NMR application in unconventional shale reservoirs – A new porous media research frontier

Accepted Manuscript NMR application in unconventional shale reservoirs – A new porous media research frontier Yi-Qiao Song, Ravinath Kausik PII: DOI: Reference:

S0079-6565(19)30015-9 https://doi.org/10.1016/j.pnmrs.2019.03.002 JPNMRS 1472

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Progress in Nuclear Magnetic Resonance Spectroscopy

Received Date: Accepted Date:

26 March 2019 28 March 2019

Please cite this article as: Y-Q. Song, R. Kausik, NMR application in unconventional shale reservoirs – A new porous media research frontier, Progress in Nuclear Magnetic Resonance Spectroscopy (2019), doi: https://doi.org/ 10.1016/j.pnmrs.2019.03.002

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NMR application in unconventional shale reservoirs – A new porous media research frontier Yi-Qiao Song∗, Ravinath Kausik Schlumberger-Doll Research, 1 Hampshire street, Cambridge MA 02139, USA Edited by Geoffrey Bodenhausen and Dominique Massiot

Abstract Unconventional shale reservoirs have greatly contributed to the recent surge in petroleum production in the United States and are expected to lead the US oil production to a historical high in 2018. The complexity of the rocks and fluids in these reservoirs presents a significant challenge to the traditional approaches to the evaluation of geological formations due to the low porosity, permeability, complex lithology and fluid composition. NMR has emerged as the key measurement for evaluating these reservoirs, for quantifying their petrophysical parameters, fluid properties, and determining productivity. Measurement of the T1 /T2 ratio by 2D NMR has been found to be critical for identifying the fluid composition of kerogen, bitumen, light/heavy oils, gases and brine in these formations. This paper will first provide a brief review of the theories of relaxation, measurement methods, and data inversion techniques and then will discuss several examples of applications of these NMR methods for understanding various aspects of the unconventional reservoirs. At the end, we will briefly discuss a few other topics, which are still in their developmental stages, such as solid state NMR, and their potential applications for shale rock evaluation. Key words: Multi-dimensional NMR, relaxation and diffusion, T1 -T2 correlation maps Well-logging, Frequency dependence of relaxation rates

∗

Corresponding author Email address: [email protected] (Yi-Qiao Song)

Preprint submitted to Progress of NMR Spectroscopy

March 26, 2019

Contents 1 Introduction

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2 Relaxation and diffusion techniques for rock study 6 2.1 Multidimensional NMR of relaxation and diffusion . . . . . . . 7 2.2 Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 NMR well-logging . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 NMR of gas shales 3.1 Relaxation mechanism of bulk methane gas . . . . . . . 3.2 Methane relaxation in porous media . . . . . . . . . . . 3.3 NMR relaxation of methane in shale . . . . . . . . . . 3.4 Methane gas diffusion in porous media . . . . . . . . . 3.5 Transport of methane gas in gas shales . . . . . . . . . 3.6 Methane gas storage and hydrogen index in nanoporous 3.7 Section summary . . . . . . . . . . . . . . . . . . . . . 4 NMR of tight oil shale rocks 4.1 T2 distributions . . . . . . . . . . . . . . . . . . . 4.2 Low field 2D T1 -T2 map of fluids . . . . . . . . . . 4.3 Examples . . . . . . . . . . . . . . . . . . . . . . 4.4 High field NMR T1 -T2 maps . . . . . . . . . . . . 4.5 Diffusion NMR and D-T2 map . . . . . . . . . . . 4.6 Reservoir quality (RQ) . . . . . . . . . . . . . . . 4.7 Solid state NMR for shale rocks . . . . . . . . . . 4.8 NMR instrumentation: frequencies and echo times 4.9 Section summary . . . . . . . . . . . . . . . . . . 5 Conclusion

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Glossary, acronym and unit Gas shale Shale rocks containing natural gas Play, shale play A prospective oil and gas reservoir consisting of organic mudstones Reservoir A geological storage space for fluids such as water, hydrocarbon liquids or gas Reservoir quality, RQ The hydrocarbon storage and production capacity of a reservoir RPI Reservoir producibility index TGIP Total gas in place Tight oil shale Shale rocks containing light crude oil Thermal Maturity The extent of conversion of sedimentary organic matters into petroleum through cracking. Often measured by the reflectance of light from the surface of vitrinite particles in sedimentary rocks

Asphaltene Molecular substances found in crude oils which are soluble in aromatic solvents like benzene and toluene but insoluble in lighter paraffin like pentane and hexane Bitumen A viscous crude oil that is soluble in common organic solvents Brine A high-concentration aqueous solution of salt, often found in subsurface formations Dead oil A crude oil without its volatile components Hydrogen index The density of hydrogen of a sample relative to that of water at STP Kerogen Organic materials found in oil shale that is insoluble on common organic solvents Maltene The remaining part of crude oils after asphaltenes are removed 3

Formation evaluation The acquisition of data and quantification of parameters to characterize subsurface geological formations needed for drilling, production, reservoir characterization and reservoir engineering Well logging Practice of making measurements of geological formations penetrated by a borehole CMR+, MR Scanner Names of Schlumberger NMR well-logging tools CPMG A multiple pulse NMR sequence developed by Carr, Purcell, Meiboom and Gill, used extensively in low field NMR IRCPMG An NMR pulse sequence which combines inversion-recovery with CPMG to measure T1 –T2 correlations MD Multi-dimension, or multi-dimensional SRCPMG An NMR pulse sequence which combines saturation-recovery with CPMG to measure T1 –T2 correlations

Barrel (bbl) A unit of volume often used in petroleum industry, 1 bbl = 42 gallons = 158.987 L Darcy, mD A unit of permeability, 1 D = 1000 mD = 0.9869233 µm2 Pa, psi Units of pressure, 1 psi = 6894 Pa Poise, cP, Pa·s Units of dynamic viscosity, 1 cP = 10−2 P = 10−3 Pa·s

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1. Introduction The term “unconventional reservoirs” covers a wide range of hydrocarbonbearing formations that require stimulation for the economic production of hydrocarbons. Such fields include gas shales, tight oil shales, tight-gas sandstones, gas hydrates, oil shale formations, heavy oil sandstones etc. In this paper, we will focus on the first two examples namely hydrocarbon production directly from organic-rich mudstones: gas shales and tight oil shales. The United States is rich in unconventional oil and gas resources that have contributed tremendously to the US oil and gas production. For example, EIA estimates that production from unconventional tight oil reservoirs makes up 54% of the total of 9.3 million barrels per day (b/d) in 2017 and to lead to 10.6 million b/d in 2018, the highest annual average production level, surpassing the previous record of 9.6 million b/d set in 1970. This trend is expected to continue to reach ∼12 million b/d in the early 2040s [1]. Organic mudstones (commonly called “shales”) are specifically defined as fine-grained sedimentary rocks with an elevated total organic carbon (TOC) content (above 2%). They are typically composed of a clay-size material (< 4 µm) and variable amounts of silt-size floating grains (up to ∼62.5 µm) of both biogenic and detrital origins [2, 3]. Shale rocks are highly heterogeneous with a complex inorganic mineralogy that may vary vertically and laterally at different scales [4, 5] due to their depositional environments, stacking patterns and sequence stratigraphy [6, 7, 8, 9]. Unlike traditional oil and gas reservoirs where the hydrocarbons migrate from the source rocks into reservoir rocks with a higher porosity and permeability, the shale reservoir (often called shale play) is itself the source rock with low porosity and low permeability making it challenging for exploration and production. An additional challenge is the presence of complex fluids and organic solids in shales, such as kerogen, bitumen, and oil in the case of tight oil reservoirs or gas in the case of tight gas shales. Kerogen refers to the solid, insoluble and immobile organic matter [10] and can be isolated in the laboratory by the removal of all other organic matter using solvent extraction, and of all mineral matter by acid demineralization. Bitumen refers to the soluble but ultra-high viscosity organic matter that is created early in the maturation process before oil generation. The hydrocarbons in shale rocks are oils in lower maturity shale rocks existing in nm-sized pores in the organic kerogen or in the inorganic mineral matrix, or gases in free or adsorbed phases in more mature shales, mainly in the kerogen nanopores. The other 5

fluid present in shales is water, in either a bound phase associated with the clays or in a mobile phase in the inorganic pores. Many techniques are being applied to the study of shale rocks as a part of the formation evaluation process in petroleum industry. For example, nuclear physics based techniques (such as neutron scattering, γ-ray and X-ray) are widely used to measure mineral properties, resistivity and low-frequency electro-magnetic measurements to identify and image water/oil distributions, and acoustic measurement to assess mechanical properties of rocks [11]. NMR has proved vital for the characterization of fluid compositions of shale rocks in both downhole logging and laboratory studies. The main applications include the measurement of the total porosity, the quantification of the kerogen volumes, the identification of fluids such as water or hydrocarbons, determining the confining environments like organic kerogen pores or inorganic mineral pores and thereby determining bound versus mobile fluid volumes. The unique ability of NMR to provide these quantities has made it a crucial and routinely-applied logging and laboratory analysis tool for shale rocks. Extensive work has been reported about NMR on shale rocks from novel technical developments to field applications. This paper is intended to introduce the fundamental challenges in shale gas and tight oil exploration and the NMR methodologies that have been developed to address them. Section 2 will review the relevant NMR techniques and data inversion methods that will be used in the later sections. Section 3 will focus on the NMR behavior of natural gas in nanoporous gas shale rocks, gas adsorption and transport, and discuss the applications of such understandings. Section 4 will discuss the challenges of fluid characterization for tight oil shale rocks and the quantification of the different compositions by 2D NMR experiments leading to the definition of a reservoir producibility index. Low and high field NMR experiments will be discussed. This paper draws examples mostly from the authors’ own work and related collaborations to highlight ideas that have led to practical applications. A comprehensive review of this field would be highly desirable and timely, however, it would require a more extensive effort. 2. Relaxation and diffusion techniques for rock study This section reviews the fundamentals of the most relevant NMR relaxometry and diffusometry techniques used in the unconventional reservoirs and

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the associated data inversion techniques. Non-destructive NMR measurements on native (and in-situ) samples yield fluid saturations, wettability, and fluid types and have become an indispensable technique for the understanding of petroleum reservoirs throughout the formation evaluation process For example, the dipolar contribution to relaxation is sensitive to the local fluid viscosity and the molecular size of the constituents. As a result, relaxation and diffusion measurements can yield physical properties of fluids and distributions of molecular sizes [12]. When a fluid is confined to a pore, fluid-rock interactions cause additional relaxation and the diffusive dynamics is modified. These effects can be quantified by NMR in order to determine pore sizes and fluid-rock wetting conditions [13]. Washburn has provided a recent review of relevant interactions in porous rocks such as shales [14]. 2.1. Multidimensional NMR of relaxation and diffusion Conventional 2D NMR spectroscopy [15] is usually performed by measuring a signal matrix as a function of two independent time variables. Then, a 2D Fourier transform is performed with respect to the two variables to obtain a 2D spectrum as a function of the two corresponding frequencies. The 2D NMR methods to measure relaxation and diffusion (e.g., see our recent review [16]) is similar conceptually in that the signal matrix is measured as a function of two variables. However, since relaxation and diffusion often cause the spin magnetization to decay exponentially, the data matrix is analyzed by Laplace inversion, instead of Fourier transformation. The results of such experiments are 2D joint probability distributions of pairs of T1 , diffusion or T2 parameters ( D-T2 , D-T1 , or T2 -T2 ). Several works have been reported many years ago [17, 18, 19], however, applications have not been widespread due to the difficulty in data analysis using conventional Laplace inversion technique, for example as described by Provencher [20]. The Fast Laplace Inversion (FLI) algorithm discovered in 2000 [21, 22] enabled 2D Laplace inversions using contemporary computers. One-dimensional relaxation and diffusion techniques measure one parameter (e.g. T1 , T2 , T1ρ , or D) in a single experiment. For mixtures, the total signal, s(t), (which is proportional to the spin magnetization M ) will result from a superposition of all components resulting in multi-exponential decays. For example, the signal can be obtained by integrating over T2 : Z s(t) = dT2 F(T2 ) exp(−t/T2 ), (1) 7

where F(T2 ) is the T2 distribution or T2 spectrum. Note that in a complex sample, such as a porous rock saturated with a mixture of water and oil, the relaxation time constant of water and oil may be similar since T2 can be affected by the molecular size of oil and surface relaxation independently. The multi-dimensional (MD) NMR technique improves the resolution of signals by measuring correlation functions of two or more parameters, such as T1 -T2 correlation, D-T2 , T1 -T2 -D, D-D, etc. The pulse sequence for a T1 -T2 experiment can be the following: π Recycle Delay − π − ta − − te /2 − [π − te ]N . (2) | {z } |2 {z } first part second part where data acquisition occurs during the echo spacing te after each π pulse, N is the echo number and the sequence is called IRCPMG since it combines inversion-recovery (for the measurement of T1 ) and the CPMG to measure the T2 decay. The total signal as a function of the two time variables ta and tb = N te is: ZZ s(ta , tb ) = (1 − 2e−ta /T1 )e−tb /T2 × F(T1 , T2 )dT1 dT2 , (3) where F(T1 , T2 ) is the probability density of molecules with specific T1 and T2 . To obtain F from the data matrix s, a 2D Laplace inversion is needed. Typically, for every value of ta , data at every value of tb can be acquired over the full range of tb is acquired, although not necessarily. Such a concept can be extended to multiple dimensions by varying two or more experimental parameters and acquiring NMR signals accordingly as a full or partial matrix. Laplace inversion of such MD matrices is used to obtain multi-parameter correlation functions. Other 2D experiments have been designed and tested, such as T2 -T2 correlations, diffusion-relaxation correlations, e.g. D-T2 and D-T1 , and diffusiondiffusion correlations [23, 16]. Similar to the relaxation-relaxation correlations, diffusion can be used for the both dimensions of the 2D experiments. Such experiments can be performed with pulsed field gradients or static gradients as well as higher dimensional extension of these experiments. 2.2. Inversion Integral equations, such as Eq. 1 can be written in a matrix form S = KF, 8

(4)

where S is the signal vector, F the vector of the T2 spectrum, and K is the kernel matrix. For a 1D experiment, such as Eq. 1, the kernel matrix is defined as Kij = exp(−ti /T2j ), (5) where ti is the i-th component in the list of echo times, and T2j the j-th component of the T2 list. For CPMG experiments, the echo time (t) is incremented linearly, however, t can vary in an arbitrary fashion. The T2 list is often a list of values equally spaced in log(T2 ) domain, but other lists can also be use (e.g. linear in T2 ). Many properties of the experiments are reflected in the quality of the kernel matrix [24]. The difficulty of this approach is that the kernel matrix can be very large and the calculation time-consuming. For a 2D experiment, such as Eq. 2, F is a 2D matrix. However, the signal equation can still be written in the same format as Eq. 4 by rewriting both F and S as 1D vectors. For certain experiments, a more condensed matrix equation can be used, S = K1T F K2 ,

(6)

where all matrices, S, F , K1 and K2 are 2D matrices (K1T being the transposed matrix of K1 ). Here, K1ij = 1−2 exp(−tai /T1j ), and K2pq = exp(−tbp /T2q ). The advantage of this approach is that all the matrices can be easily handled in contemporary desktop/laptop computers [21]. The solution for such inversion problems can be obtained in two general approaches. One is the regularization method, as described for example in Ref. [21]. The essence is to obtain a solution F that fits the data, and furthermore satisfies other constraints. In Ref. [21], the solution is obtained by minimizing ||M − KF ||2 + α||F ||2 (7) where α is called regularization parameter, and || · || is the Frobenius norm of the matrix. The first term is the difference between the data and fit, the second term is called regularization term. The presence of the regularization term produces slightly broader peaks in F and a more stable solution. Many related methods have been used for such inversions including maximum entropy method, and a brief review can be found in Ref. [16]. Another general method is to find an ensemble of solutions that fit the signal equation, e.g. Eq. 4 within the noise of the experiment, F ≡ {Fi } where 9

each Fi is a solution [25, 26]. Using the ensemble of solutions, any properties of the spectrum can be obtained as well as the statistical uncertainty. 2.3. NMR well-logging NMR well-logging tools have been widely used for almost three decades [27, 28]. They are designed with permanent magnets to provide the B0 fields of up to 0.05 T corresponding to a 1 H Larmor frequency of about 2 MHz. The sensitive spot is outside the tool body and thus it is sometimes called an inside-out NMR sensor [29]. With such a geometry, the B0 field is very inhomogeneous and the CPMG-type sequences [30, 31, 32] are the most efficient methods to acquire NMR signals. The current tools (such as CMR+ and MR Scanner) comprise a programmable pulse sequencer that can execute many pulse sequences, such as the 2D measurements using saturationrecovery-CPMG (SRCPMG) for T1 -T2 , diffusion-editing CPMG for D-T2 and 3D sequences for T1 -T2 -D maps. One unique challenge of performing NMR logging is that the tool continuously move during the pulse sequences. For example, the typical wireline logging speed is 550 m/hr (1800 ft/hr ). For a CMR+ tool with a coil length of 0.17 m (6 inches), the coil will move out of the regime of the initial π/2 pulse excitation of CPMG sequence during the acquisition of the echo train. As a result, NMR logging is typically performed much slower, such as at 180 m/hr (600 ft/hr) or lower. The slow speed is a significant drawback of NMR well-logging and in particular for performing 2D sequences. For shale rocks, due to the presence of the solid and highly viscous organic components, a significant amount of the signals exhibits short T2 relaxation times, such as down to less than 20 µs for the kerogens or ranging from a few tens of micro seconds to a millisecond for the bitumens [33]. As a result, short echo times are highly desirable in order to observe the bitumen signals. For example, a typical CMR+ sequence for shales uses a short echo time of 200 µs. 3. NMR of gas shales Gas shales are characterized by very low porosity and ultra low permeabilities. Their porosity is dominated by nanometer-scale pores in the organic kerogen that restricts diffusional motion, in addition to enhanced surface relaxation. At high pressures, the gas exists in an adsorbed phase on the pore

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surface and as a free gas phase in the pore interior. Thus, relaxation and diffusion properties of the gas are greatly affected. This section will review the NMR behavior of methane gas in bulk and porous media for the several relaxation and diffusion experiments covered in Section 2, and the adsorption dynamics in nanoporous materials. In addition, we discuss how the understanding of NMR of natural gas has contributed to practical applications. 3.1. Relaxation mechanism of bulk methane gas The main component of natural gas is methane whose critical pressure and temperature are 4.6 MPa (667 psi) and 190.45 K (-82.75 ◦ C), respectively. In subsurface gas reservoirs, methane is supercritical and therefore shows clear deviation from the behavior of an ideal gas. The relaxation times of bulk methane gas are dominated by the spin rotation mechanism at low to moderate (e.g., 69 MPa or 10 kpsi) pressures while at high pressures the inter-molecular dipolar interactions could start playing a role [34] due to the increased molecular collisions. For bulk methane gas (which is in the motional narrowing limit), the relaxation rates due to spin rotation is expressed as [35]: 1 T1SR

=

1 T2SR

=

2(C 2 + 2C⊥2 )I1 τF kB T, 32

(8)

where τF is the correlation time of rotation, kB the Boltzmann’s constant, I1 the moment of inertia of the spherical methane molecule, T the temperature, C and C⊥ are the principal components of the spin rotation tensor [35]. The correlation time τF is given by τF = 3I1 D/4a2 kB T , where D = kB T /6πaη, η is the viscosity and a the radius of the molecule. It is important to note that the dependence of temperature, viscosity and molecular size is different for spin rotation from those that pertain to intramolecular dipole-dipole interactions. The physical properties of methane gas, including the variation of relaxation times with pressure and temperature, have been well characterized experimentally [37, 36]. Figure 1 shows the experimental T2 times of bulk methane for pressures of 7–35 MPa (1–5 kpsi). The right panel of Figure 1 shows the peak values of T2 as a function of the pressure. An empirical relation between the spin lattice relaxation times and the methane density ρ has been obtained [38], T1SR = cρT 3/2 , 11

(9)

Figure 1: T2 distributions of bulk methane (left panel) and peak T2 value versus pressure (right panel). Both the T2 peak amplitude and intensity of the T2 distributions increase with pressure. Data from Ref. [36] with permission.

where ρ is the density in g/cm3 , T the temperature in Kelvin and the constant c = 1.57 × 105 cm3 K 3/2 sg −1 estimated for T1 data of methane gas for pressures up to 20 MPa. Figure 2 compares our measured T1 with Eq. 9 for a broader pressure range 3.4–68.9 MPa [39] showing excellent agreement. This agreement demonstrates that spin relaxation is indeed dominated by spin rotation throughout the entire pressure range. 3.2. Methane relaxation in porous media When the methane gas is confined to nm-sized pores hosted in the kerogen matrix of gas shales, the dominant mechanism of relaxation is no longer spin rotation but surface relaxation due to dipole interactions. The principal fraction of the nanoscale porosity in gas shales exists in the organic-rich kerogen material. The rotational diffusion of the gas molecules is slowed down in the nano-confinements in kerogen resulting in dipolar interactions strongly enhancing the relaxation. Another phenomenon that occurs for a confined gas at at pressures of tens of MPa (several thousand psi), is that a large fraction of the gas molecules are adsorbed on pore surfaces [40]. Adsorption leads to a reduction of the relaxation times and can be effectively considered a form of a wetting phenomenon, where the adsorbed gas is the wetting phase. A gas molecule in a kerogen nanopore is adsorbed or free at any given instant of time. However, the two populations cannot be differentiated be12

10 9

T1exp Empirical

8 7

T1 (s)

6 5 4 3 2 1 0 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Density (g/cm3)

Figure 2: Longitudinal relaxation times (dots, T1LM ) of bulk methane gas at various densities at 295.2 K (22 ◦ C). The solid line corresponds to the empirical description of T1 relaxation times, Eq. 9. Data from Ref. [39] with permission.

cause they are in fast exchange with each other with respect to the time scale of the NMR relaxation times. The resulting relaxation can be expressed as 1 1−  = + , Ti Ti,free Ti,adsorbed

(10)

where  is the fraction of molecules in the adsorbed phase and 1 −  the fraction of non-adsorbed molecules in the pore interior, and i is 1 or 2 for T1 or T2 , respectively. The net relaxation times inside the pores are dominated by surface relaxation rates and can also be generally expressed as 1 1 ρi Sp = + , Ti Ti,bulk Vp

(11)

where ρ1 and ρ2 denote the surface relaxivity for the T1 and T2 processes, respectively, and Sp /Vp the surface-to-volume ratio. 3.3. NMR relaxation of methane in shale We next discuss the relaxation dynamics of methane in gas shale samples. The gas shale samples were prepared in three different states: a brine13

saturated irreducible state, a 37.5-MPa (5 kpsi) methane saturated state of the dried sample, and a brine-saturated irreducible state that is then saturated with 37.5 MPa (5 kpsi) methane. The T2 relaxation distribution were measured at 30 ◦ C and shown in Figure 3. The irreducible state was reached by centrifuging the sample at 2.4 MPa (340 psi) after brine saturation. Further experimental details are provided in Ref. [36]. The T2 distributions of the residual brine are shown in Figure 3 as a peak at T2 ∼ 1 ms. The T2 distribution with methane gas shows a large peak at T2 ∼1 s, from the bulk gas outside the sample in the annulus dead volume between the sample and the sample holder. The peak around T2 ∼ 10 ms is due to methane gas inside the pores of the shale rocks.

Figure 3: T2 distributions in brine (irreducible) and methane-saturated states for two Haynesville gas shale samples (#1 and #2). The large peaks at T2 ∼ 1 s are due to the methane gas outside the sample. Brine (irreducible state) and methane saturated states have T2 distributions peaking about 1 and 10 ms and with overlap between the distributions. For the methane saturated samples with brine (irreducible state), note that the signals may not be well separated in the T2 dimension. Data from Ref. [36] with permission.

The identification of a methane gas signal in shale rocks is important to define the optimal measurement protocol for downhole logging. Since we found that the methane gas T2 relaxation times inside shale rocks are approximately 10 ms, the NMR well-logging program was then adjusted to focus on the fast relaxing components [36]. For example, a typical logging program includes a CPMG scan with a long recovery time (e.g., 8 s) in anticipation that light crude oils may have long T1 . For shale gas, it is much more efficient to use many scans with short recovery times, often called burst acquisitions 14

[41] in order to enhance the contributions of signals with short T1 . Such an acquisition mode has become standard practice in shale applications [41, 42]. Furthermore, as shown in Figure 3 (right panel), signals from methane and bound water may sometimes overlap and thus other measurements (e.g., resistivity or dielectric constant measurements) might be needed to quantify water and gas contents separately. 3.4. Methane gas diffusion in porous media The diffusion dynamics for gas in gas shales is different from the bulk gas behavior and have been investigated using NMR diffusometry [36] which allows to differentiate the gas in gas shale that exists in two phases, one absorbed on the pore surface and the other as free gas in the pore interiors. The net diffusion coefficients result from contributions in both phases and are modulated by their exchange. The free gas in the pores can exhibit a diffusion behavior in the short or long time-limit, depending on the encoding times used in NMR diffusion experiments. The free gas diffusion can also be dominated by Knudsen or bulk diffusion characteristics depending upon whether the mean free path is larger or smaller than the pore diameters. The diffusion coefficient of bulk methane gas is about 6 × 10−8 m2 /s at 30◦ C and 35.7 MPa (5 kpsi). This high diffusion coefficients of bulk methane gas can be exploited to separate gas from other liquids with lower diffusivities such as oil and water in 2D-NMR experiments [12]. At short diffusion times, i.e. when the diffusion length scale is much smaller than the pore size scale, the diffusion coefficient is known to be reduced from its bulk value D0 by an amount proportional to Sp /Vp [43, 44]: √   4 D0 tSp D(t) ≈ D0 1 − √ . (12) 9 π Vp At longer diffusion times, the complex geometry of pores can be explored by the diffusion process. For example, when the gas molecules diffuse across a closed pore space in the measurement interval, the root-mean-square displacements (< x2 (t) >) in the long time limit (when spins diffuse through distances larger than the dimensions of the pores) reach a constant value proportional to the pore radius (R): < x2 (t) >→ 12R2 /5, for spherical pores. In the case of a system with connected pores, the diffusion coefficient in the long time limit approaches the tortuosity limit given by: D(t → ∞) ≈ 15

D0 , T

(13)

Figure 4: Schematic 2D-NMR plot for gas inside gas shales shows reduced diffusion coefficients and relaxation times as a result of adsorption, surface relaxation and restricted diffusion in the small pore sizes. Figure from Ref. [36] with permission.

where T is called the tortuosity of the medium and is related to the formation factor (FF ) and the porosity φ by T = FF · φ. Therefore, in gas shales, the diffusion can be in the tortuosity limit when the heterogeneity length scales of the pore structure are short compared to the root mean square displacements during the NMR encoding time. Combining Eq. 11 and 12, we can derive a relationship between T2 and D as the Sp /Vp varies, to obtain the surface relaxivity,   1/T2 − 1/T2b 4 ρ∼ √ √ . (14) 9 π D0 t 1 − D(t)/D0 Detailed theoretical analysis and experimental tests have been published [45, 46]. 2D NMR D-T2 maps of the gas shale samples are shown in Figure 5 [36]. In the figure, a vertical line separates the signal of the dead volume (right 16

side) from the useful signal of the gas shale samples (left side). The restricted diffusion lines for gas (red dashed lines) and water (blue dashed lines) have been plotted for different surface relaxivities without taking adsorption into account [45]. Thus, these lines can be considered as upper bounds as adsorption would further reduce the diffusion coefficients and relaxation times. The

Figure 5: D-T2 plots for methane gas inside an Haynesville gas shale sample. The restricted diffusion formalism [45]) has been applied for both the gas and water for four different surface relaxivity values of 1, 10, 50 and 100 µm/sec. The data to the left of the vertical line is the relevant contribution from the fluids in the gas shale, while that to the right is the contribution from the annulus dead volume. Data from Ref. [36] with permission.

peak with a long T2 due to the free gas in the dead space serves as a convenient bulk gas reference. The signal with a T2 value shorter than the vertical line is due to methane in the nanopores showing a progressive reduction in T2 and diffusion coefficient. 3.5. Transport of methane gas in gas shales The transport of methane gas in shale rocks is vital for understanding production. The low porosity (< 10%) and ultralow permeability (10−8 µm2 , or tens of nanoDarcy) in the gas shale rocks have necessitated the application of massive hydraulic fracturing to aid gas transport and to enhance production. Methods such as scanning electron microscopy and transmission electron microscopy provide important information regarding the nanometer-scaled pore 17

geometry in gas shales [47, 48]. However, these static structural characterizations provide only indirect knowledge of gas storage and flow pathways. Recently it has been demonstrated that NMR can be used to observe the transport of methane gas and to investigate the effect of fracturing on the transport properties [49]. Figure 6 shows the methane signal increase as a function of time after three stepwise pressure increases [49]. Once the methane gas is injected under high pressure into the NMR sample cell, an FID signal is acquired every 10 s to observe the rapid rise of methane content. A much slower increase in the methane signal follows over the next few hours until equilibrium. The time-limiting step for this equilibrium process comes from the transport of methane gas into the shale core plug due to the ultralow permeability of the gas shale samples [49]. This characteristic time which is on the order of magnitude of thousands of seconds, was observed in all the Haynesville gas shale samples studied. The methane gas in the core plugs experiences a large pressure gradient during the injection process. Because of the pressure dependence of viscosity and compressibility, it is challenging to establish an explicit expression between the characteristic time and the permeability [50, 51]. An order-of-magnitude estimate of the permeability can be made based on κ ∼ µ(r0 /τ )(r0 /∆P ) where µ is the methane viscosity, r0 the sample radius, and ∆P is the step pressure difference. Using the characteristic time 1kpsi of τGS03 ∼ 2.4 × 103 s observed experimentally, we obtain a permeability of κ ∼ 1.6 × 10−9 (1.6 nanoDarcy), which is in agreement with the often encountered low permeability of gas shale [52]. After the sample was equilibrated with methane gas at 21.3 MPa (3 kpsi), the pressure was then rapidly released and the transient process of gas transporting out of the core plugs was measured. A much shorter characteristic 3kpsi time was observed during such a pressure release process, τGS02 |release ∼ 189 s. The much faster release of gas suggests that fractures were created during the sudden pressure release when the sample is subject to a tensile stress. To confirm this, a pressure increase to 7.1 MPa (1 kpsi) was applied to the same sample after all methane had been evacuated. The characteristic time during this second pressure reloading step was found to be 60 s, significantly shorter than the initial loading time. These observations confirm that the sudden pressure release has created permanent structural changes that can accelerate methane flow. High resolution X-ray micro-CT images also confirmed the presence of fractures of several µm in width in the samples [49]. 18

Figure 6: The transport kinetics of methane gas in three gas shale core plugs at different pressure steps: (a) from vacuum to 7.1 MPa (1 kpsi), (b) from 7.1 MPa to 14.2 MPa (1 kpsi to 2 kpsi), (c) from 14.2 MPa to 21.3 MPa (2 kpsi to 3 kpsi). The characteristic time is obtained by fitting to a single exponential growth function for each curve shown as solid lines. The standard deviation of the characteristic time is shown in the parenthesis. Data from Ref. [49] with permission.

3.6. Methane gas storage and hydrogen index in nanoporous media The unique challenge in shale rocks is that a large fraction of the natural gas is stored in nano-pores of the organic kerogen resulting in an exposure to a high surface area and strong interactions with the pore surfaces. Understanding the gas storage capacity in the nano-pores is further complicated by the lack of reliable pore-pressure measurements and the complexity of the adsorption behavior. Nano-porous Vycor glass has been used as a proxy to demonstrate the complex adsorption behavior in nanopores over a large pressure range covering those encountered in gas shale reservoirs and the determination of the total stored gas and their separation into free and adsorbed 19

Figure 7: The signature of fracturing from transport kinetics of methane gas in a gas shale sample GS Plug #1. (a) The pressure loading from vacuum to 7.1 MPa (1 kpsi) shows a long characteristic time of ∼ 3.8 × 103 s; (b) The sudden pressure release from 21.3 MPa (3 kpsi) to vacuum creates fractures with a short characteristic time of ∼189 s; (c) The pressure reloading process reveals a short characteristic time of ∼60 s. Note the different time scale between (a) and (b), (c). Data from Ref. [49] with permission.

20

fractions using NMR relaxometry [39]. 800

35

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Signal Intensity (a.u.)

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Background Signal 2.1MPa 3.4MPa 4.8MPa 6.2MPa 7.6MPa 9.0MPa 10.3MPa 11.7MPa 14.5MPa 20.7MPa 27.6MPa 39.6MPa 56.0MPa 87.6MPa

γ 200 100 0 −6 10

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Figure 8: T2 distribution of methane gas in Vycor for various pressures at 22 ◦ C. The pressure dependence of the signal intensity and the T2 values for the different components can be identified. The small α component corresponds to the small amount of methane gas trapped in small pores and the background signal of Vycor. The γ peak with the longest relaxation time corresponds to the gas outside the sample. Data is from Ref. [39] with permission.

Vycor porous glass is composed of 96% SiO2 and 4% B2 O3 with a porosity of 28% and average pore size of 5.7 nm [39]. The NMR relaxation for spin1/2 nuclei in porous media generally occurs through two dominant pathways. The first is the slow motions at the surfaces resulting in enhanced relaxation caused by mechanisms such as Reorientation Mediated by Translational Diffusion [53, 54]. The second mechanism is due to paramagnetic impurities, and is weak in Vycor as the material exhibits low quantities of such impurities [55]. The T2 distributions of methane gas in Vycor are shown in Figure 8 [39]. We focus on the β component from the methane gas in the pore space. The significant reduction in the spin-spin relaxation time of the β peak with respect to the annulus gas γ is due to the enhanced surface interaction of the methane molecules with the pore walls. The strong pressure-dependent 21

increase in the intensity of this peak compared to bulk methane gas is due to adsorption of methane molecules on the pore walls. Adsorbed gas has a much higher density compared to bulk gas resulting in higher spin density in the pores. As the time scale of exchange between the free and adsorbed gas is much faster than the NMR relaxation time scale, these two components cannot be separated and therefore result in a single T2 distribution for the free and adsorbed phases. The pressure dependence of the free gas can be approximated by that of bulk methane while that of the adsorbed gas is more complex. The excess gas in the pore space resulting from adsorption can be determined experimentally as a function of pressure and fitted to a modified Langmuir isotherm (which assumes mono-layer adsorption) in the low-pressure regime to determine the adsorbed gas density, ρadsorbed , Langmuir pressure, PL and the maximum number of adsorbed molecules, NL . At methane pressures greater than about 40 MPa, a deviation from the Langmuir isotherm behavior resulting from the formation of multiple adsorbed layers on the pore surface is observed. The pressure dependence of the adsorbed gas at higher pressures can be explained by the Brunauer-Emmett-Teller (BET) model which accounts for multilayer adsorption. The number of adsorbed molecules for multilayer adsorption process is given by [56],   cx 1 − (N + 1)xN + N xN +1 ρf ree Nads = NL × 1− . (15) 1 − x 1 + (c − 1)x − cxN +1 ρads where x = P/P0 , where P0 is the saturation pressure, N is the number of adsorbed layers, NL is the number of molecules in a monolayer and c ∝ e(E1 −En )/RT is a unitless constant related to enthalpies of the first and N -th layers (E1 , En ). Using this approach, the gas in the Vycor nanopores can then be separated into free and adsorbed fractions for the entire pressure range from 0.7–89.7 MPa, shown in Figure 9. Up to a pressure of 40 MPa the “free gas” molecules in the pore interiors show a bulk-like pressure dependence while the adsorbed gas fraction increases faster at lower pressures but saturates at higher pressures. This indicates that at low pressures P < 12 MPa, more gas is stored as an adsorbed gas. Multiple adsorbed layers start to form for P > 40 MPa and therefore a rapid drop is observed in the “free gas” fraction. On the other hand, the adsorbed gas shows a rapid increase up to P = 89.7 MPa, accounting for 91% of the total stored methane. The amount of bulk methane gas that could be accommodated in the pore volume, in the absence of adsorption is also shown in magenta [39]. 22

8

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Total methane gas in pore space Free methane gas in pore space Adsorbed methane gas in pores space Bulk methane gas

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Figure 9: The excess gas in the pore space due to adsorption (Nexcess ) fitted for the entire pressure range using the modified multilayer BET adsorption model is shown on the left. The separation of the total gas into free and adsorbed fractions is shown on the right. The blue squares correspond to the total methane gas in the nanopores which is separable into adsorbed gas (black diamonds) and free gas (red circles). The bulk methane gas in Vycor (assuming no adsorption) is also shown in magenta triangles. Data is from Ref. [39] with permission.

The downhole measurement of the total gas in place (TGIP) in a shale gas play is important for field appraisal as it is a direct measure of the total amount of natural gas and the net value of the reservoir. Knowledge of TGIP as a function of depth enables the identification of gas-bearing zones and aids in the determination of the depths for drilling horizontal wells. Recently, a TGIP-NMR method has been developed by combining downhole NMR logging (to provide total hydrogen content) and mud-logging (to provide gas composition by gas chromotography) to determine gas volume [42]. This technology has now emerged as the leading solution for the formation evaluation of shale gas plays because it circumvents the requirement to obtain the hydrogen index, pore sizes, pore pressures, formation temperature and core measurements in order to determine free and adsorbed gas fractions. The TGIP-NMR method has been successfully demonstrated in various shale gas plays where gas-in-place measurements have been compared to conventional approaches. Often 20% more gas is identified with the TGIP-NMR method than with the conventional method, most likely because the conven23

tional approaches underestimate the gas density in the nanopore systems of organic shales [42]. 3.7. Section summary The presence of nano-pores is a significant challenge to the identification and quantification of gas shale reservoirs since the conventional gas reservoirs exhibit much larger pores and higher porosities. Experiments reviewed above explored methane NMR in model and real shale samples to understand relaxation/diffusion behaviors. These work led to effective downhole measurement strategies as well as understanding of the gas storage and quantification. 4. NMR of tight oil shale rocks Tight oil organic shales correspond to a lower state of maturity of the organic matter in comparison to over-matured gas shale rocks. They are also more complex in terms of their composition, as they contain solid kerogen, viscous bitumen, lighter oil in organic and inorganic pores, in addition to free and clay-bound water. Accurate identification of different components of unconventional tight oil reservoirs is fundamental to determining reservoir quality (RQ) or in other words the quality of the formation for hydrocarbon production [57, 58, 59]. NMR, in particular the low-field two-dimensional T1 -T2 experiment, often performed at 2 MHz, has played a critical role in fluid identification and quantification both in the lab and downhole. Furthermore, high field NMR contributes to the fluid identification by exploring the frequency dependence of T1 [33]. Below we discuss both low- and high-field experiments of tight oil shales. 4.1. T2 distributions The organic matter undergoes maturation at elevated temperatures and pressures over geological time scales and produces bitumen, oil and gas. This process can be simulated by subjecting the low maturity kerogen to higher temperatures (pyrolysis) as discussed in [60, 61] to produce samples of different maturation for laboratory investigations. 13 C NMR spectroscopy has shown to be successful to track the compositional changes during the pyrolysis. The behavior is also reflected in the 1 H T2 distribution shown in Figure 10 demonstrating the initial increase and later reduction of the bitumen components (T2 ∼0.1–1 ms) as a function of maturity. The kerogen peak (T2 ∼ 20µs) decreases progressively for more matured samples. 24

Figure 10: 1 H T2 distribution of shale samples of different maturity, normalized by the sample weight. Note that as a function of maturity, the peak at 20 µs progressively reduces, and the peak at ∼300 µs increases first and then decreases. Measurement performed at 400 MHz and with an echo spacing of 20 µs (Avance DMX-400, Bruker Biospin). The samples were from Ref. [61].

4.2. Low field 2D T1 -T2 map of fluids The spin relaxation of hydrocarbon materials is primarily due to intramolecular dipolar couplings. Based on Bloembergen-Purcell-Pound (BPP) theory [62, 63], the T1 and T2 rates may exhibit a significant frequency dependence: µ  1 0 = γ 4 ~2 I(I + 1)(5r6 )−1 [J(ω) + 4J(2ω)] , (16) T1 (ω) 4π and µ  1 0 = γ 4 ~2 I(I + 1)(5r6 )−1 [3J(0) + 5J(ω) + 2J(2ω)] , T2 (ω) 4π

(17)

where µ0 is the vacuum permeability, I is the spin number (I = 1/2 for hydrogen nuclei), γ the gyromagnetic ratio, ~ is Planck’s constant divided by 2π and r is the inter-nuclear distance. The spectral densities J(ω) can be obtained by the Fourier transform of the autocorrelation functions of the magnetic field fluctuations. T2 is dominated by the J(ω = 0) term and therefore very sensitive to the low frequency or slow motions. On the other hand T1 is sensitive to the spectral densities at the Larmor frequency and twice this frequency (ω and 2ω), and therefore to the applied magnetic field. 25

The longitudinal relaxation time obtained in the limit of very low Larmor frequencies, (ωτc  1) (where τc is the rotational correlation time of the molecules), T1 becomes proportional to T2 (ω) due to the dominance of the J(ω) term. Therefore, the T1 − T2 map is sensitive to molecular motions in the frequency range between the Larmor frequency and very low frequencies. This shows the significance of using the T1 /T2 ratio as the parameter to reflect molecular mobility in fluids, both in their bulk state and in confined state [64]. A universal T1 -T2 map for all different constituents of gas and tight oil shale at 2 MHz Larmor frequency is summarized in Figure 11 [65, 66]. The bulk gas relaxing due to spin rotation has the longest T1 and T2 relaxation times and a ratio T1 /T2 = 1. Motional averaging is said to occur when the spin-bearing nuclei used as probes satisfy the condition, ωτc  1. If this situation is satisfied, then T1 and T2 are equal. For example, at low Larmor frequencies, bulk fluids such as oil and water are motionally averaged and relax due to the dipolar interactions resulting in a ratio T1 /T2 → 1. As these dipolar interactions are more efficient in oil and water than spin rotation in gases, they have shorter relaxation times. Due to the mixed-wet nature of the inorganic pores, relaxation times of oil and water are also reduced from their bulk values and the T1 /T2 ratios range from 1 to 2. The organic porosity is hydrocarbon wetting resulting in a very short T2 and a T1 /T2 ratio generally in the range of 2 to 6. The clay-bound water has a T1 /T2 ratio generally between 1 and 2. The viscous bitumen has a very short T2 which could overlap with that of bound water and T1 /T2 ratios ranging from 4 to 15 or higher [65, 66]. In bitumen very high T1 /T2 ratios of up to 100 have been measured at 2 MHz in shales from other regions (unpublished data). The light oil in the bulk state undergoes relaxation due to intermolecular and intramolecular dipolar relaxation processes that are related to their chain lengths [67, 68]. The relaxation in heavy oils is more complex due to the presence of asphaltene and their aggregation states. The maltenes or the lighter fractions of the oils are relaxed by both the proton-proton intermolecular interactions modulated by slow motions due to their interactions with the asphaltenes, and the proton-electron interactions with the paramagnetic ions and free radicals in the asphaltenes. This causes a reduction of the relaxation times, generally resulting in the heavy oils and bitumen having a T1 /T2 ratio of 3 to 7 at 2 MHz and room temperature. Higher values of T1 /T2 ratios can also be observed for more viscous bitumen. The relaxation mechanisms of oil in organic pores differ from those in 26

Figure 11: An illustration of the low-field (2 MHz) T1 -T2 map for all the components in unconventional shales in a homogenous magnetic field. Data from Ref. [65] with permission.

the inorganic pores due to their wettability, and therefore T1 -T2 maps can be uniquely used as a probe for segregating oil-filled porosities into organic kerogen porosities versus inorganic mineral hosted porosities. This is because increased wettability results in slower motions at the surfaces. Since T2 relaxation times are sensitive to slow motions, they are affected more than T1 relaxation times which are sensitive to motions at the Larmor frequency (Eqs. 16 and 17). This leads T1 /T2 ratios to increase with increasing wettability. The inorganic porosity in tight-oil shale has a mixed wettability, resulting in a reduction in the relaxation times of the oil and a T1 -T2 ratio of about 1.2 to 1.5. The oil in the organic pores has a much higher reduction in relaxation times, mainly due to the long residence times or extreme slowing down at the oil-wetting organic pore walls. Furthermore, interactions with the 1 H nuclei of the organic kerogen could also lead to an additional relaxation mechanism of the oil molecules. This results in the oil in organic porosity having a much higher T1 -T2 ratio, ranging from 2 to 6. These differences in the relaxation mechanisms resulting in the different T1 -T2 ratios dependent on wettability enable the application of low field 2D NMR relaxometry for the separation of organic porosities from inorganic porosities in both downhole logging applications and in core analysis in a laboratory

27

[65, 66]. 4.3. Examples We discuss NMR relaxometry results from both the Eagle Ford and the Bakken plays in this section [33, 66]. The NMR T2 distribution of an “as received” shale sample in its native state is presented in Figure 12. This T2 distribution corresponds to the residual fluids after the movable fluids have escaped during core retrieval. The sample was then resaturated with the “dead oil” (crude oil without its volatile components) from the formation, and its T2 distribution is shown in the same figure in green. No significant difference is seen between the native and re-saturated samples at T2 < 0.5 ms. This is because that this range of T2 corresponds to the bitumen and the bound water contributions, which are unchanged by the resaturation process.

Figure 12: The 2-MHz NMR T2 relaxation distribution of a native shale sample (black line) and after re-saturation (green line), demonstrating that the oil refilling the empty pores can be identified and quantified. Further identification of the pore type is possible from the T1 /T2 ratios as discussed in the text. Data from Ref. [65] with permission.

The re-saturated crude oil signal above 0.5 ms in the T2 distributions of Figure 12, corresponds to the oil in both the organic and inorganic pores. The separation of this oil into contributions from organic versus inorganic porosities can be done based on the T1 /T2 ratios, as shown in Figures 13, where the native shale and the re-saturated shale are shown on the left and right panels, respectively. The oil in the organic porosity has a higher T1 /T2 ratio of about 5 and T2 values from 0.5 ms to about 6 ms at 27 ◦ C. The light 28

oil in the organic kerogen pores of the native state sample is the fraction that remains in the core during retrieval, and therefore constitutes the bound light hydrocarbon. Core re-saturation results in an increase of this fraction, as shown in the right panel (Figure 13) identifying the movable oil in this organic porosity. This shows that the pressure drop during core retrieval can result in a large fraction of light oil escaping from the organic kerogen pores. The oil after re-saturating the mixed inorganic pores has T2 relaxation times longer than 6 ms and much lower T1 /T2 ratios of about 1.4 [65, 66].

Figure 13: Left: T1 − T2 map of the native shale sample is shown wherein the T1 /T2 ratios, together with the T2 , enable the separation of the bound water and bitumen signals from those of the oil in organic pores. Right: T1 -T2 map of the re-saturated shale sample, wherein a clear increase in the signal of the crude oil in the inorganic and organic porosities is found compared to the native samples. Data from Ref. [65] with permission.

The amount of oil that re-saturates native shale samples corresponds to the porosity of the movable fluids in the formation, which have escaped during core retrieval. The resaturated oil porosity is compared with the gasfilled porosity of the native state cores and is shown to be in good agreement [65]. Therefore, by this methodology NMR can provide an estimate of the movable fluid porosity and also enable separating it into organic and inorganic fractions. The T1 /T2 ratio is a new parameter that have been found useful to characterize the different fluid components in shales. Even though the theory of such behavior is understood, its significant applications in petroleum industry started with the shale exploration owing to the need to characterize a 29

T 1/T 2 ratio

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Figure 14: Average T1 /T2 ratio measured at 400 MHz for crude oil samples from many oil fields. Measurements were made using IR-CPMG at 30, 40, 50◦ C.

wide range of compositions of organic materials. In order to obtain T1 /T2 ratios from samples of multiple components, T1 -T2 correlation experiment has found an important niche. 4.4. High field NMR T1 -T2 maps The T1 /T2 ratio is a sensitive measure of the molecular dynamics, as seen in Eq. 16 and 17. Compared to low field measurements, a higher Larmor frequency probes the spectral density at much higher frequency and potentially increases the T1 /T2 ratio [64, 69, 70]. This behavior can be observed in high field measurement of crude oils over a large range of viscosities, Figure 14. The lighter oils show long T1 s and low to moderate T1 /T2 ratios, while the higher viscosity oils exhibit progressively shorter T2 and higher T1 /T2 ratios. When T2 is about 10-20 ms, the T1 /T2 ratio approaches 100 demonstrating a much slower molecular rotation. We could expect that this ratio could be even higher at higher viscosities or when approaching the solid state. The high-field NMR T1 -T2 maps from the Upper Bakken shale samples are shown in Figure 15 [33]. Kerogen and bitumen were isolated from the native samples and measured separately, and data shown in Figure 15 together with those of the native-state samples. The main advantage of high field NMR is that the differences in the frequency dependence of the spin lattice relaxation times (T1 ) can be utilized to better separate the hydrocarbon and the aqueous (clay-associated) fractions in comparison to lower frequency measurements. 30

10 3 2

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Figure 15: Left 3 panels: High field (400 MHz) 2D T1 -T2 maps of a native shale sample from the Upper Bakken formation, and the kerogen and bitumen isolated from it. Right panel: 2D T1 -T2 map of the native shale sample measured at 2 MHz. Data from Ref. [33] with permission.

The T1 of the solid and viscous hydrocarbon fractions (i.e. kerogen and bitumen which are characterized by short T2 values) are about 150 ms at 400 MHz. The aqueous signals (corresponding to the clay-associated water) have shorter T1 values, < 50 ms) [33]. The different components of the shale rocks such as kerogen and bitumen are considered as a solubility class and only defined operationally. The kerogen is defined as the component that cannot be dissolved in organic solvents while bitumen is the heavy viscous oil component which is soluble in organic solvents. The light oil hydrocarbons which are also soluble in organic solvents can be differentiated from bitumen by their viscosity. In general, a cutoff in viscosity of about 10 Pa·s (10,000 cp) can be operationally used to differentiate the bitumen from the lighter hydrocarbon fractions. As discussed below, NMR T1 -T2 provides an alternate way of identifying and possibly defining these components. Two dominant T1 /T2 ratios (about 2000 and 600) can be identified from the 2D maps of the native shale rock samples shown in Figure 15, and similar ratios observed for other samples [33]. The T1 -T2 2D maps of the isolated fractions of the kerogen and bitumen from the native shale rocks are also shown in the right panels. The isolated kerogen has the dominant T1 /T2 peak at about 2000 to 4000 and tail extending to a T1 /T2 ratio of 300-700. The isolated bitumen fraction in contrast has a dominant T1 /T2 signal at about 400-1000 and a smaller signal at about 6000. This overlap in the kerogen and bitumen signals in the 2D NMR maps reflects the similarity in their molecular structures/dynamics, which is not surprising considering kerogen

31

being the precursor of bitumen. These results also reflect the challenge in drawing a sharp boundary for the kerogen and bitumen components of shale rocks, even though the different fractions can be identified. The solid and viscous hydrocarbon components are a continuum in the T2 dimension with 1/T2 highly correlated with the component viscosity. Despite this continuum distribution it can be clearly seen that the kerogen is dominated by the shorter T2 and higher T1 /T2 ratios (0.03 ms and 3000) while the bitumen is dominated by the longer T2 and lower T1 /T2 ratios (1 ms and 1000). In comparison, the measurement at 2 MHz for the same native sample (the right panel in Figure 15) shows a much lower T1 /T2 ratio and the different components are not separated as well as in the high field experiment. These signatures of the two components can therefore be used qualitatively and quantitatively for understanding fundamental shale properties, such as maturity [33]. 4.5. Diffusion NMR and D-T2 map The identification of free fluids with a T2 greater than 10-30 ms as either oil or water is challenging using the T1 -T2 measurement at either low or high magnetic fields. This is because fluids with such relaxation times are either in their bulk state or in mixed-wetting inorganic mineral pores, where the behaviors of oil and water are similar. This results in similar T1 /T2 ratios for oil and water, therefore limiting their differentiation. On the other hand, the diffusion properties of oil and water can be substantially different. Some D-T2 or similar experiments have been used to distinguish oil and water in core samples. Examples of such applications are shown in Figure 16 and Figure 17 for shale samples from the Smackover [71] and the Middle Bakken/Three Forks formations [33] in the US. The NMR experiments were performed on the samples retrieved from the wells without further cleaning. Such native samples can be used to detect the residual fluids still remaining in the samples of the formation because of the extremely low permeability of the rocks. In Figure 16 the D-T2 map clearly shows a major signal right on the oil line, indicating the presence of oil in the core. The small peak near T2 ∼ 10 ms is weak and its diffusion coefficient is less accurate. Signals below T2 = 10 ms due to the bound fluids are not observable in this experiment due to the long echo times. This experiment shows unambiguously that the native fluid is oil and the formation is potentially a hydrocarbon-producing reservoir.

32

Figure 16: D-T2 map measured at 2 MHz of a native shale sample from the Smackover formation. The horizontal line gives the water diffusion coefficient and the diagonal line is the so-called “oil line” where signals of oils are found to lie [12]. The signal is clearly along the oil line indicating the presence of light oil in the sample. The top and right panels are projections along the respective dimensions. Data from Ref. [71] with permission.

The 2D NMR diffusion D-T2 maps of the Middle Bakken and the Three Forks formations are shown in Figure 17, and clearly identify the free fluids as oil. These slowly relaxing components (centered ∼ 100 ms) form the dominant fraction of the fluid that can be extracted, therefore, their identity is important to assess the value of the shale play. NMR diffusion measurements work best when the T2 relaxation times are longer than a few tens of milliseconds (∼ 30-50 ms), and, therefore, the presence of fluids with such long relaxation times (∼100 ms) has made this an ideal candidate for lab and downhole fluid characterization. The limits of such diffusion measurements for downhole and laboratory applications on continuous gradient and pulsed field gradient instruments have been well understood [72].

33

Figure 17: D-T2 maps from the Middle Bakken and Three Forks obtained with a CMR+ well-logging tool. The appearance of signals along the oil lines indicates the presence of light oil (long T2 ∼ 0.1 s) in the formations. Data from Ref. [33] with permission.

4.6. Reservoir quality (RQ) The Reservoir Quality (RQ) of unconventional resources can be understood as the ability to produce hydrocarbons upon stimulation by hydraulic fracture and is determined by the amount and type of organic matter present in the formation. In shale gas resources, natural gas is typically stored in pores hosted in the organic kerogen and transported from them. Therefore, a high quantity of kerogen can typically lead to higher porosity and is considered to be a positive RQ indicator. The main fluids encountered in the gas shale reservoirs are often the bound water and the natural gas. The ability of NMR to be sensitive to these two components and the possibility of separately quantifying them based on the differences in their relaxation properties makes NMR very useful for the evaluation of these reservoirs [36, 42]. In tight-oil shale rocks, fluid characterization is more challenging due to the presence of bitumen/kerogen and lighter oil in addition to free and bound water. Furthermore, the light oil can be hosted in the mineral pores or in the organic kerogen pores, while the bound water can be associated 34

with the clays or resides in mineral pores of varying sizes. The fluids in the mineral-hosted pores contribute more significantly to the produced fraction while the kerogen and bitumen can trap fluids by adsorption, clogging pore throats, and swelling. Therefore, the kerogen and bitumen can be considered to be a negative RQ indicator in tight-oil shale reservoirs while the light oil is a positive RQ indicator. This emphasizes the value of differentiating the different fractions of organic matter. NMR logging has become a critical measurement for the downhole evaluation of tight oil shale rocks due to its unique ability to identify these fractions, typically via T1 -T2 measurements [33, 73, 74, 75]. The above considerations have been applied to define a reservoir producibility index (RPI) based exclusively on downhole measurement of NMR, nuclear and resistivity well-logging tools. In computing the RPI, the quantity of oil is primarily determined using NMR measurement logs while the quantity of all organic matter is measured by a combination of other logs such as nuclear spectroscopy and resistivity measurements. The utility of the RPI has been demonstrated in examples from several tight-oil reservoirs, in which zones with the highest RPI are found to be the most productive [57, 58, 59]. 4.7. Solid state NMR for shale rocks The one unique aspect of shale rocks is that the organic matter includes solid phases as well as fluid phases with a continuous variation of viscosity. Such solid and highly viscous materials are known to exhibit slowly modulated dipolar interactions between the nearby hydrogens resulting in broad spectral lines and short T2 ’s. Previous works have used solid state NMR techniques, for example, to examine kerogen composition by CP/MAS 13 C spectroscopy and relaxation [76, 77, 78, 79, 80], in combination with several techniques [81]. Recently, several reports have discussed methods to enhance the data analysis of such solid signals [82] as well as the use of magic echoes and other solid state sequences to improve the 1 H signal acquisition [83, 84, 85, 86]. These techniques may lead to better quantification of the total kerogen and bitumen in core samples. Further work will be needed to explore the possibility of applying these techniques to well-logging. 4.8. NMR instrumentation: frequencies and echo times The NMR well-logging tools, due to space limitations, may only provide measurements at frequencies around 2 MHz and lower. In addition, 35

since many fluid components exhibit very short T2 , it is essential for an NMR instrument to be able to perform measurements with short echo times. The Schlumberger CMR+ and the recently released CMR-NG have become workhorses in shale applications that benefited greatly from their short echo times (200 µs). In the laboratory, however, instruments with a large range of magnetic fields and shorter echo times are commercially available. For example, echo times of 60-100 µs can be achieved routinely (e.g. by Oxford Instruments and Niumag Co.). Higher field T1 -T2 measurements are known to better differentiate different fluid components due to the higher T1 /T2 ratios (e.g. in Ref. [87, 88, 85, 89]) and the typically shorter echo times. Xie and Gan have argued that a 20 MHz NMR system is ideal for shale studies [90]. Instruments at up to 60 MHz can be readily built using permanent magnets which are suitable economical deployment. Furthermore, automated sample handling peripherals might prove to be essential for the wide adoption of such technologies. Another interesting area of application is the NMR characterization of drill cuttings (typically 1-5 mm size) during drilling at or near the location of the wells, often called well sites. Cuttings are a byproduct of the drilling process and are commonly examined by geologists on-site as a qualitative measure of lithology and rock types. Several studies have shown the possibility of performing NMR measurement on such cuttings [91, 92, 93]. Portable systems designed for NMR of drilling muds [94] or other lightweight equipment [95] might possibly be converted for well-site measurements. 4.9. Section summary The complex organic materials in the pore space of tight oil shale rocks present a major challenge to the traditional formation evaluation methods. Most traditional methods could not distinguish the various components that differ primarily in viscosity and molecular composition. The sensitivity to such molecular composition and dynamics gives NMR a unique opportunity to become the key measurement to identify the reservoirs and to quantify the production potentials. 5. Conclusion This paper reviews the development of NMR relaxation and diffusion measurements for the characterization of fluids in unconventional shale reser36

voirs. Such shales reservoirs are unique due to the presence of solid, viscose and light hydrocarbons and their quantification is important for their exploration and production. As a result, the sensitivity of NMR to the motions of these molecules gives rise to its unique applications, e.g., measurements of the amount of gas in a reservoir (TGIP, Section 3) and reservoir quality (RPI, Section 4). During the technical development, knowledge of NMR physics and the effects of porous media is essential, e.g. restricted diffusion, frequency/magnetic field dependence of spin relaxation, and MD NMR techniques. Equally important is the understanding of the relevant industrial processes and the critical steps where new sciences/technologies could make a difference. Collaborations among people with multi-disciplinary and technical backgrounds are fundamentally enabling. For example, the discovery of the fast methane relaxation in shales allowed a rapid implementation of fast data acquisition sequences to enhance SNR for well-logging of shale formations [36]. In summary, NMR experiments, in particular 2D T1 -T2 and D-T2 measurements have been proven effective for downhole and laboratory applications in unconventional oil and gas shale exploration. These experiments allow us to identify signals from different phases and quantify their distribution. A combination of low- and high-field experiments provides insight into the interpretation of these signals and has enabled practical applications. They have become the key measurement to identify the reservoirs of unconventional oil and gas fields and and have been widely incorporated in the routine field development workflow in the US and increasingly in other countries. Acknowledgments We thanks Schlumberger-Doll Research for the support of this work and permission to publish the results. *Bibliography [1] U.S. Energy Information Administration. (2018).

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Highlights Unconventional shale reservoirs present a challenge to the understanding and measurements of the properties of rocks and fluids

NMR has proved vital for the characterization of fluid compositions of shale rocks in both downhole logging and laboratory studies

Multidimensional relaxation and diffusion distributions have been widely used to identify and quantify the fluid compositions in gas shale and tight oil shales