvernay shale samples (artificial cuttings). With estimates of ASlit and L from low-pressure adsorption, the high-pressure isotherms can now be predicted (Fig. 17). Both excess and absolute adsorption isotherms are shown – in order to convert from excess to absolute adsorption, the

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ACCEPTED MANUSCRIPT following equation is used: n abs = nGibbs (

ρa ) ........................................................................................................................................................... (8) ρ a − ρ bulk CO2, high pressure prediction

N2, high pressure prediction 200

180

quantity adsorbed (SCF/ton)

180

a)

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b)

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140

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100

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80 60 40 20

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Fig. 17. Use of the SLD model to predict high-pressure, high temperature (328K) N2 (a) and CO2 (b) excess and absolute adsorption isotherms collected for crushed Duvernay shale samples (artificial cuttings).

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Of greater interest for shale gas resource calculations is the hydrocarbon-in-place. Although low-pressure methane isotherms were not measured for the Duvernay artificial cuttings samples, ASlit and L obtained from the low-pressure isotherms measured above, plus a value of εfs/kB for methane may be used to predict the high-pressure methane isotherms using the SLD model. With an estimate of ASlit and L, then εfs/kB can be adjusted to be in the range of isotherm data measured with commercial equipment on larger samples (i.e. from cores with similar TOC and compositional values etc.). A problem arises, however, in the choice of low-pressure gas isotherm for the determination of ASlit and L. Haghshenas and Clarkson (2016a, in preparation) found that ASlit and L from N2 adsorption data provided more reasonable values for εfs/kB compared to CO2 data. The first step in the prediction of high-pressure methane isotherms for the four Duvernay artificial cuttings samples is calibration of the εfs/kB parameter to actual measured isotherms – Fig. 18a illustrates a match of high pressure isotherms obtained on core samples with a similar TOC range (and depth) as the artificial cuttings samples. The εfs/kB is estimated to be 31 (K). The next step is the prediction of high-pressure methane isotherms for the artificial cuttings samples using this calibrated/estimated value of εfs/kB and ASlit /L from low-pressure N2 data. This is illustrated in Fig. 18b. The Haghshenas and Clarkson study demonstrates that it is possible to predict high-pressure methane isotherms reasonably using small masses of cuttings samples. CH4, model fit to high pressure exp. data

CH4, high pressure prediction quantity adsorbed (SCF/ton)

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b)

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3.1.3 Fluid property modeling. The above discussion has focused on single-phase (gas) storage properties of shale gas reservoirs. As noted previously, in North America, liquids-rich shale plays, which include shale gas-condensate reservoirs, are currently being exploited. As noted by several authors (Altman et al., 2014), wells producing from such reservoirs often exhibit anomalous fluid production characteristics, such as long periods of constant (and small) condensate-gas ratios for wells (or conversely, high gas-oil ratios, see Fig. 19a) that are apparently relatively rich condensate reservoirs as determined from conventional PVT testing. This behavior is in contrast with the general expectation for gas condensate reservoirs: an increasing trend of GOR over time. In conventional gas condensate reservoirs, as the pressure drops below the dewpoint pressure during isothermal production, the first liquid condensate droplets are formed, but the accumulated condensate does not move in the reservoir until it reaches the critical condensate saturation. After a significant condensate liquid saturation builds up, some of the condensate liquid begins to flow towards the producing well (Fan, 2005); this causes a decrease in gas saturation, gas relative permeability and flow rate (well productivity). Precipitation of condensate in the reservoir also leads to leaner gas production because some of the heavier hydrocarbon components are left in the reservoir. With these mechanisms in mind, the well productivity decline in shale gas condensate wells may be expected to be severe because pressure drawdown gradients are large (due to low permeability). However, a number of shale gas condensate wells have produced for long periods of time in North America, suggesting that the productivity of these wells may not be dramatically declining (Altman et al., 2014). This observation, in addition to long term constant GOR production (Fig. 19a) in some North America gas condensate shale wells, highlights the need for more detailed rock/fluid study for liquids-rich (gas condensate) (LRS) shale plays. The anomalous production characteristics of LRS plays have been variously attributed to flow geometry, low permeability, drawdown and pore confinement effects. The influence of pore confinement will be discussed in this section. A problem with conventional PVT testing, such as a constant volume depletion (CVD) or constant composition expansion (CCE) test is that the properties of the fluid are determined in large cells and hence the influence of pore walls is not taken into account. As with gas content estimation discussed above, the influence of pore confinement on fluid properties can be explored using the SLD model calibrated to pore structure information obtained from cuttings. Once the pore size distribution is determined, then the effect of pore confinement can be simulated by combining the SLD model with an equation-of-state, such as the Peng-Robinson EOS (Rangarajan et al., 1995; Chen, 1997; Fitzgerald et al., 2006; Mohammad et al., 2009). The SLD model can be used to estimate fluid density gradients from pore wall to center, and therefore explicitly considers pore geometry in adsorption modeling. As illustrated previously (Fig. 7), the SLD model predicts that fluid densities near pore walls are greater than the bulk phase density of the same fluid. This modeled density is further used to adjust the critical properties and phase envelope of the confined fluid. An example of such a calculation for a gas condensate fluid system is shown in Fig. 19b. In this figure the SLD model was used to predict the phase envelope for a gas condensate system as a function of pore size, from 300 nm to 2 nm. We see that the phase enveloped migrates from higher saturation pressures/temperatures to lower values and correspondingly, predicts a later onset for condensate dropout in shale reservoirs than for bulk systems. This is consistent with the reported “dewpoint suppression” behavior that others have reported for liquid-rich shale systems (Singh, 2011; Devegowda, 2012; Ma and Jamili, 2014; Didar and Akkutlu, 2015; Pitakbunkate, 2015), and in part explains the appearance of leaner fluid production than expected for these systems. The picture is further complicated when considering the fact that the lower mobility of oil than gas in nanopores causes changes in the flowing fluid composition along the transport path (Xiong et al., 2013); the desorption of heavier components at pressures below critical desorption pressure also contributes to the change in the fluid composition.

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ACCEPTED MANUSCRIPT Monthly GOR behavior of gas condensate wells in Eagle Ford shale. Modified after Altman et al., 2014

90

a)

70 60 50 40 30 20 10 0 5

10

15

20

Well 27

Well 28

Well 29

45

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Well 30

phase envelop of a gas-condensate fluid under confinement

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pore width=10nm pore width=2nm

EP

pore width=300nm pore width=5nm

600

AC C

Fig. 19. (a) Long-term constant GOR observed for producing liquids-rich shale wells and (b) use of the SLD model to predict the phase envelope for a gas condensate system as a function of pore size, from 300 nm to 2 nm. Experimental investigations into the effects of pore-confinement on fluid properties are currently underway to verify model predictions such as those shown in Fig. 19. Examples of such work include Zarragoicoechea and Kuz (2002, 2004), Singh et al. (2009), Hamda et al. (2007) and Jiang et al. (2005). In Section 4, additional causes for anomalous fluid production characteristics will be discussed, and a method for fluid model calibration using combined assisted history-matching and compositional history-matching will be demonstrated. 3.2

FIP calculations: Accounting for the effects of pore confinement.

As noted in Section 2, the common approach for shale gas-in-place calculations is to assume that adsorbed and free gas components can be assessed independently in shale and then summed to obtain total gas-in-place. However, as further noted, in shale organic matter containing nanopores, a density gradient for gases occurs from pore wall to pore center, with the gas density approaching the bulk-phase density at the center of the pore for larger pores. While the SLD model may be used to rigorously estimate gas storage as a function of pore size (accounting for fluid density gradients in the pore space), the most common method for gas-in-place estimation is to define and measure adsorbed and free-gas storage separately and add them together to get total gas content (Eq. (1)). However as noted above, it may be difficult to separate out free and adsorbed

21

ACCEPTED MANUSCRIPT phases in certain shale pore size ranges. Ambrose et al. (2012) suggested that if most gas storage occurs in shale kerogen, then the free-gas storage calculation (Eq. (2)) will be affected by the volume occupied by the sorbed phase (see Fig. 7 for a visual). They therefore modified the conventional free gas storage term to account for the pore space occupied by the sorbed phase, using the Langmuir equation to calculate the sorbed phase volume at standard conditions and converting this to a volume at reservoir condition assuming a sorbed-phase density. Ambrose et al. (2012) assumed the sorbed phase volume to be a non-effective porosity (i.e. a fraction of bulk volume) and derived the following formula for calculating standard cubic feet of in-place free gas:

RI PT

G f ,i

VL Pi   ρb   Ah 1 ( PL + Pi ) φ( 1 − S wi ) −  .......................................................................................................................... (9) = Bgi  379.48 ρ ai     

SC

Where ρa is adsorbed phase density (g/cm3). Note that the second term within the brackets is the sorbed phase porosity fraction that is subtracted from the free gas porosity fraction to provide a corrected free gas volume. Haghshenas et al. (2014a) recognized that, because the sorbed phase is a fluid phase, not a solid grain or isolated void space, it would be more realistic to treat the sorbed phase as a fluid saturation (i.e., a fraction of pore volume). In other words, the sorbed phase saturation occupies a portion of the available (effective) void volume in exactly the same way as the other fluid components within the void space (i.e. water, gas etc). Therefore, the free gas-in-place expression is corrected as given below: Ahφ ( 1 − S wi − S ai ) ....................................................................................................................................................... (10) Bgi

M AN U

G f ,i =

Where, Sai is introduced here as adsorbed phase saturation (fraction): S ai =

ρb VL Pi ..................................................................................................................................................... (11) 379.48 ρ aiφ ( PL + Pi ) 1

L − σ ff

.............................................................................................................................................................. (12)

EP

ρa =

L −σ ff / 2 [ ρ ( z ) ]dz ∫ σ ff / 2

TE D

Eq. (10) can then be used instead of Eq. (2) for calculation of initial free gas-in-place. In Eq. (11), the sorbed phase density must be estimated – for methane, this density is often assumed to be the boiling point density, but it can be calculated more rigorously using molecular simulation or the Simplified Local Density model. Using the SLD model, for example, the average sorbed phase density at a given pressure may be estimated as follows:

AC C

Haghshenas et al. (2014 a,b) demonstrated the importance of these corrections using numerical simulation. Volumetric calculations using Eq. (1) may be performed using rock data collected along the length of the horizontal well, as described in the previous section; further, log data (usually from vertical wells – see Fig. 1), calibrated to core data, may be used to build a petrophysical model which in turn can be used to estimate OGIP in between wells. While volumetric gas-inplace calculations are relatively straight-forward, even when accounting for the effects of sorbed phase volume, dynamic gasin-place calculations (as derived from production data using static or dynamic material balance calculations) are not because the sorbed phase density is a function of pressure and the sorbed phase saturation will also change with pressure. Further, dynamic estimates are usually performed using commingled (all hydraulic fracture stages flowing) production data, and therefore estimates of GIP in each stage are generally not possible with this technique. 3.2.1 Static material balance calculations Eq. (1) may be used to estimate gas-in-place volumetrically given an estimate of gas storage (previous section) and volume of the reservoir. A common reservoir engineering method for estimating gas-in-place contacted by wells from cumulative production data and reservoir pressure is static material balance, where “static” refers to the use of shut-in pressures in the analysis. It is well-known that it is difficult to obtain accurate estimates of reservoir pressure from shutting in shale wells because of the exceedingly long time required to obtain a stabilized reservoir pressure. However, material balance calculations are a critical component of “dynamic” production analysis methods (where “dynamic” refers to the use of flowing pressures instead of shut-in pressures) and therefore it is instructive to discuss how these techniques must be modified to

22

ACCEPTED MANUSCRIPT account for the unique aspects of shale gas storage discussed above. There have been numerous material balance equations that have been proposed to account for shale storage mechanisms such as adsorption (King, 1993; Clarkson and McGovern, 2001; Ahmed et al., 2006, Ambrose et al., 2012; Moghadam et al., 2011; Williams-Kovacs et al., 2012; Thararoop et al., 2015); one example, developed initially for coalbed methane reservoirs with significant free-gas storage as well as adsorbed gas storage, is the Clarkson and McGovern (2001) equation, which assumes a volumetric reservoir:

RI PT

 Pi  − Gp P 1 1 + [φ( 1 − Swi )] = + + [φ( 1 − Swi )] ........................................................................ (13) PL + P Bg ρbVL AhρbVL  PL + Pi Bg ρbVL  This equation was developed using Eqs. (1-3) for gas-in-calculations, and hence does not account for free-gas pore volume change due to adsorption. Only recently has the effect of adsorption on the free-gas storage calculation been considered in material balance calculations. The first attempt was by Williams-Kovacs et al. (2012) who used the Ambrose et al. (2012) approach to adjust the free-gas storage term and modified the Clarkson and McGovern equation as follows:

M AN U

SC

VL P   ρb   P 1 ( PL + P ) 1 φ ( 1 − S wi ) − = + ρ ai PL + P Bg ρ bVL  379.48    ..................................................................................... (14)  VL Pi   ρb    − Gp ( PL + Pi )   1 1  Pi φ ( 1 − S wi ) −  + + Ahρ bVL  PL + Pi Bgi ρ bVL  379.48 ρ ai      

φ ( 1 − S wi ) −

VL Pi ρb ( PL + Pi )

379.48

φ ( 1 − S wi )

ρ ai

.................................................................................................................................... (15)

EP

φc = φ

1

TE D

In order to test the use of this equation using numerical simulation, Williams-Kovacs et al. (2012) suggested that commercial simulators must be “tricked” to account for a change in porosity due to adsorbed phase volume – they achieved this by noting that some simulators include pore volume multipliers as a function of pressure in the form of lookup tables, and used the following equation to modify porosity for the sorbed phase volume:

AC C

In Eq. (14) and (15), the free-gas storage term is dynamically adjusted to account for the adorbed phase, however, as noted by Haghshenas et al. (2014a), the adsorbed phase density is assumed to be a static property, which is clearly not the case (see Eq. (12)). Hagshenas et al. (2014a) used their modified free-gas storage term (Eq. (10)) to further modify the Clarkson and McGovern equation as follows:

 − Gp P 1 1  Pi + [φ(1 − Swi − Sa )] = + + [φ( 1 − Swi − Sai )] ............................................................... (16) PL + P Bg ρbVL AhρbVL  PL + Pi Bgi  Which includes the sorbed-phase saturation term, calculated to be a function of pressure. In order to apply this equation, the P 1 left-hand side { + [φ (1 − S wi − S a )] } is plotted versus G p , resulting in a straight line with slope −1 . Ahρ bVL P + PL B g ρ bVL OGIP can be calculated from the x-intercept, and reservoir bulk volume from the slope. With an estimate of thickness (h) from well logs, drainage area (A) may also be calculated. In Eq. (16), the effective pore volume within the rock matrix is not only occupied by water and free gas but also by the adsorbed gas. As pressure decreases the sorbed phase vaporizes and makes a vacant space that is instantly occupied by free gas. In order to test Eq. (16), Haghshenas et al. (2014a) also used reservoir simulation combined with pore volume multipliers

23

ACCEPTED MANUSCRIPT to account for free-gas porosity changes with adsorption. The porosity correction factor in this case is as follows:

φc φ (1 − S wi − S a ) = ............................................................................................................................................................... (17) φ φ (1 − S wi )

in-place calculation

porosity correction factor- constant ρs vs. variable ρs

4.0 y by different methods

0.90 0.89

b)

3.5 3.0 2.5 2.0

M AN U

porosity correction factor

a) 0.91

SC

0.92

RI PT

Because sorbed phase saturation (Sa) is a positive value, the porosity multiplier is always a quantity smaller than one. Further, as Sa gets larger (e.g. at higher pressures or heavier gas molecules), the porosity multiplier shifts more from unity. An important consideration in Eqs. (16-17) is the effect of sorbed-phase density. Fig. 20a, from Haghshenas et al. (2014a), illustrates the use of Eq. (17) for constant and variable sorbed phase density, the latter being calculated using the SLD model with Eq. (10). Fig. 20b similarly illustrates the use of Eq. (16) for calculating OGIP – from this plot we see that the impact of variable sorbed-phase density can be significant.

0.88 0.87

1.5 1.0 0.5 0.0

0.86 0

1000

2000 3000 4000 Gp (MMSCF)

constant ρs

5000

6000

variable ρs

0

1000

2000 3000 4000 Gp (MMSCF)

corrected-variable ρs conventional-variable ρs conventional-constant ρs

5000

6000

in-place extrapolation in-place extrapolation in-place extrapolation

TE D

Fig. 20. Plot of (a) Eq. (17) vs cumulative production and (b) left-hand side of Eq. (16) vs cumulative production, generated using numerical simulation. Considering the variable sorbed phase density with production causes the correction factor to approach a value of one with increased production (a correction factor equal to one means whole pore volume is available for free gas and the sorbed gas has zero volume). As pressure decreases during production, the sorbed phase evaporates and allows more of the pore volume to be occupied with free gas. Modified from Haghshenas et al. (2014a).

 P/ Z   Pi / Z i

*

AC C

EP

While Eq. (16) accounts for free-gas storage volume changes due to adsorption, and desorption, it assumes a volumetric reservoir (no change in fracture porosity, no water influx etc.). Recently, a general equation Eq. (18) was developed by the Haghshenas and Clarkson (2016b, in preparation) that can be applied to both dual porosity coalbed methane and shale reservoirs. The new equation accounts for water encroachment/production, expansion of formation and residual liquids in overpressured reservoirs in the matrix and fractures, gas desorption and matrix shrinkage and swelling effects, the latter being important for some CBM reservoirs. As with Eq. (16), the non-zero volume of the adsorbed phase in the matrix pore volume is accounted for with an adsorption phase saturation term. Variation in adsorbed phase thickness is accounted for by introducing adsorbed phase compressibility into the material balance equation. An important aspect of the new equation is that it is presented in a simple and familiar P/Z form and the straight line plot simultaneously gives G f ,i and Gt ,i .

 1  = 1 − Gp G f ,i 

......................................................................................................................................... (18)

Where

 P/ Z   Pi / Z i

*

Bgi   P / Z  =  1 + [( 1 − ω )Cm ,eq + ωC f ,eq ] ∆P − ρ bi φi S gi   Pi / Z i

(

)

 VL Pi V P  ( We − W p Bw )5.615   − L  −  ...................... (19) Bg G f ,i   PL + Pi PL + P 

24

ACCEPTED MANUSCRIPT All of these terms are defined in the nomenclature section. The full derivation and application procedure are provided in Haghshenas and Clarkson (2016b, in preparation). Fig. 21 provides and illustration of how to apply the new material balance equation to production data.

2

RI PT

0

-1

-2

[(p/z)/(pi/zi)]*min

-3 1

Gfree,i

2

3 Gp

4

5

Gt,i

6

M AN U

0

SC

[(p/z)/(pi/zi)]*

1

Fitted line

Production data

Fig. 21. Illustration of how to use new straight line method for calculation of free- and total-gas-in-place. Modified from Haghshenas and Clarkson (2016b, in preparation).

TE D

3.2.2 Dynamic (flowing) material balance calculations As noted above, because representative reservoir pressures are difficult to obtain, a preferred method for estimating OGIP from producing wells is the (dynamic) flowing material balance method (Agarwal et al., 1999; Mattar and Anderson, 2005). However, to get an estimate of OGIP from this method, the well must be producing during boundary-dominated flow. If the well is still in transient flow, some (e.g. Nobakht, 2014) have suggested that the technique can be used to estimate contacted gas-in-place (CGIP), which is an estimate of how much gas volume the well has contacted until the end of its producing period. A form of the flowing material balance (FMB) equation that can be used for analyzing shale gas reservoirs is as follows (Morad and Clarkson, 2008):

()

( )

()

AC C

( )

EP

m(Pi ) − m P q 1 1 = − G ............................................................................................................................... (20) m(Pi ) − m Pwf b pss b pss G m(Pi ) − m Pwf

Where q is gas rate, m(Pi), m P and m(Pwf) are pseudopressures at initial pressure, average reservoir pressure and flowing pressure, respectively, bpss is the inverse of productivity index (PI) and G is original gas-in-place. A material balance equation (MBE) is required to evaluate average pressure P , and hence, as noted by Williams-Kovacs et al. (2012), the results of FMB analysis are dependent on which MBE is selected. It should be noted that for gas reservoirs, FMB analysis is iterative as both the data and the model are functions of OGIP. There are two issues with Eq. (20) as it has been conventionally applied to shale gas reservoirs. The first is that the effect of the adsorbed layer (and it’s affect on free gas pore volume during production) has not been considered. Williams-Kovacs et al. (2012) investigated this by employing the modified version of the Clarkson and McGovern equation (Eq. (14)) in the previous section) for calculating average pressure in Eq. (20). In order to test this version of the FMB, Williams-Kovacs et al. (2012) used a numerical simulation model, combined with the pore volume multiplier in Eq. (15), and generated gas production profiles for three different shale gas cases. They then compared applications of Eq. (20) to the simulated data using three different MBEs (the original Clarkson and McGovern, Eq. (13); a conventional gas reservoir MBE; King’s MBE (King, 1993); and the modified Clarkson and McGovern MBE, Eq. (14)) for three different combinations of shale gas input

()

25

ACCEPTED MANUSCRIPT parameters. An example comparison is shown in Fig. 22. In Fig. 22, “Normalized Rate” is the term (20) and “Normalized Cumulative Production” is the term

()

q m( pi ) − m p wf

( ) in Eq.

m ( pi ) − m p G . It can be seen that the corrected MBE (for pore m( pi ) − m pwf

( )

volume change with adsorption) better matches the simulation case analyzed. It should be noted that the more rigorous MBEs provided in the previous section (Eq. (16) and Eq. (18)) can similarly be used for FMB calculations. b)

OGIP = 5,117 MMscf A = 56 Acres Under-Estimate

4.E-03 3.E-03 2.E-03 1.E-03 0.E+00 0

5.E-03

3.E-03 2.E-03 1.E-03 0.E+00

1000 2000 3000 4000 5000 6000 7000 8000

0

1000 2000 3000 4000 5000 6000 7000 8000

M AN U

Normalized Cumulative Production, MMscf

Clarkson and McGovern Flowing Material Balance

OGIP = 5,833 MMscf A = 50 Acres Under-Estimate

4.E-03 3.E-03 2.E-03 1.E-03 0.E+00 0

1000 2000 3000 4000 5000 6000 7000 8000 Normalized Cumulative Production, MMscf

Corrected Flowing Material Balance

d)

Normalized Rate, scf/D/psi2/cp

5.E-03

TE D

Normalized Rate, scf/D/psi2/cp

OGIP = 5,833 MMscf A = 50 Acres Under-Estimate

4.E-03

Normalized Cumulative Production, MMscf

c)

RI PT

5.E-03

King Flowing Material Balance

SC

Conventional Flowing Material Balance Normalized Rate, scf/D/psi2/cp

Normalized Rate, scf/D/psi2/cp

a)

5.E-03 4.E-03

OGIP = 5,991 MMscf A = 57 Acres Accurate Estimate

3.E-03 2.E-03 1.E-03

0.E+00 0

1000 2000 3000 4000 5000 6000 7000 8000 Normalized Cumulative Production, MMscf

AC C

EP

Fig. 22. Comparison of the use of Eq. (20) for four different material balance equations: a) Conventional gas MBE (“p/z cum. Plot”; b) King MBE; c) Clarkson and McGovern MBE (Eq. (13)); d) Corrected Clarkson and McGovern MBE (Eq. (14)). The expected values for OGIP and drainage area for the simulated shale are 6,003 MMscf and 57 acres, respectively. Modified from Williams-Kovacs et al. (2012). A second issue arises with the application of Eq. (20) to shales during transient flow to derive CGIP. Nobakht (2014) noted that conventional FMB analysis, which was developed for analyzing boundary-dominated flow to derive OGIP, requires a pore volume average reservoir pressure for analysis. However for shales with low-permeability during transient flow, this is difficult to obtain; Nobakht (2014) argued that the appropriate average pressure to use during transient flow is not pore volume average pressure for the entire reservoir (as for boundary-domated flow analysis), but average pressure in the area of investigation (region of influence). Re-writing normalized cumulative production in terms of material balance pseudo-time yields: Normalized Cumulative Production =

2qtca Pi

[µ g Zct ][m( pi ) − m( pwf )] .............................................................................................. (21)

Where tca is material balance pseudotime which is defined as:

26

tca =

(µ g ct )i qg

ACCEPTED MANUSCRIPT t

q g dti

0

µ g ct

∫

............................................................................................................................................................. (22)

Where µ g and ct are gas viscosity and total compressibility, which are traditionally evaluated at average reservoir pressure for the entire reservoir. Nobakht (2014) demonstrated that for wells producing during the transient linear flow period (for the case of constant pressure production, the average pressure in the distance of investigation is constant, and evaluation of material balance pseudotime with this pressure results in the following definition of normalized cumulative production:

2 Pi S gi

[µ g Zct ]i [m( pi ) − m( pwf )] G p f cp .................................................................................. (23) 2

RI PT

Normalized Cumulative Production =

Where Gp is cumulative gas production, and fcp is a correction factor, calculated as follows:

(µ g ct )i

f cp =

µ g ct

......................................................................................................................................................................... (24)

SC

Where µ g and ct are now evaluated at average reservoir pressure in the distance of investigation. The importance of this

M AN U

correction was again demonstrated by Nobakht (2014) using numerical simulation, an example of which is given in Fig. 23. The plot in Fig. 23a illustrates use of the FMB plot using Eq. (21) while Fig. 23b illustrates the use of Eq. (23) – the difference is about 2 bcf, demonstrating that the error could be quite significant. Nobakht (2014) also used this correction factor to correct the square-root of time plot, used to analyze transient linear flow, for the effects of drawdown, non-Darcy (slip flow and diffusion) and stress-dependent permeability; more recently, Qanbari and Clarkson (2013a) and Behmanesh et al. (2015) derived a correction factor that can be used to account for multi-phase flow effects in tight gas condensate systems. 0.025

0.025

a)

b)

6

2

Normalized Rate, MMscfd/10 psi /cP

0.01

0.005

0 0

2

4

EP

TE D

0.015

6

Normalized Cumulative Production, Bscf

Normalized Rate, (MMscf/D)/(106 psi2 /cP)

0.02

0.02

0.015

0.01

0.005

0 8

10

0

2

4

6

8

10

12

Normalized Cumulative Production, Bscf

AC C

Fig. 23. Use of (a) standard FMB plot (Eq. (21)) and (b) corrected FMB plot (Eq. (23)) for analyzing a simulated shale gas well. The corrected plot provides an OGIP estimate that is closer to the actual simulator input. As noted previously, the current paper is focused primarily on dry gas (low molecular weight hydrocarbon) shale reservoirs. For completeness, it should be noted that dynamic material balance methods have been developed for tight gas condensate systems by Heidari Sureshjani et al. (2014) and Behmanesh (2015). The reader is refered to those works for multiphase systems.

4. Advances in well-deliverability estimation for multi-fractured horizontal wells completed in shales In Section 3, both lab- and production-based techniques were discussed for estimating fluid-in-place for multi-fractured horizontal wells. In this section, lab- and field-based techniques will again be discussed for estimating well deliverability, but with the addition of well-test methodologies (DFITs). Again, consideration of the complexities of shale properties (in this case transport properties) will be the focus. For production analysis, a workflow was previously provided by Clarkson (2013b) in a teaching aid paper, and is reproduced in Fig. 24. In Section 3, straight-line analysis (static and dynamic material balance) were discussed for the purpose of estimating fluid-in-place. In this section, straight-line methods for extracting well productivity information, and adjusted to

27

ACCEPTED MANUSCRIPT account for the unique properties of shale gas reservoirs, will be discussed. Further, numerical simulation techniques, for the purpose of extracting reservoir, hydraulic fracture and in-situ fluid properties will be reviewed.

STEP 1:

ASSESS DATA VIABILITY Gather Reservoir, Completion and PVT Data

Review Production Data

STEP 2:

CHECK FOR DATA CORRELATION

STEP 3:

PRELIMINARY DIAGNOSIS

RI PT

Review Well History

Review/Edit Data

SC

Filter Data for Clarity

PERFORM STRAIGHTLINE ANALYSIS

STEP 5: Obtain Preliminary Estimate of Hydraulic Fracture Properties

Validate Reservoir Permeability Estimate

TE D

Validate Hydraulic Fracture Property Estimates

Obtain Preliminary Estimate of Hydrocarbons-in-Place

Validate Hydrocarbonsin-Place Estimate

PERFORM FORECAST WITH MODEL

EP

STEP 8:

Obtain Preliminary Estimate of Reservoir Permeability

PERFORM TYPE-CURVE ANALYSIS

STEP 6:

STEP 7:

M AN U

IDENTIFY FLOW REGIMES

STEP 4:

FIT EMPIRICAL MODEL TO FORECAST

AC C

Fig. 24. Workflow used for rate-transient analysis. From Clarkson (2013b). Prior to discussing commingled production analysis techniques, methods to extract permeability and rock mechanical property data from lab-based techniques will be reviewed. An emphasis will be placed on obtaining such information from cuttings, but cuttings analysis must first be confirmed using more classic core-analysis techniques. Methods for obtaining matrix and fracture permeability and rock mechanical properties from core will first be discussed, followed by estimation of matrix and rock-mechanical properties from cuttings. Rock-fluid interaction, which affects both fluid-distribution and flow, will be reviewed, with a special emphasis on characterizating these effects at the micro-scale. Finally, although not a labbased technique, the possibility of using DFIT analysis for providing stage-by-stage stress information in multi-fractured horizontal wells will be addressed. 4.1

Estimating matrix and fracture permeability and rock mechanical properties from core.

Prior to discussing estimation of matrix permeability and rock mechanical properties from cuttings, issues around the estimation of these parameters from core must first be addressed. Further, because it is now believed that fractures (natural and induced) are very important conduits for production to shale wells (Fig. 3), estimation of fracture permeability from cores will also be discussed in this section.

28

ACCEPTED MANUSCRIPT

4.1.1

Matrix permeability from core

M AN U

SC

RI PT

Referring to the core analysis workflow (Fig. 12), three different techniques for estimating permeability from cores are referred to including pressure-decay profile permeability on slabbed cores and core plugs (run at ambient conditions), pulsedecay permeability on core plugs subject to confining pressure, and crushed-rock permeability measurements (also run at ambient conditions). Each of these techniques use different physical principals and are run on samples subject to variable conditions (applied stress etc.). In order to provide a direct comparison of the various methods for Montney Fm tight gas/shale samples, Ghanizadeh et al. (2015b) performed profile permeability measurements on core plug ends (Fig. 25a), pulse-decay permeability measurements on the same plugs, and then crushed the plugs to perform crushed rock measurements, the results of which are shown in Fig. 25b. Following the procedures of Clarkson et al. (2012b), the profile permeability measurements were corrected to in-situ stress conditions; after correction these measurements compared favorably to the pulse-decay measurements, while the crushed-rock permeability measurements were significantly different than the core-plug measurements. There are several possible reasons for the difference between core plug/crushed-rock measurements, the most likely including 1) influence of sample geometry and 2) existence of stress-induced microfractures. Handwerger et al. (2011) noted that stress-release-induced microfractures in shale core plug samples may cause the permeabilities to be artificially high, even after being subject to stress, while Cui et al. (2013) noted the influence of flow geometry on the measurements – for core plug measurements measured parallel to laminations, which is the case in the Ghanizadeh et al. (2015b) study, the measurements provide an arithmetic average permeability of this layered system, whereas for crushed-rock analysis, the flow geometry provides more of a geometric average permeability. For the core plug measurements made parallel to bedding, the arithmetic average would be expected to be greater than geometric average permeability, even if stress-induced fractures were not present. Also affecting the results is the fact that crushed-rock permeabilities were measured at ambient conditions (no external stress) and lower pore pressures, both of which could lead to artificially high permeabilities due to the effects of stress and non-Darcy flow, which means that geometric effects could actually lead to even greater differences. Systematic studies of this nature should continue into the future so that the cause of permeability measurement differences for different shale lithologies can be better understood and predicted. At of the time of writing, no concensus has been reached by researchers on the best method to be used for estimating shale matrix permeabilities. It is important to note that, in the cuttings analysis discussed in the next section, the results should be compared directly with crushed-rock methods for matrix permeability assessment. 1.E-01

TE D b)

1.E-02

rp35 = 100 nm rp35 = 50 nm

1.E-03

rp35 = 25 nm rp35 = 10 nm

1.E-04

rp35 = 5 nm

AC C

EP

Gas (He, N2) permeability (mD)

a)

• rp35 calculations were performed using a modified Winland-style approach Di and Jensen (2015).

1.E-05

rp35 = 2.5 nm

1.E-06

Montney: Profile Perm, Uncorrected for in-situ stress

1.E-07

Montney: Profile Perm, Corrected for in-situ stress

1.E-08

Montney: Pulse-decay Perm Montney: Crushed-rock Perm

1.E-09 0

2

4

6

8

10

Helium porosity (%)

Fig. 25. (a) Typical core-plug used for profile permeability (see green spot on core) and pulse-decay permeability measurement. (b) Comparison between various permeability measurement techniques. Lines are rp35 correlations based on Di and Jensen (2015) method. Modified from Ghanizadeh et al. (2015b). Also shown in Fig. 25b are permeability-porosity correlations using the Winland-style approach of Di and Jensen (2015). Such approaches are desirable so that more routine and less-costly measurements, such as mercury-intrusion, may be used to

29

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predict permeability, given porosity. As noted by Clarkson et al. (2012b) and others, these correlations are useful for evaluating “flow units”, which are determined by pore throat size, to allow targeting of specific intervals for stimulation. Di and Jensen (2015) noted, however, that for low permeability rock, existing correlations such as those proposed by Aguilera (2002) and Pittman (1992), may be in error. Di and Jensen (2015) proposed a model whose r35 values are more consistent with mercury-intrusion and low-pressure adsorption than Aguilera’s or Pittman’s method, which usually underestimate or overestimate (respectively) r35 by a factor of 2 or more in the smaller pore size ranges seen in unconventional reservoirs. The r35 correlations of Di and Jensen (2015) are therefore used in the correlation of the data in Fig. 24b. It can be seen that the dominant pore diameter controlling flow, based on pulse-decay and corrected profile permeability data, is 10 – 100 nm, whereas the crushed-rock data suggests pore sizes of 5 – 10 nm. An alternative approach for determination of effective pore throat size is to use steady-state and/or pulse-decay gas permeability techniques combined with the Klinkenberg approach (based on gas slippage theory), to estimate the effective pore width of core plug samples under in-situ effective stress conditions (Letham and Bustin, 2015; Ghanizadeh et al., 2015d). The Klinkenberg equation is (Klinkenberg, 1941):

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Where kB is the Boltzmann constant, δ is the gas collision diameter, T is temperature. Noting that (Letham and Bustin, 2015; Ghanizadeh et al., 2015d):

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then the effective (pore throat) diameter to flow can be estimated. This procedure was applied to selected Montney tight gas/shale samples using (N2) pulse-decay permeability tests (Fig. 26). Additional data included in the plot are that of Heller et al. (2014) collected for various American shales and that of Ghanizadeh et al. (2015d) for potential European shale oil/gas reservoirs. Noting that the pore width = 2 x pore throat radius, the estimated effective pore (throat) radii from this procedure (~ 12 nm to ~ 58 nm) are generally in agreement with those obtained from the r35 correlations (Di and Jensen, 2015) and lowpressure gas adsorption tests.

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Indirect methods for permeability estimation from core can also be obtained by combining pore-scale imaging with porescale modeling (Wildenschild and Sheppard, 2013; Fatt, 1956; Blunt et al., 2013). A particular challenge for this approach, when applied to shales, is that the imaging method must be able to resolve pores down to the nano-pore scale to evaluate pore connectivity and flow at that scale. Although CT-scan imaging has proven useful for imaging micron- and large-scale pores for conventional reservoirs, it is unable to resolve pores or pore connectivity necessary for extraction of a pore network from shales or other tight rock with sub-micron scale pore structure. A further challenge is up-scaling the result to core-scale in order to estimate permeability for comparison to actual measured values (using pulse-decay etc.). An example analysis of a low-permeability Cardium sample is provided. For pore-scale modeling, back-scattered electron (BSE) images of the Cardium Formation sample were acquired using an FEI Quanta 250 FEG variable-pressure and environmental field emission scanning electron microscope (VP-E-FESEM). The sample was impregnated with epoxy and finely polished. Taking advantage of cutting edge software, large-area images (3.5 cm × 1.5 cm) were acquired at high imaging resolutions (< 250 nm), see Fig. 27. This large scale mapping (on the order of core-scale) allows for proper classification of the heterogeneity of the rock in two dimensions. From the 2D images a pore network is extracted. Simple thresholding of the pore space extracts all the open porosity. However, to accurately model the flow network, only the connected porosity is needed. The connected porosity is extracted by using the large open pores as seed-makers, and applying grayscale morphological reconstruction to extract the connecting pore throats. A modified maximum circle algorithm is applied to the binarized image to extract the 2D pore network. Analyzing the binarized image in combination with the 2D pore network allows for the extraction of the rock characteristics, such as pore size distribution, grain size distributions, coordination numbers, etc. This information is needed to properly build a stick-and-ball network model, work that is currently being done. We note that the image collected in Fig. 27 required 6.5 GB of storage space!

Fig. 27. (a) Back-scattered electron (BSE) images of a Cardium sample were acquired using an FEI Quanta 250 FEG variablepressure and environmental field emission scanning electron microscope (VP-E-FESEM). The sample was impregnated with epoxy, finely polished, and mounted on a slide. Taking advantage of cutting edge software, large-area images (3.5 cm × 1.5 cm) were acquired at high imaging resolutions (< 250 nm). The superimposed image, taken from the area within the orange box, illustrates the high spatial resolution. An additional example of the use of BSE for visualizing pore structure for network extraction is provided in Fig. 28. In this example, a sample from a low-permeability Cardium Formation sample was analyzed using Focused Ion Beam (FIB) Scanning Electron Microscope (SEM). Stripe artifacts created during the ion-milling procedure were removed using combined wavelet-Fourier filtering (Munch et al., 2009) and the images were registered to remove acquisition drift.

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Fig. 28. (a) Representative 2D image slice of 3D volume showing the acquisition artifacts. (b) Stripe artifacts removed using combined wavelet-Fourier filtering.

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The pore structure was subsequently segmented from the 3D volume using mathematical morphological methodologies, including marker-controlled image reconstruction. As a result the open versus closed porosity can be deduced. The results are shown in Fig. 29. The FIB SEM allows for high 3D spatial resolution, where each voxel is 50 nm cubed. However the sample size is only a few millimeters in diameter. Therefore large area (3 cm x 3 cm) 2D maps are also acquired with high spatial resolution (100 nm) using an SEM, as described above. The 2D pore network can also be extracted. The advantage of the large scale 2D images is that more of the heterogeneity of the sample is understood and the derived rock characteristics and properties are statistically more representative of the core sample. Once the pore network (Fig. 29) is extracted, then techniques such as those described by (Blunt et al., 2013; Øren and Bakke, 2003; Al-Dhahli et al., 2013; Piri and Blunt, 2005; Joekar-Niasar and Hassanizadeh, 2012) may be used to estimate sample permeability.

Fig. 29. Extracted pore structure for studied Cardium sample. Each connected pore structure is represented by different colors. Three orthogonal 2D image planes illustrate the grain matrix.

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A workflow (Fig. 30) has been developed by the Clarkson research group to comprehensively analyze tight rock and shale samples using a variety of imaging and lab techniques for assessment of matrix permeability and other rock properties. The imaging techniques start with large scale, non-destructive, coarse resolution (µCT) and end with small-scale, destructive, highresolution FIB SEM. The same plane is imaged with every imaging technique. Work is ongoing to register the various imaging resolutions (Sok et al., 2010). Registration of the high-resolution 2D images to the coarse-resolution 3D image volumes enables better understanding of the pore connectivity at coarser resolutions and begins to address the scaling problem from nano to micro.

*Performed at Geological Survey of Canada

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Fig. 30. A workflow developed by the Clarkson research group to comprehensively analyze tight rock and shale samples using a variety of imaging and lab techniques for assessment of matrix permeability and other rock properties.

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The above discussion of pore extraction focuses on the extraction of total and connected pore volume for the purposes of estimating single-phase matrix permeability, but does not consider the distribution of phases in a multi-phase system. In shales that produce dry gas, the presence of water, often assumed to reside primarily in inorganic matter, will affect flow of single-phase gas. While core-plug or crushed-rock matrix permeability measurements may be evaluated using preserved samples or samples in the “as-received” state, which may be used to derive an effective permeability to gas at the preserved water saturation, pore-network extraction/modeling methods often either do not consider the effects of entrained water on matrix permeability, or use macro-wettability measurements (sessile-drop) to determine wettability and fluid-rock information for pore-scale modeling. However, in shales or tight rock with mineral matter heterogeneity, the wettability of fluids is expected to vary at the micro-scale, causing macro-scale measurements to be insufficient for characterizing fluid rock interaction. A technique for characterizing fluid-distribution and wettability at the micron-scale has been pioneered by our research group and involves the use of the VP-E-FESEM to “visualize” fluid distributions in tight rock, and also to directly observe fluid-rock interactions as they occur in real time. All experiments associated with determining micro-scale sample wettability are being performed using an FEI Quanta FEG 250 VP-E-FESEM, with a rich suite of ancillary equipment including a Peltier stage, a Kleindiek MM3A-EM and MIS micro-manipulator and micro-injection system, and a Gatan Alto2500 cryo-stage and associated cryo-prep and transfer equipment. Three potential methods for determining micro-wettability of geological materials have been identified: 1) wetting/drying experiments using the Peltier stage and a humid chamber environment, 2) static examination of rock-fluid relationships in cryo-fixed samples using the Gatan cryo-stage, and 3) fluid micro-injection experiments using the Kleindiek hardware. A comprehensive discussion of each of these techniques will be provided in a future paper. Method 1) has been

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applied to various tight reservoirs and shales in western Canada, while method 2) has been successfully applied at larger scales in bituminous sandstones (“oil sands”), and is currently being further developed for application to tight reservoirs. Method 3) remains “under development”. An example application of method 1) for a Bakken sample is illustrated in Fig 31. These experiments were performed by affixing a small (a few mm) chip of rock (and therefore could be applied to cuttings as well) to an aluminium sample cup with thermally conductive epoxy and loading the resulting assembly onto the Peltier stage in the SEM sample chamber. The Peltier stage allows for precise control of the sample temperature, while the SEM's vacuum control system allows the sample chamber to be filled with water vapour at low pressure (several hPa). Control of water vapour pressure allows for control of the dew-point in the chamber and, combined with control of sample temperature, allows for the precise and repeatable control of condensation and evaporation of water droplets on the sample. These droplets are then observed to determine if they wet (i.e., spread out on) mineral surfaces or not (i.e., they form non-spreading beads). Provided suitably oriented grain surfaces normal to the image plane are available, contact angles can also be measured (Fig. 31).

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Fig. 31. Disposition of liquid water on hydrophilic (left) and hydrophobic (right) mineral surfaces. Note very low contact angle at edge of water droplets on hydrophilic surface (red arrows – left). Contrast with high contact angle of droplets on hydrophobic mineral surfaces (red arrows – right).

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A principal advantage of this methodology is that it is easy to perform, and the required technology is well developed. A principal disadvantage is that it is restricted to assessing the wettability of the samples to distilled water (condensed from vapour in the sample chamber), as there is no clear way to condense brines or non-aqueous fluids directly onto the sample surface. Another potential problem may occur if hygroscopic materials (e.g., salts from evaporated residual formation brine) are present, resulting in isolated areas of excessive, hygroscopically-assisted condensation with little or no condensation anywhere else. Drawbacks aside, this is the simplest method for assessing wettability to pure water. The resulting fluid-rock interaction and fluid distribution may be used to populate a pore-scale model for evaluating effective permeability to gas, which will be explored in future work. Although a full discussion of the controls on matrix permeability in shale is beyond the scope of the current summary, a few comments should be made about a paradigm that exists in the literature with respect to the impact of organic matter on matrix permeability. There are various publications that illustrate a positive correlation between permeability and TOC (e.g. see Dunn et al., 2012 correlation for Duvernay Shale). However, recent studies by Wood et al. (2015) have evaluated the impact of the type of organic matter on permeability, using the Montney tight siltstone/shale reservoir as an example. Using a combination of modified Rock-Eval procedures, discussed in Sanei et al. (2015), and FIB-SEM imaging, an example of which is shown in Fig. 9, the dominant organic matter component was determined to be thermally-degraded bitumen (as opposed to kerogen), which negatively effects permeability – this is illustrated conceptually in Fig. 32a. Wood et al. (2015) developed an innovative method to estimate bitumen saturation and correlate that to permeability as illustrated in Fig. 32b. This result clearly suggests that the type of organic matter should be considered in evaluating its effect on matrix permeability.

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Fig. 32. (a) Conceptual model showing distribution of solid biumen in pore space of Montney samples. (b) Relationship between permeability and bitumen saturation. From Wood et al. (2015).

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4.1.2 Fracture permeability from core Again referring to the core analysis workflow (Fig. 12), a technique has been developed for estimating unpropped fracture permeability, using the same apparatus as for pulse-decay permeability tests. This technique was described by Ghanizadeh et al. (2015b), which is a modification of the procedure described by Cho et al. (2013). The matrix permeability is first performed using pulse-decay measurements performed at the estimated effective stress. The plug is then fractured under-stress by altering the differential stress (axial – radial load) and permeabilities measured in steady-state mode (Fig. 33). The results of estimated fracture permeability (see Cho et al. for calculations) are shown in Fig. 34 (Ghanizadeh et al., 2015b). It can be seen that the sensitivity of fracture permeability to stress for the Montney samples using the procedure of Ghanizadeh et al. (2015b) is less than that using the Cho et al. (2013) procedure in which the core samples were sawcut to create the fractures – the differences may be due to the different lithologies studied (Montney versus Bakken Formation), but also due to the difference in how the fractures were created (under stress versus sawcut) and measurements performed. The bulk permeability of the core with the single fracture is on the order of 5-9 mD, depending on the combination of pore pressure/confining pressure applied, whereas the estimated fracture permeability is on the order of 9-10 Darcy’s. In order to use these measurements to predict field-scale values of bulk fracture permeability (to compare to those derived from production data analysis - see Section 4.3), a fracture model with specified fracture density would be required.

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Fig. 33. Apparatus used to generate a fracture and measure fracture permeability under stress conditions. Modifed from Ghanizadeh et al. (2015b).

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Fig. 34. (a) Core-plug used for (unpropped) fracture permeability measurement – the fracture was created under stress using the instrument shown in Fig. 33. (b) Comparison between unpropped fracture permeability measurements of Ghanizadeh et al. (2015b) and those of Cho et al. (2013). Modified from Ghanizadeh et al. (2015b). 4.1.3 Rock mechanical properties from core Rock mechanical properties, including Young’s Modulus, Poisson’s Ratio, compressive and tensile strength are important to be characterized for shales for the purpose of tuning hydraulic fracture models (used in turn for fracture property estimation) and for determining the ability of a shale to be hydraulically-fractured (Reinicke et al., 2010; Josh et al., 2012; Holt et al., 2015; Rybacki et al., 2015). For frac modeling, input data from geophysical logs (e.g. dipole sonic) to characterize rockmechanical properties over the interval of interest are typically used, but cored-derived properties are required for log calibration. The conventional approach for rock property estimation is to perform confined or unconfined compressive strength tests until rock failure to determine the properties of interest (Reinicke et al., 2010; Josh et al., 2012; Holt et al., 2015; Rybacki et al., 2015). Fracture initiation, propagation and reopening are controlled by brittleness/ductility of shale rocks. Combined with magnitude and orientation of in-situ stress, static elastic and failure properties such as unconfined compressive strength and their anisotropic characteristics are required to evaluate brittleness/ductility of shale reservoirs. The laboratorybased characterization of static elastic and failure properties are typically performed on core plugs for shales. The (static) rock mechanical data for the composite system making up the core plug is, therefore, derived. Two non-destructive techniques for deriving rock-mechanical properties from core plugs have recently been explored.

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The first method is based on the use of an Equotip Piccolo hardness test (Verwaal and Mulder, 1993; Viles et al., 2011; Solano et al., 2012; Ghanizadeh et al., 2015a), which is a fast, non-destructive technique to differentiate and quantify the mechanical properties of cm-scale heterogeneities in rocks. The description, operation and application of this tool for Bakken and Montney tight oil/gas/shale reservoirs was recently described by Ghanizadeh et al. (2015a). The tool can be used to estimate mechanical rebound hardness (microhardness) values, which are then converted to unconfined compressive strength (UCS), Young’s Modulus and Poisson’s ratio using correlations provided in the literature (Ghanizadeh et al., 2015a). An example application of the tool to slabbed core intervals in the Montney is illustrated in Fig. 35 – the microhardness measurements, converted to UCS, were measured at the same spots (~ 1 inch intervals; 2.5 cm) as profile permeability data to determine if there is any relationship between permeability and mechanical hardness. The chart in Fig. 35 illustrates that UCS values derived from the Equotip Piccolo tool and dynamic values obtain from logs over the same interval are in good agreement for the interval from 3044 – 3045 m where the core appears lithologically homogeneous (section D in Fig. 35). For the interval from 3042 - 3044 m where visual heterogeneities exist in the core (laminations), the agreement is not as good, but this is likely due to the difference in log resolution compared to the Equotip Piccolo tool. X-ray fluorescence (XRF) measurements are currently being run over the same intervals to allow the controls of mineralogy, and degree of cementation on mechanical properties to be ascertained. If a correlation can be determined, this will provide us with a method to extract these properties from cuttings, for which XRF data may also be collected (see Section 4.2.2).

Fig. 35. Application of a mechanical hardness-UCS correlation to estimate the UCS values along slabbed core (Montney; Alberta, Canada); modified after Ghanizadeh et al., 2015a). Note that, following an averaging approach for the comparatively homogeneous lithological units (Ghanizadeh et al., 2015a), the estimated UCS values in the comparatively homogeneous bed (section D) are in agreement with those derived from sonic logs (Vishkai et al., 2014; personal communication). Another non-destructive technique that can be used to obtain mechanical properties from core is the use of a sonic coreholder for estimating p-wave (Vp) and s-wave (Vs ) transmission through a coreplug. An example setup is illustrated in Fig. 36. These measurements can be performed at confining stress on the same coreplugs used for making matrix and fracture permeability measurements using the pulse-dcay and steady-state techniques, respectively (see Section 4.1). A source and receiver is placed on the upstream and downstream end of a coreholder, and Vp and Vs are estimated. These measurements, which could be made for dry or fluid-saturated samples at confining stress, can in turn be used to estimate sonic porosity, dynamic Young’s Modulus and Poisson’s Ratio (v) for comparison with sonic log-derived values. Further, after the sonic (dynamic) measurements are made on the coreplug, traditional static measurements of rock mechanical properties using

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triaxial strength testing may be made on the same coreplug to derive a rigorous dynamic-static correction. Preliminary results of the analysis of four coreplugs from a tight/shale formation in western Canada using this sonic coreholder technique are provided in Table 4. For comparison with log data run over the same core interval, velocities for a frequency of 10 kHZ were recorded, which is closest to the well-log frequency. Core plugs oriented parallel (horizontal) and perpendicular (vertical) to bedding were analyzed and subjected to 2300 psi effective pressure (estimate for actual reservoir conditions). Details of the measurements and results will be reported in a future paper. The agreement between core- and log-derived estimates of Vp, Vs and v is excellent, despite the differences in scale. The measurements, performed for a range in effective pressures, demonstrate that P-wave velocity changes with effective pressure are larger compared to shear velocity changes with effective pressure. As frequency increases, both P-wave and S-wave velocity increase. It should be noted that shear-wave splitting was observed in the core, suggesting that fractures can be detected in the core, which can affect matrix permeability estimates (see Section 4.1) – this method may therefore prove valuable for screening core plugs for stress-induced fractures, which can affect permeability estimates – this will also be reported in future work.

Fig. 36. Sonic core holder setup used for measurement of Vp and Vs in cores under stress. Table 4 Results of sonic coreholder measurements and comparison to log-derived properties. Vp from Core (m/s) 4947.550 4957.165 5002.393 5135.80

Vp from Logs (m/s) 4775.868 4743.653 4909.458 4911.45

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Vs* from Core (m/s) 3093.387 2959.237 2983.967 2978.375

Vs* from Logs (m/s) 2949.661 2932.981 2940.94 2935.24

Vp/Vs from Core 1.60 1.64 1.67 1.72

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4.2 Estimating fluid transport properties along a horizontal well The techniques described in the previous section are applicable to slabbed core or core plugs, but as noted previously, the only reservoir samples typically available from horizontal wells used to exploit shale gas reservoirs are drill cuttings. The following summarizes recent efforts to extract permeability/diffusivity and rock mechanical properties from cuttings. It should be noted that the techniques described in the previous section for matrix permeability, fluid-rock interaction, and rock mechanical property assessment can be used to evaluate these property estimates from cuttings. One method that can be used to compare core and cuttings-derived properties is to create artificial cuttings from the crushed core samples and performing these property estimates on the resulting materials. This approach was used for estimating fluid-in-place in Section 3.1, and will be discussed below for fluid transport properties. Additional reservoir and stress information for use in hydraulic fracture design may be obtained from DFITs, but these are typically only performed on the toe of long horizontal wells and therefore derived information is relevant to only a small

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Matrix permeability/diffusivity from cuttings 4.2.1 Again referring to the Haghshenas and Clarkson (2016a, in preparation) study of Duvernay shale artificial cuttings (crushed core samples) discussed in Section 3.1, a new model was developed by those authors for extracting permeability/diffusivity from low-pressure adsorption data that can be performed on small sample masses typically available for cuttings (1 to 2 g). As summarized by Haghshenas et al. (2015), a commercial crushed-rock permeability measurement apparatus was first tried for analysis of artificial cuttings, but the precision of data did not allow for quantitative results, even for relatively large sample masses; the use of a higher-precision, low-pressure adsorption device, which gathers pressure versus time during adsorption isotherm pressure steps, was used instead for this purpose, resulting in a very high quality data set (Fig. 37a). CO2 was used as the measurement gas. However, in order to model the data, a bidisperse pore structure was assumed, consistent with pore-size distributions observed with the samples (Fig. 14; Fig. 37b). While such a formulation has been previously considered for modeling adsorption rate data of coal reservoirs (e.g. Clarkson and Bustin, 1999b), the new model by Haghshenas et al. (2015) for shales considers the effects of adsorption in both pore systems, as well as multimechanism flow (diffusion, and slippage-viscous flow), the latter of which is important to consider because of the combination of low pressures and small pore sizes (by contrast, Clarkson and Bustin, 1999b considered adsorption only for the micropores in coal, and that diffusion was the sole transport mechanism). The result is a very flexible model for estimating micropore and macropore apparent permeability for shales from highprecision, low-pressure adsorption rate data obtained using small masses of crushed-rock samples (artificial cuttings). The resulting permeability values obtained from low-pressure adsorption analysis of small volumes of artificial cuttings are currently being compared to those derived from larger volumes of crushed rock samples using other commercial equipment as well as permeabilities derived from the original uncrushed core-plug. The method is also currently being tested on actual cuttings samples obtained from a horizontal lateral drilled in a western Canadian tight/shale formation.

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Fig. 37. (a) Fit of bidisperse numerical model to high-precision low-pressure rate-of-adsorption data for the purpose of extracting/diffusivity data and (b) conceptual model used in the development of the bidisperse numerical model. Modified from Haghshenas et al. (2015). 4.2.2 Rock mechanical properties from cuttings While the development of direct methods for estimating mechanical methods is ongoing, indirect, semi-empirical methods may be used to estimate rock mechanical properties from cuttings based on rock composition and fabric (e.g. cement versus grains). Weedmark (2014) developed an algorithm for predicting Poisson’s Ratio (PR) and Young’s Modulus (YM) from cuttings using x-ray fluorescence (XRF) data. XRF provides elemental composition, not mineral composition, and must be carefully calibrated against standards. Further, as with the permeability estimates provided above, XRF-derived mineralogy for cuttings should be compared with that derived from larger core samples. Details of these procedures are provided by Weedmark (2014). The basic process for determining rock mechanical properties is as follows:

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The mineral components identified by XRF must first be grouped into detrital and cement fractions The weight percentage of each mineral for the total rock is then calculated – if the density of the mineral is known, the weight percent can be calculated from volume percentage Most minerals other than quartz fall into the cement or detrital category. For quartz, a quartz/zircon baseline is assumed which can be used to distinguish detrital and cement fractions Rock property values are then assigned to each mineral – for example quartz has an approximate PR of 0.17 and YM of 72 GPa The final step is to assign a value for each mineral according to whether it is cement or detrital – the weighting factor is greater for cement than detrital components, owing to its greater impact on mechanical properties

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An example calculation was provided by Weedmark (2014) as follows: PRRock = (.95)(PRQtz)(QtzDet %) + (.95)(PRFeldspar)(Feldspar %) + (.95)(PRClay)(Clay %) + (.95)(PRCalcite)(Calcite %) + (1.2)(PRQtz)(QtzCmt %) + (1.0)(PRPyrite)(Pyrite %) + (Shift Factor) Where the shift factor is used to account for effects on mechanical properties that aren’t mineralogically-controlled such as burial depth, compaction and stress. A reference data set based on well-logs is therefore required to allow the calibration of the shift-factor, weighting factors and mineral properties. The method is therefore semi-empirical in nature. An example of this approach as applied to cuttings derived from two horizontal laterals drilled in the Horn River Basin shales is shown in Fig. 38. This display, which shows both geochemical and rock mechanical properties derived from XRF analysis of cuttings, demonstrates that significant variations in these properties occur along the length of the laterals as well as between the laterals targeting the same interval. The mechanical property variation in particular suggests that zones may be selectively targeted for stimulation, which is of course the intent of quantifying these properties.

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Fig. 38. Application of XRF to estimation of rock composition and mechanical properties from cuttings collected along two horizontal wells drilled in Horn River Basin shales. From Weedmark (2014).

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4.2.3 DFIT Excellent recent summaries of DFIT application and interpretation have been provided (e.g. Barree et al. 2009). A conceptual DFIT signature is provided in Fig. 39 which highlights the important parameters that can be derived from the test, as well as the data analysis periods (pre- and after-closure). Data of importance for fracture design include instantaneous shutin pressure (ISIP, which equals the final injection pressure – pressure drop due to friction in the wellbore and perforations or slotted liner) and closure pressure (a reflection of minimum in-situ stress), initial reservoir pressure and reservoir permeability. For deriving reservoir properties such as permeability and initial reservoir pressure, the focus has been on after-closure analysis. However, for shales, excessive test lengths often are required to achieve after-closure flow regimes (e.g. radial flow) which can be analyzed quantitatively. A further problem is that these tests are usually performed on the toe of long horizontal laterals (Fig. 10) and hence the resulting derived properties may not be representative of the entire well-length. A goal for horizontal well characterization is therefore to 1) obtain reservoir information earlier in the test, and hence shorten the test length 2) apply DFITs at multiple points along the horizontal lateral so that property variation along the lateral may be quantified. Clearly goal 1) must be achieved to make multiple DFITs appealing to operators to reduce the expense of the tests and the time taken to perform them. The following discusses different approaches for achieving goal 1) – additional considerations for achieving goal 2) include development of completion technology to make multiple DFITs a possibility, which is beyond the scope of the current discussion.

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Fig. 39. Conceptual DFIT plot showing rate and pressure profiles during the test. Test interpretation can be subdivided into pre- and post- fracture closure regimes. Image courtesy of Behnam Zanganeh.

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Two approaches for achieving an accelerated test (goal 1) include: 1) using conventional testing procedure combined with time to closure to develop empirical estimates of permeability estimation and 2) forcing closure by flowing back after a short period of pressure falloff. 1) was suggested by Barree et al. (2009) in application to conventional DFIT procedures used for analyzing tight rock while 2) was suggested by Yuan et al. (2011) for caprock integrity testing. Barree et al. (2009) derived (using numerical simulations) an empirical function for formation permeability estimation based on the G-function time at closure – the equation is given in the inset of Fig. 40. Application of this equation for various Montney horizontal wells, whose DFIT results suggested that analyzable after-closure radial flow regimes were obtained during the test, is also shown in the plot. There is generally within order-of-magnitude agreement between the approaches with the closure-time estimates tending to be relatively high compared to the after-closure estimates. The constants in the closure-time model may require adjustment, depending on the reservoir properties of the tight reservoir analyzed – this is currently being investigated by the Clarkson research group using coupled flow-geomechanical simulation. However, this approach is promising because the time to reach closure may be significantly shorter (hours to days) than the time to reach analyzable after-closure flow regimes.

From after-closure analysis

Fig. 40. Comparison of permeability values obtained from closure time (blue circles) and after-closure analysis (brown squares). Image courtesy of Chetan Tewari. Yuan et al. (2011) implemented a series of modified mini-frac in shallow shales in western Canada. Instead of a single test cycle consisting of an injection period followed by a falloff, multiple cycles were implemented to ensure consistency – an example of such a test cycle performed for the Clearwater shale in western Canada is shown in Fig. 41. Importantly, flowback

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ACCEPTED MANUSCRIPT was initiated during some of the cycles; a volume of water was withdrawn from the injection system (wellbore + fracture) during the shut-in/falloff cycle, effectively accelerating closure. Using a plot of bottomhole pressure versus flowback volume (referred to as a “compliance plot”), an independent estimate of closure pressure (intersection of two straight lines on the compliance plot) was obtained (Fig. 42). Theoretically, for deeper shales, the flowback cycle could also be performed during the intial falloff test, thereby accelerating closure to obtain closure pressure (and hence an estimate of minimum horizontal stress) more quickly. Although not discussed by Yuan et al. (2011), it may also be possible to obtain reservoir information using the flowback data (as discussed further below for main fracture treatments). To our knowledge, this approach has not been tried for deeper shales, but shows some promise for shortening DFIT periods by accelerating time to closure and analysis of flowback data. Cycle 1

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ACCEPTED MANUSCRIPT 4.3 Estimating fracture and reservoir properties from production data Referring again to Fig. 24 (workflow for RTA), we see that both type-curve and straight-line techniques may be used to extract reservoir and hydraulic fracture properties from production data – a comprehensive summary of these techniques was provided by Clarkson (2013a). Analytical and numerical simulation may be used to both history-match production data to confirm/revise the estimates obtained from straight-line and type-curve analysis and to generate a long-range forecast. The following discusses the use of both flowback data and longer-term online production data for this purpose.

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4.3.1 Flowback analysis In recent years several authors have attempted to quantitatively analyze flowback data to extract key fracture and reservoir properties. These attempts can be categorized at a high level to be either single-phase analysis before breakthrough (BBT) of formation fluids, or multi-phase analysis after breakthrough (ABT). Some examples of single-phase analysis include Crafton (2008), Abbasi et al. (2014) and Fu et al. (2015). Some examples of multi-phase analysis include Alkouh et al. (2013), Ezulike et al. (2013), Adefidipe et al. (2014) and Kurtoglu et al. (2015). Williams-Kovacs and Clarkson (2015) recently provided a comprehensive review of literature related to quantitative flowback analysis. Clarkson and Williams-Kovacs (2013a) and Clarkson et al. (2014) recently demonstrated, for the first time, the use of the full RTA workflow (Fig. 24), originally developed by Clarkson (2013b) for longer-term (online) production data, for flowback analysis. The objective of those studies was to quantify hydraulic fracture properties by analyzing the early time hydraulic fracture fluid production, followed by combined fracture and fluid production, and treating the fracture as a porous medium (reservoir) with quantifiable properties (permeability, fluid-in-place). The following briefly summarizes the Clarkson et al. (2014) study and extension of the approach to shale gas by Williams-Kovacs and Clarkson (2015). The subject well studied by Clarkson et al. (2014) was a multi-fractured horizontal well completed in tight/shale oil reservoir. Steps 4-7 of the RTA workflow (Fig. 24) are illustrated in Figs. 43-45. The subject well was hydraulicallyfractured in multiple stages and flowed back for over 10 days, resulting in the production profile illustrated in Fig. 43a. Just over 2 days of single-phase hydraulic fracture water production precedes breakthrough of formation fluids to the fracture. Flow-regime analysis performed on the water data (using a rate-normalized pressure, RNP, and rate-normalized pressure derivative, RNP’, plot) reveals that, BBT, the dominant flow-regime is fracture depletion or storage (Fig. 43b) and that, ABT, combined fracture depletion and linear flow from the formation to the fracture occurs.

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Fig. 43. Raw data (a) and diagnostic plot (b) for flowback analysis. RNP’ (b) shows a dominant period of single-phase fracture depletion prior to the breakthrough of formation fluids. Note that GOR is relatively stable until ~8 days, at which point the choke was significantly reduced and gas breaks through into the fractures. Modified from Clarkson et al. (2014). Fig. 44 illustrates the use of straight-line, type-curve and analytical simulation to analyze BBT frac fluid production to extract hydraulic fracture properties. Flowing material balance (Fig. 44a) was performed on the water production data BBT to extract the mobile-water-in-place in the fractures, which in turn can be converted to a total fracture area (given estimates of total fracture width and porosity) or total fracture half-length (assuming planar fractures). In cases where estimation of fracture properties is not possible (width, porosity, or geometry), the mobile fracture fluid-in-place is still a useful indicator of hydraulic fracturing efficiency (Fu et al. 2015). It is also possible to estimate the fracture permeability-thickness production from the intercept of the FMB, or from fracture transient data (not evident in this dataset). Type-curve analysis (Fig. 44b) can be used to both confirm the flow-regime interpretation and the fracture properties derived from straight-line analysis. Typecurves are selected based on the interpreted flow-regime sequence with the Fetkovich type-curve (Fetkovich 1980) being used here (transient radial to boundary-dominated flow). The final step of the BBT analysis is the use of analytical simulation to

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ACCEPTED MANUSCRIPT history-match the frac fluid production, using as input the fracture properties (in this case, fracture permeability-thickness and fracture half-length) derived from straight-line and type-curve analysis (Figs. 44c,d).

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Fig. 44. Before breakthrough analysis of flowback data: a) flowing material balance; b) Fetkovich type-curve; c) analytical rate match and d) analytical cumulative production match. Flow is single-phase and assumed to be in the fracture only. Flowregimes identified using RNP’ (Fig. 43b). Modified from Clarkson et al. (2014).

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Fig. 45 illustrates the use of an analytical simulator to history-match BBT and ABT fluid production, allowing an estimate of BBT and ABT fracture properties. The BBT fracture property estimates were obtained from the previous steps. Details of model development and use are provided by Clarkson and Williams-Kovacs (2013a) and Clarkson et al. (2014) – in the later study, the derived fracture properties from flowback (e.g. fracture half-length) were compared to independent estimates (microseismic, frac modeling and analysis of long-term production using RTA) to quantify changes in fracture properties over time.

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Fig. 45. Use of analytical model for flowback data history-matching: a) production rates of water, oil and gas; b) cumulative production of water, oil and gas. Modified from Clarkson et al. (2014).

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For shale gas wells, Clarkson and Williams-Kovacs (2013b) used a conceptual model similar to that used for analyzing production from coalbed methane wells, and applied it to modeling gas and water production during flowback of multifractured Marcellus shale gas wells. Williams-Kovacs and Clarkson (2015), later revised the approach and models to be more consistent with those applied to tight oil wells discussed above. They used a conceptual model for flowback that is similar to that used by Clarkson and Williams-Kovacs (2013a) for the development of their analytical model and methods; single-phase flow in the fracture, dominated by the fracture storage is assumed to be followed by gas breakthrough in the fracture (Fig. 46). The primary difference is that, in the modeling of Williams-Kovacs and Clarkson (2015), the following advances were made specific to the shale gas problem: dynamic fracture porosity and permeability were included; a modified material balance equation (MBE) was developed to account for additional drive mechanisms (fracture closure in addition to desorption and gas expansion); and a modified pseudo-pressure and pseudotime were applied to BBT single-phase rate-transient analysis (RTA) and ABT multi-phase RTA (conducted after analytical simulation using pressure and saturation dependent outputs from simulation). Details of the model development are provided in Clarkson and Williams-Kovacs (2015).

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ACCEPTED MANUSCRIPT Fig. 46. Conceptual model of flow-regimes observed during flowback from shale gas wells. Single-phase (frac fluid only) flow-regimes in (a) (cross-section view of a single fracture) and (b) (plan view of a single fracture) are transient radial flow (FR1) and fracture depletion (FR2), as identified on the RNP derivative (c). After breakthrough transient linear flow from the matrix to the fractures is coupled with multi-phase depletion within the fracture network (FR3) which is shown in both crosssection view (d) and plan view (e). Modified from Williams-Kovacs and Clarkson (2015).

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An example application of the new flowback model for shale gas is shown in Fig. 47. Some fracture complexity is assumed to have been generated within the fracturing stages (Fig. 48). A good history-match is achieved for both phases (Fig. 47 a,b) from which fracture permeability and total fracture half-length are estimated to be 6.5 md and 2316 ft (or 193 ft per stage) respectively. Importantly, initial reservoir pressure and reservoir permeability obtained from DFIT analysis were used to constrain the modeling, emphasizing the importance of the use of other characterization methods to constrain the modeling. Fig. 47c (gas-water-ratio versus cumulative gas production) is used to verify the quality of the two-phase history-match, while Fig. 47d uses fractional flow theory to verify that the fracture relative permeability curves input into the analytical model are resonable. While this example demonstrates that it is possible to extract fracture properties from flowback data, production analysis of long-term (online) production is required to verify the results – this is discussed in the next section.

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Fig. 47. Use of new shale analytical model for flowback data history-matching: a) production rates of water and gas; b) cumulative production of water and gas; c) GWR; and d) fractional-flow plot. Modified from Williams-Kovacs and Clarkson (2015).

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ACCEPTED MANUSCRIPT Secondary fracture zone containing no proppant

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Fig. 48. Conceptual model of flowback showing circular fracture shape and the formation of a complex fracture network around each fracture stage. Modified from Williams-Kovacs and Clarkson (2015).

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Online production analysis 4.3.2 Analysis of long-term (online) production after flowback and well shut-in has similarly been analyzed for fracture and reservoir properties. Indeed most of the effort in the field of production analysis has focused on online production. Again, referring to the RTA workflow (Fig. 24), straight-line and type-curve analysis has been used to derive fracture and reservoir properties, with analytical/numerical simulation used to confirm the derived properties and forecast production into the future. Among the most common flow-regimes observed in online production is transient linear flow (see panel e in Fig. 46). Using the same shale gas well as an example as was used for flowback analysis in the previous section, we see that the online production (Fig. 49a) for this well exhibits transient linear flow (Fig. 49b). This linear flow period is assumed to be linear flow to the individual fractures in this multi-fractured horizontal well. Using the linear flow superposition plot (Fig. 49c), the linear flow parameter (xft√k or product of the square-root of permeability and total fracture half-length) may be obtained. Using the same reservoir permeability estimate as was assumed in flowback analysis (Fig. 47), a total fracture halflength of 2328 ft was obtained, which is in excellent agreement with that obtained from flowback analysis (2316 ft). In this example, breakthrough of gas to the fracture occurred early in the flowback period, and no significant loss of producing halflength occurs from the flowback to online production periods. This is in contrast to the tight oil example analyzed in the previous section, where gas breakthrough occurred near the end of the flowback period, and the well was observed to have a loss in producing fracture half-length (see Clarkson et al. 2014 for discussion). It is therefore important to continuously monitor fracture properties during flowback and online production for changes in fracture properties.

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Fig. 49. Online production data: a) rate and calculated sandface flowing pressure; b) gas RNP; and c) linear superposition plot. The diagnostics suggest that the majority of the data comes from a linear flow-regime, which is assumed to be early linear flow, and this data is analyzed on the superposition plot. Modified from Williams-Kovacs and Clarkson (2015).

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In the online production example used above, single-phase flow of gas was assumed to be occurring during the transient linear flow period. In some cases, multi-phase flow of gas and oil (or condensate) may occur during the online production period if the well is producing below saturation pressure of the fluid. Further, continued production of flowback water or mobile formation water may occur. Finally, matrix and fracture permeability may be stress-sensitive. Several authors have recently discussed approaches to account for these effects during the transient linear flow period using analytical methods (e.g. Qanbari and Clarkson a,b; Behmanesh et al. 2015). An example application of these corrections for a tight gas condensate well is shown in Fig. 50. In the approach used, as described by Qanbari and Clarkson (2013a), a correction factor, derived analytically, was applied to the slope of the linear flow plot, from which the linear flow parameter (Ac√k, for complex fracture geometry cases, or xf√k for low fracture complexity cases) can be obtained. The correction factor is calculated as follows:

dψ D . ................................................................................................................................................................. (27)

Eq. (27) relates the correction factor to an integral (with the solution of the flow equation in the integrand). An approximate solution of the flow equation is required in order to evaluate the integral in Eq. (27). For this, following the work of Qanbari and Clarkson (2013a), the iterative integral method is used resulting in an approximate solution of the flow equation as follows:

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ACCEPTED MANUSCRIPT  exp − 2 0  ψ D (ξ ) = 1 − ∞  exp − 2 0 

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Further discussion of this approach to correct for the effects of multi-phase flow and stress-sensitivity is provided in Qanbari and Clarkson (2013a).

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Fig. 50. Analysis of linear flow period of gas condensate well producing gas, condensate and water. Analysis of the data assuming single-phase flow and static permeability can be significantly in error, as determined through the application of correction factors using the Qanbari and Clarkson (2013a) approach. In this example (Fig. 50), we can see that the assumption of single-phase flow of gas in the reservoir can lead to significant errors – corrections for condensate dropout, water production and stress-dependent permeability can be significant, and can affect estimation of the linear flow parameter (and hence fracture properties).

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4.3.3 Effect of pore confinement on production analysis In the previous discussions of rate-transient analysis of both flowback and online production, it was assumed that Darcy’s Law was valid for describing flow through the shale matrix and fractures, and that pore-confinement effects were not significant. As discussed in Section 2.2, non-Darcy flow, such as slip-flow and diffusion, may be important in the matrix of shales, and pore confinement effects, such as alteration of fluid properties, may also need to be considered. Non-Darcy flow effects on RTA were previously considered in peer-reviewed literature by Clarkson et al. (2012c) and Nobakht et al. (2012). Qanbari et al. (2014), however, considered the combined effects of non-Darcy flow, stress-sensitive permeability, and pore confinement effects on transient linear flow analysis of shale gas wells producing dry gas. The corrections applied, and application of those corrections, are summarized in the following. For pore confinement effects, the following quadratic equation (from Zarragoicoechea and Kuz 2002, 2004) was used by Qanbari et al. (2014) for adjusting gas critical temperature and pressure:

=

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Note that, as discussed in Secion 3.1.3, it is now possible to modify these properties as a function of pore size directly using the Simplified Local Density model. In the simulated example analyzed in Qanbari et al. (2014), a 2 nm pore size was assumed (Fig. 51c). For permeability modeling, the combined effects of non-Darcy flow, adsorption layer thickness, and stress-dependence were considered. The Javadpour (2009) model was used to account for slip-flow and diffusion effects, while the Simplified Local Density model was used to estimate the sorbed layer thickness, which in turn was used to adjust the effective radius for gas flow (pore radius mines the adsorbed layer thickness) for use in the Javadpour model. Finally, stress-sensitive permeability was modeled by using the permeability-pore pressure relationship of Yilmaz et al. (1994), that assumes an exponential relationship between permeability and pore pressure, which in turn requires an estimate of the permeability modulus (γk). A comparison of the permeability predictions for permeability with and without considering adsorbed layer thickness are shown in Fig. 52a, while the combined effects of non-Darcy flow, adsorbed layer thickness, and stress sensitive permeability, are illustrated in Fig. 52b for the case studied by Qanbari et al. (2014):

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In order to correct for the combined effects described above, the analytical correction factor approach of Qanbari and Clarkson (2013a) was used to “correct” the slope of the square-root of time plot, which is commonly used to analyze linear flow but assumes that Darcy’s Law is valid and does not consider pore confinement effects (Fig. 53).

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ACCEPTED MANUSCRIPT Fig. 53. Analytical method used to correct the slope of the square-root of time plot (mcp) for the effects of pore confinement and dynamic permeability changes. In order to compare the derived fracture half-length (xf) from the square-root of time plot with the correction factor in place with that derived without corrections (for non-Darcy flow, pore confinement etc.) the following function was used:

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Qanbari et al. (2014) applied Eq. (30) to a numerically-simulated case. Following a procedure that consisted of 1) calculating adjusted critical properties using Eq. (29) 2) calculating gas compressibility and gas viscosity using correlations (see Fig. 51c) 3) calculation of apparent permeability that accounts for gas slippage and diffusion, adsorbed layer thickness and stress-sensitivity (see Fig. 52b) 4) calculation of correction factor accounting for these effects (Fig. 53) and 5) calculating the ratio given by Eq. (30), Qanbari et al. (2014) found that ignoring these effects resulted in an error (overestimate) in fracture half-length of approximately 50%. However, the pore size assumed (2 nm) is on the small side of that expected to affect flow in shales; Qanbari et al. (2014) noted that the error was a strong function of pore size, permeability modulus (used in stresssensivity calculations) and flowing pressure. A sensitivity performed by those authors illustrates the sensitivity of the correction factor to pore size (Fig. 54): 1

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0.8

rp = 8 nm

0.6 0.5

EP

0.4

0

1000

2000

3000 p w (psi)

4000

5000

6000

AC C

Fig. 54. Sensitivity study to determine the impact of pore size on the correction factor used to correct the slope of the squareroot of time plot for linear flow analysis. Future work by the Clarkson research group will investigate these (pore confinement) effects for liquids-rich shales where the phase-envelope of the fluid shifts as a function of pore size (e.g. Fig. 19b) – it is anticipated that the results of RTA for these systems will be significantly affected. 4.4 Estimating hydraulic fracture, reservoir, AND in-situ fluid properties from numerical simulation Numerical reservoir simulation remains the most rigorous tool for evaluation of shale gas wells (e.g. Sun et al. 2015). Simulation history-matching of dynamic well data (production, flowing pressures) can be used to derive estimates of unknown parameters. Historically for shale gas reservoirs, simulation history-matching has been used to derive estimates of reservoir and hydraulic-fracture properties. However, as noted in Section 2.2, for liquids-rich shale plays, fluid production at the surface is a function of not only reservoir and hydraulic fracture properties, but also operating conditions and in-situ fluid composition. As further noted, the current practice of re-combining fluids to estimate in-situ fluid composition can therefore be in significant error. Finally, as noted in Section 3.1.3, pore-confinement effects can also significantly affect fluid composition. Uncertainty in fluid properties can significantly affect our ability to forecast both primary and enhanced recovery in shales.

53

ACCEPTED MANUSCRIPT In order to address the additional uncertainty in fluid properties, Hamdi et al. (2015) recently used an assisted historymatching routine, in combination with compositional numerical reservoir simulation, to characterize reservoir fluids, as well as extract reservoir properties from a multi-fractured horizontal well completed in a Montney tight/shale gas condensate reservoir in Western Canada. 20 unknown parameters, summarized in Table 5, including initial water distribution, in-situ fluid and reservoir and hydraulic fracture properties were included in this high-dimensional inverse problem. In order to reduce the number of unknowns, an element of symmetry (a single hydraulic fracturing stage) was implemented in the modeling (Fig. 55).

Fluid

PVT( C7+ Characterization) Relative Permeability (Corey’s)

Max

X1 X2 X3 X4 X11 X12 X13 X14 X15 X16 X17 X18 X19 X5 X6 X7 X8 X9 X10 X20

Movable initial water in virgin matrix Movable initial water in the SRV Shale permeability SRV permeability C1 C2 C3 i-C4 n-C4 i-C5 n-C5 C6 C7+ MW of C7+ SG of C7+ Nw Now Ng Nog γ

0.2 0.4 0.00001 md 0.01 md 0.75 0.05 0.04 0.005 0.005 0.002 0.002 0.002 0.015 100 0.75 2 2 2 2 1E-6

0.4 0.7 0.001 md 10 md 0.9 0.08 0.08 0.02 0.02 0.01 0.01 0.01 0.06 140 0.9 4 4 4 4 0.01

AC C

EP

Fracture Permeability Reduction

Min

SC

Rock

Parameter description

M AN U

Water

index

TE D

Initialization

RI PT

Table 5 Parameters adjusted for simulation of liquids-rich shale well.

y Fig. 55. Illustration of the use of an element of symmetry to history match tight gas condensate well. From Hamdi et al. (2015). In order to address this difficult multi-dimensional inverse problem, Hamdi et al. (2015) utilized the Differential Evolution

54

ACCEPTED MANUSCRIPT (DE) algorithm (Storn and Price, 1995; Price et al., 2005), which is a population-based optimization algorithm, to assist with history-matching flowing pressures, water and hydrocarbon rates, and surface compositions of produced fluids. The misfit function employed for this purpose is given as:

+ ×

=0 =0 !=1

− _

−

_

−

_

2

_

2

_ 2

+

=0

_

− _

_

2

+

=0

−

2

+ 10

RI PT

=

_

AC C

EP

TE D

M AN U

SC

where, T represents the daily timestep, q is the surface production rate; subscripts o, w, and g are for the oil, water and gas phases. Arithmetic average values are represented with a bar sign (¯ ). δ represents fluid composition. Fluid compositions obtained from separator data from the 100th day of production were used in the history-matching. Additional details of model setup and history-matching can be found in Hamdi et al. (2015). Fig. 56 represents the best history-match obtained with the DE algorithm. A satisfactory match of all data was achieved, although there is an early mismatch of oil rate data; the operator confirmed however that there were some reporting errors for oil rate at early time. The estimated fluid composition obtained from this history-match is a lean gas condensate fluid with a maximum oil saturation of 1.5 %, and a dew point pressure of 2708 psi at a reservoir temperature of 162 °F. At this time it is uncertain how this fluid composition varies from that obtained from conventional PVT testing obtained from the lab for this field, however, Whitson and Sunjerga (2012) have suggested that liquid-rich shale gas condensate wells will always produce much leaner fluids than conventional systems for the same initial fluid system – they attributed this to low permeabilities and high drawdowns causing condensate to drop out in the reservoir and remain immobile. Whitson and Sunjerga (2012) further suggested that, because of the immobility of the liquid condensate in the reservoir, only solution condensate (carried in reservoir gas) at bottomhole pressure is produced. As noted earlier, an additional cause of the apparently leaner fluid production is pore confinement, causing a change in the phase envelope of the fluid relative to the bulk state. At the present time, it remains an important research question to sort out the various causes of anomalous reservoir fluid production in shales. The Hamdi et al. (2015) approach, while providing a rigorous method for tuning the in-situ fluid equation-of-state and characterizing the in-situ fluid, does not, at the present time, help to distinguish these controlling factors. These questions must be addressed to help with the understanding of how to optimize both primary and enhanced recovery of hydrocarbons in such fluid systems. Indeed, should liquid hydrocarbon mobility be limited, then enhanced recovery must be considered in the future – however, an understanding of in-situ fluid (hydrocarbon, water and injected fluid) is of paramount importance for proper prediction of enhanced recovery in these systems.

55

ACCEPTED MANUSCRIPT 16

4500

Observed Data

A

4000

14

3500 3000 2500 2000 1500

10 8 6 4 2

500

0 0

50

100

150

200

250

300

350

400

0

50

100

150

200

14

Observed Data

400000 350000 300000 250000 200000 150000 100000

10 8 6 4 2

50000 0

0 100

150

200

250

300

350

400

0

50

Time, Day

400

100

150

200

250

Matched Data

300

350

400

Time, Day

1

0.8

Matched Data

0.7 0.6 0.5 0.4 0.3 0.2 0.1

Seperator Oil Composition

E

0.8

Observed Data

Observed Data

M AN U

0.9

350

Observed Data

D

12

Matched Data

SC

C

Water Rate, STB/Day

450000

50

300

RI PT

500000

0

250

Time, Day

Time, Day

Gas Rate, SCF/Day

Matched Data

12

1000

0

Seperator Gas Composition

Observed Data

B

Matched Data

Oil Rate, STB/Day

Well Bottomhole Pressure, psi

5000

0.7

Matched Data

F

0.6 0.5 0.4 0.3 0.2 0.1

0

0 C1

C2

C3

iC4

nC4

iC5

nC5

Component

nC6

C7+

C1

C2

C3

iC4

nC4

iC5

nC5

nC6

C7+

Component

TE D

Fig. 56. Best-case history matching results for pressure (A), oil (B), gas (C) and water (D) and rates, and separator gas (E) and oil (F) compositions. The red curves are the observed/measured data and the black curves are the model matches. From Hamdi et al. (2015).

AC C

EP

An important aspect of the Hamdi et al. (2015) study is the integration of various reservoir/hydraulic fracture characterization methods to help constrain some of the model inputs for this difficult problem. For example, rate-transient analysis of the transient linear flow period observed for the studied well was performed using the techniques of Behmanesh et al. (2015) to correct for the effects of multi-phase flow in the reservoir as the well flowed below dewpoint pressure. The result was an initial interpretation of the dominant flow period to be transient linear flow (using RTA flow-regime diagnostics, see Fig. 24) and a preliminary estimate of individual-stage fracture half-length (from RTA straight-line analysis, see Fig. 24) used as a constraint in the simulation history-match. Further, DFIT data from the field was used to constrain initial pressure estimates and initial reservoir permeability. Although not performed as part of this study, additional constraints on model inputs such as permeability of the matrix and fractures, stress-sensitivity of those properties, and relative permeability, could have been applied through use of core data (see Section 4.1). A more recent study by Kanfar and Clarkson (2016, in preparation) provides an even better example of the integration of various hydraulic fracture/reservoir characterization for the purpose of constraining history-matching of liquid-rich shale reservoirs, following the workflow originally suggested by Clarkson et al. (2012a) (see Fig. 2). In that work, flowback and microseismic data were further included to constrain the model inputs. The study provides a rigorous approach for modeling flowback as well as on-line production using numerical simulation.

5. Conclusions Although shale gas reservoirs have been exploited for several decades in North America, there remain questions regarding the fundamentals of fluid flow and storage. As a result, characterization methods designed to extract reservoir and hydraulic fracture properties continue to evolve as our understanding of shale reservoirs advances. While multi-fractured horizontal wells have provided a mechanism to exploit shale gas reservoirs, they are difficult to evaluate. Reservoir samples are mostly

56

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

limited to small volumes of drill cuttings collected at various intervals along the well. Because MFHWs can be very long (1000+m), it is expected that reservoir properties change significantly along the length of the well. Therefore, reservoir sample analysis techniques must be adapted specifically to obtain key fluid storage and transport properties, as well as rock compositional and mechanical properties, at sampling points along the well – this is necessary not only to guide stimulation point location and design, but to model flow from these wells. Advances have been made in cuttings analysis, as outlined herein, but quantitative analysis remains challenging. Reservoir sample analysis, however, is limited to small scales. Processes that occur at these scales are necessary to quantify, but feed into flow systems that occur at larger scales such as natural fractures, or induced hydraulic fractures. Welltest techniques, which sample a larger reservoir volume including natural fractures, such as diagnostic fracture injection tests, may be adapted to collect reservoir and stress information along the well, but challenges remain in accelerating the tests so that they can be performed quickly and cheaply to enable multiple tests to be executed. Production analysis techniques, such as rate-transient analysis, sample an even greater reservoir volume, which includes the hydraulic fracture network. However, these techniques need to account for the complexities of shale gas reservoirs such as multi-phase flow during flowback and online production, non-static permeability, pore confinement effects etc. Numerical simulation techniques, which are the most rigorous methods for analyzing shale well production, also continue to evolve with our understanding of shale gas reservoir properties. Efforts are now ongoing to not only quantify reservoir and hydraulic fracture properties using simulation, but also in-situ fluid properties. As exploitation of shale reservoirs has shifted from dry gas systems to liquid-rich (gas condensate, volatile oil and black oil) systems, there has become an increased need to evaluate in-situ fluid properties – however, conventional lab test techniques, which do not consider pore confinement effects, may be in error, and conventional methods to develop an in-situ fluid model may also be in error. Because fluid production is a function of not only in-situ fluid composition, but also reservoir, hydraulic fracture, and well operating conditions, fluid properties must be included in the history-matching exercise, significantly complicating this process. Nonetheless, these steps are necessary so that primary recovery, and eventually enhanced recovery processes, may be confidently forecasted. The current work has reviewed current advances in reservoir and hydraulic fracture characterization of shale gas reservoirs at multiple scales, from fine-scale reservoir sample analysis to well-scale evaluation. However, future work should be dedicated to properly integrating the analyses for complete reservoir and hydraulic fracture description.

Acknowledgements

Nomenclature

Contacted gas in place Contacted oil in place Distributed acoustic sensing Diagnostic fracture injection test Distributed temperature sensing Flow/buildup (conventional well-test) Gas oil ratio Gas water ratio Inorganic matter Instantaneous shut-in pressure Original gas in place Original oil in place Organic matter Production logging tool Pore size distribution Pore throat distribution Simplified local density Stimulated reservoir volume Total organic carbon

AC C

CGIP COIP DAS DFIT DTS F/BU GOR GWR IOM ISIP OGIP OOIP OM PLT PSD PTD SLD SRV TOC

EP

Abbreviations

TE D

Clarkson would like to thank Alberta Innovates Technology Futures, Encana and Shell for supporting his Chair in Unconventional Gas and Light Oil Research, Department of Geoscience, University of Calgary. All authors would like to thank the sponsors of Tight Oil Consortium (www.tightoilconsortium.com) for their ongoing support of our research. Finally, partial support for this research has been provided through the NSERC CRD and Discovery Grant programs.

Field Variables

57

ACCEPTED MANUSCRIPT Fluid-fluid attraction parameter Fluid-fluid repulsive parameter or gas slippage factor Areal extent of gas reservoir, acres or ft2 Surface area contacted by hydraulic fracture, ft2 Surface area of the slit pore, ft2/lb rock Gas formation volume factor, cf/scf Initial gas formation volume factor, cf/scf or bbl/scf

Bw Cm,eq Cf,eq ct d E ED ES f f ff f bulk fc f cp

Water formation volume factor RBBL/ STB Material balance parameter for matrix Material balance parameter for fracture Total compressibility, psi-1 Effective pore throat diameter, nm Young’s modulus, psi Dynamic Young’s modulus, psi Static Young’s modulus, psi Fugacity, psi Fluid fugacity, psi Bulk gas fugacity, psi Correction factor, dimensionless Drawdown correction for constant flowing pressure case, dimensionless

Fc G Gc Gp Ga,i Gf,i Gt,i h k k∞ KB Kn krg kro krw khf khsys L m mrock m(P) m(Pi) m(Pwf) mP mCP M n nabs nGibbs Pc Pcp P Pbreakthrough Pc Pi PL Pmean

Fracture conductivity, md-ft Gas-in-place, scf Gas content, scf/ton Cumulative gas production, scf Initial adsorbed gas-in-place, scf Initial free gas-in-place, scf Total initial gas-in-place, scf Formation thickness, ft Permeability or apparent gas permeability (Eq. (25)), md Slip-corrected permeability, md Boltzmann constant, J/K Knudsen number, dimensionless Relative permeability to gas, dimensionless Relative permeability to oil, dimensionless Relative permeability to water, dimensionless Permeability-thickness product of the fractures, md-ft Permeability-thickness product of the system (combined fractures and matrix), md-ft Slit width, nm Cementation factor, dimensionless Rock mass, lb rock Real gas pseudopressure, psi2/cp Real gas pseudopressure at initial pressure, psi2/cp Real gas pseudopressure at flowing pressure, psi2/cp Real gas pseudopressure at average pressure, psi2/cp Slope of square-root time plot Gas molecular weight, lb/lbmol or kg/mol Saturation exponent, dimensionless Absolute adsoption, lbmole/lb rock Gibbs adsoption, lbmole/lb rock Critical pressure, psia Critical pressure of confined gas, psia Pressure, psia Breakthrough pressure, or pressure that formation fluids breakthrough to the fracture duing flowback, psia Capillary pressure, psia, kPa or MPa Initial pressure, psia Langmuir pressure, psia Mean pore pressure used in permeability experiment, psia Fuid–solid interaction energy parameter, K Vitrinite reflectance, % reflectance in oil

εfs/kB

Ro

SC

M AN U

TE D

EP

AC C

()

RI PT

a b A Ac ASlit Bg Bgi

58

ACCEPTED MANUSCRIPT Hydraulic radius of flow, ft Universal gas constant, 10.73 psi.ft3/(lbmole.R) Pore throat aperture at 35% cumulative pore volume (mercury saturation in capillary pressure test), microns Gas production (surface) flowrate, MSCF/D Adsorbed phase saturation, dimensionless Initial adsorbed phase saturation, dimensionless Gas saturation, dimensionless Oil saturation, dimensionless Water saturation, dimensionless Initial water saturation, dimensionless Material balance pseudotime, days Temperature, K Critical temperature, K Critical temperature of confined gas, K Adsorbed phase volume, ft3 Reservoir bulk volume, ft3 Pore volume, ft3 or P-wave velocity, m/s Langmuir volume, scf/ton S-wave velocity, m/s

We

Water encroachment into the gas formation, bbl

Wp

Cumulative water produced at surface, STB

xf z Z

Fracture half length, ft Distance from the surface of slit wall, ft Gas compressibility factor, dimensionless

Dimensionless fracture conductivity Dimensionless gas pseudo-pressure

Greek Variables

v vD vS ξ

λ

ω ϕ

φc φm

ρ(z) ρa ρb ρbulk ρm σff ψ

δ

Energy parameter of fluid-solid molecular interaction Gas viscosity, cp Poisson’s Ratio, dimensionless Dynamic Poisson’s Ratio, dimensionless Static Poisson’s Ratio, dimensionless Boltzman variable Mean free path of gas molecules, ft

EP

µg

Material balance parameter for fracture to matrix storage ratio Total porosity fraction, dimensionless Corrected porosity, fraction Matrix porosity, fraction Fluid molar density, lbmol/ft3 Average adsorbed phase density, lbmol/ft3 Rock bulk density, g/cm3 or lb/ft3 Bulk gas molar density, lbmol/ft3 Matrix density, g/cm3 Molecular diameter of the adsorbate, ft Gas pseudo-pressure, psi2/cp Gas collision diameter, nm

AC C

εfs

TE D

FcD ψ

M AN U

Dimensionless Variables

SC

RI PT

rh R rp35 qg (or q) Sa Sai Sg So Sw Swi tca T Tc Tcp Va Vb Vp VL Vs

59

ACCEPTED MANUSCRIPT References

AC C

EP

TE D

M AN U

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RI PT

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FACULTY OF SCIENCE

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Christopher R. Clarkson, PhD, PEng Professor and AITF Shell/Encana Chair in Unconventional Gas and Light Oil Research Department of Geoscience 2500 University Drive NW Calgary, AB T2N 1N4 | CANADA T +1.403.220.6445 C +1.403.918.2379 E [email protected] ucalgary.ca

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An overview of shale gas storage and transport mechanisms is provided Challenges associated with evaluating key reservoir and hydraulic fracture properties are discussed Recent advances by the authors in the area of shale gas reservoir and hydraulic fracture characterization are highlighted Multiple scales, from fine-scale reservoir sample analysis to well-scale evaluation, are discussed

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Highlights of “Nanopores to Megafractures: Current Challenges and Methods for Shale Gas Reservoir and Hydraulic Fracture Characterization”: