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A novel experimental approach for fracability evaluation in tight-gas reservoirs
Journal of Natural Gas Science and Engineering 23 (2015) 239e249
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Journal of Natural Gas Science and Engineering journal homepage: www.elsevier.com/locate/jngse
A novel experimental approach for fracability evaluation in tight-gas reservoirs Daobing Wang a, b, *, Hongkui Ge a, b, Xiaoqiong Wang a, b, Jianbo Wang a, b, Fanbao Meng a, b, Yu Suo a, b, Peng Han a, b a b
Unconventional Natural Gas Research Institute, China University of Petroleum, Beijing 102249, People's Republic of China State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, People's Republic of China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 November 2014 Received in revised form 26 January 2015 Accepted 28 January 2015 Available online
Hydraulic fracturing is an effective stimulation method for the economic development of tight-gas reservoirs in which extremely low matrix permeability requires complex fracture networks. Petrophysical/mechanical experiments and XRD/SEM analyses demonstrate that volcanic sedimentary rock is characterized by developed natural fracture, strong brittleness, stress sensitivity, AE activity, weak anisotropy, and fluid sensitivity. Fracability index is often utilized as a key parameter to evaluate the ability to generate fracture networks. In this study, a new systematic experimental approach and a new mathematical model are established for the comprehensive evaluation of the fracability of tight-gas formations. These two methods integrate natural fracture, stress sensitivity, rock anisotropic nature, AE activity and crack density. The results indicate that the fracability index (FI) and the calculated crack density (CRD) are positively linearly correlated. The method is successfully applied to evaluate the fracability of the Yingtai gas field in northeast China. © 2015 Elsevier B.V. All rights reserved.
Keywords: Tight-gas reservoir Fracability evaluation Fracture network Crack density Velocity anisotropy Stress sensitivity
1. Introduction The interest in unconventional gas reservoirs has rapidly increased worldwide in recent years due to the combined technology of horizontal well drilling and multi-staged hydraulic fracturing (Liu, 2013). Tight-gas reservoirs are characterized by very low matrix permeability. They need hydraulic fracturing technology to provide sufficient permeability and economic productivity. The conventional hydraulic fracturing technique often produces two symmetric fractures and struggles to achieve the economic development of tight-gas reservoirs. Stimulated reservoir volume (SRV) fracturing is an efficient stimulation method that can generate complex or “tree” fracture networks (Fig. 1). Therefore, stimulation challenges associated with a tight gas alter the fracture design concept from a simple fracture to a complex fracture. Fracability is the capability of shale gas plays to effectively carry out hydraulic fracturing and generate fracture networks (Chong et al., 2010). Fracability index is a key parameter to evaluate
* Corresponding author. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, People's Republic of China. E-mail address: [email protected] (D. Wang). http://dx.doi.org/10.1016/j.jngse.2015.01.039 1875-5100/© 2015 Elsevier B.V. All rights reserved.
whether oil and gas reservoirs can effectively produce complex fractures during treatment. Currently, many researchers use brittleness index to directly evaluate fracability. Rickman et al. (2008) proposed a brittleness index formula based on the Poisson ratio and Young's modulus, using their crossplot to evaluate the brittleness of Barnett shale. A lower Poisson ratio corresponds to a higher Young's modulus and a more brittle rock in which it is easy to generate complex fracture. Sondergeld et al. (2010) adopted the proportion of quartz-carbonate-clay to calculate fracability index. The most brittle rock has the most quartz and least clay. Therefore, the brittleness formula is BI ¼ Qz/(Qz þ Ca þ Cly), where Qz is the fractional quartz content, and Ca is the calcite content by weight in the rock. In fact, in addition to quartz, feldspar and dolomite, which are often neglected when evaluating shale brittleness, are also brittle components in shale gas plays (Wang and Gale 2009; Altamar, 2013; Fang et al., 2014). Considering the dolomite component, the brittleness index can be corrected as BI ¼ (Qz þ Dol)/(Qz þ Dol þ Ca þ Cly þ TOC), in which Dol is dolomite content, Ca is the calcite content, and TOC is the total organic carbon content by weight. Fracability is affected not only by rock brittleness but also by natural fracture, diagenesis and other factors. Tang et al. (2012) established a comprehensive mathematical model to calculate
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Fig. 1. Various hydraulic fracture morphologies: from simple fracture transition to tree fracture network.
shale fracability index using analytic hierarchy process. The model was successfully applied in the region of Southeast Chongqing, Southwest China. Guo et al. (2013) used soundless crack agents to expand the borehole and produce artificial fractures and then evaluated crack density by fractal dimension. Yuan et al. (2013) evaluated the shale fracability based on the brittleness index, fracture toughness and rock mechanical properties and established three-dimensional distribution figures of shale fracability. Jin et al. (2014) integrated both brittleness and energy dissipation during hydraulic fracturing. The formation with the higher fracability index is considered to be the better fracturing candidate. Heterogeneities in shale fracability play an important role in optimizing the hydraulic fracture parameter (Jahandideh and Jafarpour, 2014). An optimization approach for hydraulic fracturing design under spatially variable shale fracability was developed, and the results of several numerical experiments showed that the proposed method is effective and suitable. Mullen and Enderlin (2012) defined a new fracability index, the complex fracability index (CFI), that integrated the sedimentary fabric, mineral distribution, pre-existing weakness planes and present-day stress state. The CFI methodology is suitable for both shale gas plays and tight sandstone driven by the data that is available. From the above study, fracability is not completely equal to brittleness; it is related to many factors. The geometry and complexity of hydraulic fracture stimulation treatments is largely controlled by the heterogeneous and anisotropic nature of rocks (Mullen and Enderlin, 2012). It is difficult and time-consuming to define a uniform mathematical formula for fracability index considering all of the factors. Investigations of fracability index have mainly concentrated on shale gas reservoirs, while the evaluation of fracability is rare for tight gas reservoirs. Herein, we adopt a new systematic experimental approach integrating natural fracture, stress sensitivity, rock anisotropic nature, AE activity and crack density for the comprehensive evaluation of the fracability of tight gas formations.
volcanic breccia. Its pore type includes matrix dissolved pore, phenocryst dissolved pore, gas pore and intragranular dissolved pore. The lithology of the yc2 reservoir is mainly sandstone, conglomerate and tuffaceous sandstone. Its pore type includes intragranular dissolved pore, intergranular pore and intragranular pore. The reservoir space types are mainly dissolved pore (90%) and intergranular pore (7%). The yc1 formation has mainly high-angle tectonic fracturing extending along the south and north directions. The crack density is low, less than three metres per crack, and is mainly distributed in the top position of the volcanic reservoir. High-angle tectonic fractures also exist in the yc2 formation, but the fractures are small (widths of 5~50 microns), almost filled and less developed. Because of large difference in planar distribution, the developed zones are mainly concentrated near Well Longshen303 and Well Longshen202. The average porosity and permeability of the yc1 formation are 9.6% (middle porosity) and 0.36 md (extra-low permeability reservoir), respectively. The average porosity and permeability of the yc2 formation are 7% (extra-low porosity) and 0.05 md (tightgas reservoir), respectively. These two reservoirs are both dry gas reservoir with normal temperature and pressure systems. 2.2. Sample preparation The volcanic rock samples were cored from gas wells in the Yingtai Gas Field (Jilin Province, northeast China) at depths of 1900~4500 m. All samples were cored and polished to cylinders of 25 mm in diameter and 50 mm in length (Fig. 2). Their basic physical parameters are shown in Table 1. The main rock type is volcanic sedimentary rock (tuff gritstone) and sandstone. Their structures are relatively stable. Intergranular pores are preserved relatively well. Secondary pores are developed and are dominated by intragranular dissolved pores, or intergranular pores, followed by matrix dissolved pores. Among the secondary fractures, structural fractures are predominant, followed by dissolved fractures, which were shown to be partially open by thin section analysis. The granularity is coarse (>0.5 mm), as demonstrated by casting thin
2. Experimental samples and procedures 2.1. Geologic features The Yingtai Gas Field is located in the Songliao Basin, Jilin Province, northeast China. The overall structural feature is two east-dipping nose-like structures located in the north and south and divided by NWeSE subsags. North-east and north-south faults are developed. The gas reservoir is buried at a depth of 3000~5000 m. The complex lithology of tight-gas reservoirs mainly contains two sets of reservoirs: volcanic reservoirs in the first member of the Yingcheng Formation, termed yc1; and pyroclastic sedimentary reservoirs in the second member of the Yingcheng Formation, termed yc2. The lithology of the yc1 reservoir is mainly rhyolite, tuff and
Fig. 2. The volcanic sedimentary rock samples from Jilin Oilfield in northeast China.
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Table 1 Basic physical parameters of rock samples. Well name
Core number
Depth (m)
Weight (g)
Diameter (mm)
Length (mm)
Density (g/cm3)
Permeability (103 mm2)
Porosity (%)
1-1 1-2 2-1 3-1 4-1 5-1 5-2 5-3 6-1 6-2 7-1 7-2 7-3 7-4 8-1 8-2 9-1
C608-1-1 C608-2-1 C607-2-2 C7-1-2 C10-2-1 L8-1-2 L8-2-1 L8-4-1 L309-3-2 L309-7-2 L306-1-2 L306-2-1 L306-3-2 L306-4-4 L1-1-1 L1-2-1 L208-1-1
1917.04-1918.28 2455-2457 2728.85-2730.27 1862.20-1866.00 2862.65-2863.45 3575.97-3577.9 3581.87-3587.51 3836.3-3838.54 4287.56-4289.12 4294.63-4296.46 3687.10-3688.28 3831.50-3833.28 3833.28-3835.00 4046-4047.83 2408.58-2410.44 2410.55-2412.55 4422.70-4424.65
65.96 60.49 63.30 67.88 69.72 67.57 66.01 63.56 66.14 60.01 61.34 61.29 60.27 67.86 65.19 63.16 63.77
24.93 25.05 24.95 24.89 24.92 24.86 24.83 24.90 25.35 24.77 24.85 24.89 24.85 24.82 24.86 24.83 24.87
51.40 52.81 53.04 52.88 54.13 53.97 52.86 51.98 50.94 49.53 49.97 50.28 48.84 55.53 52.36 50.51 48.82
2.63 2.32 2.44 2.64 2.64 2.58 2.58 2.51 2.57 2.51 2.53 2.51 2.54 2.53 2.57 2.58 2.69
0.0022 0.00282 0.002766 0.001212 0.0413 0.014224 0.0014 0.0006 0.0012 0.007504 0.000747 0.0021 0.001312 0.0067 0.001194 0.002459 0.008343
5.95 1.23 2.46 1.11 6.85 7.37 1.97 1.26 3.58 3.6 0.616 7.8 4.24 0.687 5.95 3.74 7.58
section testing (Fig. 3). Pore-throat analysis statistics using mercury intrusion show that most of the pores are less than 0.1 micron in size, reflecting the complex pore structure characteristics. The porosity is in the range of 0.56%~7.83% with an average of 2.9% in the Yingtai area. In the gas pay, porosity is in the range of 0.67% ~5.91% with an average of 2.27%. The Klinkenberg permeability is in the range of 0.0006e0.0413 mD with an average of 0.0079 mD and most being below 0.01 mD.
2.3. Experimental equipment and procedure An ultrasonic velocity measurement system composed of an Olympus 5077 PR pulse-generator, oscilloscope and V157 shear wave transducer with a 5-MHz dominant frequency was employed at room temperature and atmospheric pressure (Fig. 4). The waveform sampling rate was 3.125 MHz. To improve the signal-tonoise ratio, we adopted a shear wave couplant and U-shape tools to bring the core ends into close contact with the transducers. Compressional wave velocities (P waves) were conducted by handy press under uniaxial stress at a rate of 2 MPa/min up to 60 MPa. The P waves were measured along the direction parallel to the stress using a classic ultrasonic pulse transmission technique between an emitting and a receiving transducer (Nano30) with a dominant frequency of 250 kHz. For each velocity measurement, 100 acoustic waveforms were stacked to increase the signal-tonoise ratio. The arrival times were determined by cross-
Fig. 4. The oscilloscope and pulse-generator.
correlation to a reference waveform so that the relative error in velocity measurements is estimated to be lower than 1%. Acoustic emission was recorded by an ASC digital oscilloscope (Cecchis) under uniaxial stress. The preamplifier gain was 40 db, the sampling rate was 10 MHz, and the analogue filter bandwidth was 100 kHze2 MHz. The threshold value was set to 8 mV. Twenty acoustic waveforms were stacked in order to increase the signal-tonoise ratio.
Fig. 3. Casting thin section results: the observation of a natural but undeveloped fracture with the characteristics of a tight reservoir. The red scale is 628.52 mm in the top left corner of the figures.
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Fig. 5. Vp and Vs velocities of 17 core samples in ascending order. Vp(L) and Vs(L) represent the compressional wave velocities along the longitudinal direction, and Vp(D) and Vs(D) represent the shear wave velocities along the diametrical direction.
3. Experimental results 3.1. Elastic velocity and anisotropy at room temperature and atmospheric pressure The elastic velocities of 17 core samples were obtained along the longitudinal and diametrical directions (Fig. 5). Vp is in the range of 3.5~6 km/s, and Vs is in the range of 2~3.4 km/s. Both show significant variation, which is possibly related to the mineralogical composition and heterogeneity as the clay content is only 9.6% in Well 5-3, while it reaches 30% in Well 5-1 according to the XRD results. A higher clay content corresponds to a lower wave velocity (Han, et al., 1986), which is in line with Fig. 6. Thomsen (1986) proposed a characterization method of weak anisotropic medium parameters ε and g, which represent Vp and Vs anisotropy, respectively. They are defined as follows: Fig. 7. (a) Brittleness index of 17 core samples in ascending order along the longitudinal and diametrical directions. BI(L) and BI(D) represent brittleness index along the longitudinal and diametrical directions, respectively. (b) Brittleness index calculated from the XRD results.
Vp ðLÞ Vp ðDÞ 100 Vp anisotropy ¼ min Vp ðLÞ; Vp ðDÞ Vs anisotropy ¼
jVs ðLÞ Vs ðDÞj 100 minfVs ðLÞ; Vs ðDÞg
(1)
(2)
Unapparent bedding planes are observed in the 17 core samples, they are simply viewed as weak anisotropic media. The Thomsen parameters ε and g were calculated from formulae (1) and (2), and the results are shown in Fig. 6 in ascending order. In Fig. 6, the blue horizontal line corresponds to a value of 10, which distinguishes between weak and strong anisotropy. With the exceptions of Wells 9-1 and 5-3, most core samples show weak anisotropy. Well 5-3 has strong anisotropy because of the observed natural fracture. Fig. 6. Vp and Vs velocity anisotropy parameters of 17 core samples in ascending order. If the value is above the blue line (corresponding the vertical coordinate value of 10), anisotropy is strong; conversely, anisotropy is weak. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
3.2. Brittleness analysis The brittleness is usually evaluated by brittleness index (BI), which is calculated from the XRD mineralogical composition or
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Fig. 8. Compressional wave velocity (Vp) changes with stress.
rock mechanical parameters (Rickman et al., 2008; Sondergeld et al., 2010). The dynamic Young's modulus and Poisson ratio are calculated from Vp, Vs and density data (Fjar et al., 2008). According to Rickman's formula (2008), BI is obtained in the longitudinal and diametrical directions (Fig. 7a). In this study, the BI for most core samples is greater than 40 along the two directions, indicating that these samples are highly brittle. According to the XRD results, BI was also calculated by quartz, clay and carbonate contents (Sondergeld et al., 2010), as shown in Fig. 7b. The rock mineral and clay mineral analyses were conducted by XRD for a total of 21 samples. In the Yingtai area, the average clay content is 30.4%, while the content of mixed-layer minerals of illite and smectite reaches 58%. The majority of their BI values is greater than 40, which is consistent with the results shown in Fig. 7a. The higher quartz and feldspar contents lead to the much stronger brittleness, which is beneficial for the production of complex fracture networks.
Table 2 Stress sensitivity of Vp for nine volcanic sedimentary rocks. Core number
Slope of Vp-stress curve
degree of stress sensitivity
C10-2-1 C608-1-1 L208-1-1 L8-4-1 L306-1-2 L306-3-2 L306-4-4 L309-3-2 L309-7-2
2.414 2.362 4.341 4.3517 2.6979 0.0716 2.1398 3.7357 2.4
Strong Strong Very strong Very strong Strong Very weak Strong Strong Strong
3.3. Stress sensitivity measurements with velocities The changes in compressional wave velocity with stress in the longitudinal direction were measured for a total of nine core samples. The changes in Vp of four cores are shown in Fig. 8, and the fitting line was obtained using the least squares method. With increasing stress, Vp increases almost linearly due to closed natural fracture and compressed pore space. The slope of the fitting line represents the sensitivity of stress to velocity; a larger slope corresponds to a more sensitive stress: if the slope is greater than 4, the stress sensitivity is very strong; if it is between 2 and 4, the stress sensitivity is strong; if it is between 1 and 2, the stress sensitivity is weak; and if it is less than 1, the stress sensitivity is very weak. The statistical results are listed in Table 2. Among the nine core samples, only one has a very weak stress sensitivity. Six samples have strong stress sensitivities, and two samples have very strong stress sensitivities. Natural fractures are observed in most of the nine samples, which is main reason for their strong stress sensitivities. At the same time, a very weak stress sensitivity reflects that the matrix is very tight. The fluid sensitivity to velocity for seven core samples was measured along the longitudinal direction. After sample preparation, the samples were air dried at room temperature and then dried at an elevated temperature of 90 C in an oven for 24 h. First, the samples were saturated with distilled water for 24 h, 48 h or 72 h for comparison with dry samples. After long-time imbibition, a uniaxial test is conducted on these samples at a constant stress rate of 2 MPa/min up to 30 MPa. The results of four samples are shown in Fig. 9. After water imbibition, the P wave velocities of volcanic sedimentary rock increase compared to dry rock, but the dependence of velocity on stress is weak (the slope is almost horizontal),
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Fig. 9. Fluid sensitivity with velocities.
and the changes in wave velocity are small; thus, fluid sensitivity is weak. After 72 h of water imbibition, Vp deceases, possibly due to the production of microfractures during the long immersion time. 3.4. Acoustic emission AE activities along the longitudinal direction were recorded in a total of seven core samples (Table 3). AE activity number is defined as AE number/stress. The AE numbers for four core samples are 2e4, and three samples have AE activity numbers of 5~11. The AE activities of the L306-3-2 and L306-4-4 samples from Well 7-3 and Well 7-4, respectively, are quite different. The AE activity number of L306-3-2 sample is greater than 10. This discrepancy might be due to the different mineralogical compositions. The XRD results show that the quartz and clay contents in L306-3-2 are 52.9% and 23.2%, respectively, whereas they are 39% and 43%, respectively, for L3064-4. The AE activities of samples L1-1-1 and L306-4-4 are stronger, which is favourable for the generation of fracture networks; thus, the fracability is better. The XRD results show that the average quartz content in the Yingtai Area is 41%. This high quartz content and natural fractures to produce stronger AE activities (Lockner et al., 1992). The cores containing natural fractures emit many AE events in the early loading stage. AE events gradually increase in the late stage because of sliding friction between quartz particles. When approaching the failure stage, the AE events dramatically increase. Larger AE activity numbers correspond to greater production of complex fractures, which is conducive to SRV fracturing. 4. Discussion 4.1. Natural fracture density inversion Rock is unusually subject to three mutually orthogonal principle stresses underground: one vertical stress and two horizontal
stresses. In the process of tectonic movement and hydraulic fracturing, if the vertical principle stress is less than the two horizontal principle stresses, it will produce horizontal fracture. Conversely, vertical fracture will be generated. The presence of natural fracture is a sign of in situ stress inhomogeneity. Its development zone is usually a weak stress zone. Therefore, natural fracture reduces the tensile strength of rock, which causes in-situ changes in stress near the wellbore and affects the generation and extension of cracks. Therefore, to a certain extent, more developed natural fractures correspond to a better fracability. Natural fractures are mechanically weak links that could improve the stimulation effect and decrease breakdown pressure by 50%. During hydraulic fracturing treatment, natural fractures and induced fractures interact. Fracturing fluid leaks into reservoirs through natural fractures, which boosts the resulting pressure to produce induced fractures. These generated induced fractures reopen natural fractures, facilitating fluid entry into reservoirs. In shale gas reservoirs, natural and induced fractures together constitute the high-speed channel of shale gas output (Tang et al., 2012). The difference between the maximum and minimum horizontal principal stress is a key factor in whether SRV fracturing can be achieved. From the above description, natural fracture density can reflect stress inhomogeneity and fracability. Rock is a two-phase body consisting of a solid matrix and fluid in the pores and cracks. Its elasticity is an effective elasticity of the body. Increasing crack damage is of vital importance to changes in the effective elastic parameters. The effective elastic properties of rock depend on two critical elastic parameters: crack density and crack aspect ratio. Effective medium theory can effectively predict the relationship between rock damage and elastic properties. The simplest inversion method of crack density is non-interactive assumption (NIA) theory, which can directly calculate crack density and aspect ratio according to elastic wave velocity (Fig. 10) guen, 2003). According to NIA (Kachanov, 1993; Schubnel and Gue theory, crack density is calculated by following formulae:
D. Wang et al. / Journal of Natural Gas Science and Engineering 23 (2015) 239e249 Table 3 AE activity parameters.
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Table 4 Calculated crack density compared with permeability and porosity.
Core number
Depth (m)
AE number
Stress (MPa)
AE Activity number
Natural facture
Failure
C10-2-1 L1-1-1 L8-2-1 L306-3-2 L306-4-4 L309-3-2 R2
2862.65-2863.45 2408.58-2410.44 3581.87-3587.51 3833.28-3835.00 4046-4047.83 4287.56-4289.12 /
299 487 221 769 362 411 238
80 60 100 74 100 80 80
3.74 8.12 2.21 10.39 3.62 5.14 2.98
Yes No Yes Yes Yes Yes No
No No No Yes No Yes No
Permeability range (mD) 0.00014e0.0171 porosity range 0.56~7.83% calculated crack density range 0.07~0.44
Average permeability (mD) 0.0026 average range 2.27% average crack density 0.1
Because its wave velocity anisotropy is very weak (Section 2.1), volcanic sedimentary rock is considered to be an isotropic medium, and NIA theory is applicable to calculate the natural fracture density. The inversion results of natural fracture density are shown in Fig. 11. The results are consistent with permeability and porosity, indicating that the inversion result is reasonable, Table 4. The casting thin section experimental results also reflect the presence of natural fractures in the Yingtai Area. Natural fracture is beneficial to fracability. 4.2. Fracability index model 4.2.1. Relationship between stress sensitivity and natural fracture The scatter plot of calculated crack density vs. the slope of stress sensitivity with velocity (Section 2.3) is plotted in Fig. 12. The fitting
Fig. 10. Coordinate system of plane coin-like circular crack; c is crack radius, w is crack width, and z ¼ w/c is crack aspect ratio. q is the included angle between the normal direction of the crack plane and axis 3 (0~90 ). The normal directional coordinate of ! the crack is n . 4 is in the range of 0 to 2p radian (Wang, 2012).
DensityðGÞ ¼
ðG0 =G 1Þð1 þ yÞ h 1 5y
16 1 y20 h¼ 9ð1 y0 =2Þ
(3)
(4)
where y is the initial Poisson's Ratio, G is the initial bulk modulus, and G0 and y0 are the bulk modulus and Poisson's Ratio when the crack is just closed, respectively.
Fig. 11. Inversion results of natural fracture density.
Fig. 12. The relationship between crack density and stress sensitivity coefficient.
Fig. 13. The relationship between crack density and FI calculated using the new model.
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curve is a straight line, indicating that they have a strong positive correlation. The regression equation is y ¼ 0.0143x þ 0.0125, and the correlation coefficient (R2) is 0.8858. This again verifies that the crack density is reasonable. Simulated natural fracture density and AE activity are also positively correlated; more natural fracture development corresponds to stronger AE activity, which is conducive to producing complex fracture networks. 4.2.2. Comprehensive fracability index model Based on the above discussion, fracability index (FI) is not only related to brittleness index (BI) but also to natural crack density (CRD), the sensitivity of velocity to stress (Str) and acoustic emission activity number (AE). The current brittleness index cannot completely evaluate FI. We integrate all of the factors into a new model to evaluate the FI. To create very complex fracture networks and maximum the SRV, the formation should have high BI, CRD, Str and AE. Thus, the mathematical model for FI is defined as follows:
FI ¼
X
Xid
(5)
i
Xid ¼
Xi Ximin Ximax Ximin
(6)
where Xid presents each of the normalized impacting factors of fracability, Xi presents each impacting factor of fracability such as BI, CRD, Str and AE, and Ximax and Ximin are the maximum and minimum values of Xi, respectively. From equation (5), fracability is the sum of normalized brittleness index (BId), natural crack density (CRDd), the stress sensitivity coefficient Vp(Strd) and AE activity number (AEd). Each of the normalized impacting factors of fracability is in the range of 0e1, and FI is thus in the range of 0e4. The formation with FI ¼ 4.0 is the best fracture candidate, and the formation with FI ¼ 0 is the worst fracture candidate (Jin et al., 2014). A larger FI corresponds to easier complex fracture network formation. With the new model presented above, we calculated the FIs of nine core samples and plotted the relationship curve between FI and CRD (Fig. 13). FI and CRD exhibit a positive linear correlation, which is consistent with Fig. 12. This indicates that the new FI model is suitable. A higher FI value indicates a better fracability. Among the nine samples, the maximum FI values are 2.145 and 2.043 for the Well 5-3 and Well 9-1 reservoirs, respectively; thus, these two wells have the best fracability. 4.2.3. Model verification 4.2.3.1. Laboratory validation. To verify the new model, 90 shale outcrop samples from southwest China were tested in the laboratory. According to the same method described above, their compressional and shear wave velocities, stress sensitivity coefficients of Vp and natural fracture densities were measured, and the corresponding FIs were then calculated using the new model. The relationship curves of the 90 core samples determined by the new model are shown in Fig. 14(a)e(d). The curve shown in Fig. 14(a) indicates a very weak linear correlation (R2 ¼ 0.025) between BI and FI. This again indicates that brittleness is not
Fig. 14. The relationship curves of 90 core samples determined using the new model: (a) the relationship between brittleness index (BI) and fracability index (FI); (b) the relationship between stress sensitivity coefficient and FI; (c) the relationship between natural crack density and FI; and (d) the relationship between crack density and fracability stress sensitivity coefficient.
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Fig. 15. (a) Formation microscanner image (FMI) logging maps of Well 9-1 at a depth of 4420e4429 m. Natural fracture density is 3.5 pieces per metre, fracture width is 5.8 mm, and fracture porosity is 0.0026%. (b) The top structural maps of the Yingtai fault depression in the yc2 formation.
equivalent to fracability. A formation with a high brittleness might not be good for fracturing because other factors such as more developed natural fractures might be lower, possibly leading to a lower FI. However, the curves shown in Fig. 14(b)e(d) all have
strong positive linear correlations (R2 ¼ 0.71, 0.77 and 0.93, respectively). Therefore, by using the new model, better statistical regularities are obtained from the data of 90 shale core samples. This demonstrates that the new model is reliable.
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4.2.3.2. Field application. The FMI logging results of Well 9-1 at a depth of 4420e4429 m are shown in Fig. 15(a); this well is located at the Yingtai fault depression (Fig. 15(b)). Many developed natural fractures are observed in Well 9-1, which is consistent with its great natural fracture density and very strong velocity sensitivity to stress. The natural fracture density is 3.5 pieces per metre, the fracture width is 5.8 mm, and fracture porosity is 0.0026%. Stimulated reservoir volume (SRV) fracturing is carried by means including high pumping rate (8e12 m3/min), small-size proppants (40e70 mesh), low-viscosity slick-water injection and fibreassisted diverting materials in order to form complex fracture networks and enhance the stimulation volume. Pump pressure fluctuation is identified under constant slick-water injection, which is due to many failure processes (Moridis et al., 2014). Every time failure occurs, pump pressure drops during the treatment. Natural gas production is 35,000 cubic metres per day after the stimulation treatment. In addition, the gas output of its offset well 9-2 reaches 76,000 cubic metres per day. This indicates that two wells both have better fracability, which agrees with the FI predicted by the new model (2.145; Fig. 13). 5. Conclusions 1) A new method for fracability evaluation is established: the brittleness, natural fracture, stress sensitivity and AE activity number are integrated to evaluate the ability to generate complex fracture. Preliminary experimental results show that this comprehensive approach for evaluating fracability is feasible. 2) Brittleness and mineral composition analysis results show that the brittleness of volcanic sedimentary rock is generally strong in the Yingtai area. 3) Wave velocity and elastic properties demonstrate that the overall wave velocity anisotropy of volcanic sedimentary rock is weak in the Yingtai area, although there is strong microheterogeneity. 4) SEM analysis and natural fracture development index show that natural fractures are developed in the volcanic sedimentary rock in the Yingtai area, which makes stress sensitivity very strong. This influences the permeability and productivity and is important in selecting a reasonable working system. 5) After water imbibition, the compressional wave (P wave) velocities of volcanic sedimentary rock increase compared to dry rock, but the sensitivity of velocity to stress is weak, and the wave velocity changes are small; thus, fluid sensitivity is weak. 6) Due to its higher quartz content, volcanic sedimentary rock produces more acoustic emission events. The cores containing natural fractures generate many acoustic emission events in the early loading stage, and AE events gradually increase in the late stage because of quartz particle sliding friction. Approaching failure, AE events dramatically increase. A greater AE activity coefficient corresponds to a stronger ability to produce complex fractures, which is conducive to fracture network formation. 7) Overall, the ability to form complex fractures is strong in the Yingtai area, especially in the Well 5-3 and Well 9-1 reservoirs. Acknowledgements This study was supported by the National Science Foundation of China (Grant No. 41304141), the Strategic Leading Science and Technology Project of Chinese Academy of Sciences, China (Grant No. XDB10050203), Project supported by the Scientific Research Foundation for the Introduced Talent of China University of Petroleum (Beijing) (Grant No. YJRC-2012-03 and No. YJRC-2013-18), Science and Technology Innovation Fund Research Project of CNPC
(Grant No. 2013D-5006-0213) and China's Ministry of Science and Technology (973 program, Grant No. 2015CB250900). Abbreviations AE BI CRD FI G0 G h Str Vp Vp(L) Vp(D) Vs Vs(L) Vs(D)
n n0 Xi Xid Ximax Ximin
acoustic emission activity number brittleness index crack density fracability index shear modulus of rock matrix initial shear modulus intermediate variable which is related to poisson ratio of rock matrix n0 stress sensitivity coefficient of compressional wave velocities compressional wave velocity compressional wave velocity along the longitudinal direction compressional wave velocity along the diametrical direction shear wave velocity shear wave velocity along the longitudinal direction shear wave velocity along the diametrical direction initial poission ratio poission ratio of rock matrix every impacting factor of fracability such as BI, CRD, Str and AE every normalized impacting factors of fracability such as BI, CRD, Str and AE the maximum value of Xi the minimum value of Xi
References Altamar, R.P., 2013. Brittleness Estimation from Seismic Measurements in Unconventional Reservoir: Application to the Barnett Shale. Doctoral dissertation. University of Oklahoma, O-klahoma. Chong, K.K., Grieser, W.V., Passman, A., et al., 2010. A completions guide book to shale-play development: a review of successful approaches toward shale-play stimulation in the last two decades. In: Canadian Unconventional Resources and International Petroleum Conference. Society of Petroleum Engineers. Fang, C., Amro, M., 2014. Influence factors of fracability in nonmarine shale. In: SPE/ EAGE European Unconventional Resources Conference and Exhibition. Society of Petroleum Engineers. Fjar, E., Holt, R.M., Raaen, A.M., et al., 2008 Petroleum Related Rock Mechanics, second ed., vol. 53. Elsevier, pp. 179e180. Guo, T.K., Zhang, S.C., Ge, H.K., 2013. A new method for evaluating ability of forming fracture network in shale reservoir. Rock Soil Mech. 34 (4), 947e954. Han, D.H., Nur, A., Morgan, D., 1986. Effects of porosity and clay content on wave velocities in sandstones. Geophysics 51 (11), 2093e2107. Jahandideh, A., Jafarpour, B., 2014. Optimization of hydraulic fracturing design under spatially variable shale fracability. In: SPE Western North American and Rocky Mountain Joint Meeting. Society of Petroleum Engineers. Jin, X.J., Shah, S.N., Roegiers, J.C., et al., 2014. Fracability evaluation in shale reservoirs- an integrated petrophysics and geomechanics approach. In: SPE Hydraulic Fracturing Technology Conference. Society of Petroleum Engineers. Kachanov, M., 1993. Elastic solids with many cracks and related problems. Adv. Appl. Mech. 30, 259e445. Liu, Y.X., 2013. Workflows for sweet spots identification in shale plays using seismic inversion and well logs. AAPG Search Discov. 1e5. Article #90187. Lockner, D.A., Byerlee, J.D., Kuksenko, V., et al., 1992. Observations of quasistatic fault growth from acoustic emissions. Int. Geophys. 51, 3e31. Moridis, G.J., Kim, J., Um, E.S., 2014. Fracture propagation fluid flow and geomechanics of water-based hydraulic fracturing in shale gas systems and electromagnetic geophysical monitoring of fluid migration. In: SPE Hydraulic Fracturing Technology Conference. Society of Petroleum Engineers. Mullen, M., Enderlin, M., 2012. Fracability IndexeMore than just calculating rock properties. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. Rickman, R., Mullen, M.J., Petre, J.E., et al., 2008. A practical use of shale petrophysics for stimulation design optimization: all shale plays are not clones of the Barnett Shale. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. guen, Y., 2003. Dispersion and anisotropy of elastic waves in Schubnel, A., Gue
D. Wang et al. / Journal of Natural Gas Science and Engineering 23 (2015) 239e249 cracked rocks. J. Geophys. Res. Solid Earth 108 (B2), 2101e2116. Sondergeld, C.H., Newsham, K.E., Comisky, J.T., et al., 2010. Petrophysical considerations in evaluating and producing shale gas resources. In: SPE Unconventional Gas Conference. Society of Petroleum Engineers. Tang, Y., Xing, Y., Li, L., et al., 2012. Influence factors and evaluation methods of the gas shale fracability. Earth Sci. Front. 19 (5), 356e363. Thomsen, L., 1986. Weak elastic anisotropy. Geophysics 51 (10), 1954e1966.
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Wang, F.P., Gale, J.F., 2009. Screening criteria for shale-gas systems. Gulf Coast Assoc. Geol. Soc. Trans. 59, 779e793. Wang, X.Q., 2012. Experimental Studies of Damage and Physical Properties Evolution on Brittle Rock Samples. Doctoral dissertation. Institute of Geophysics, China Earthquake Administration, Beijing. Yuan, J.L., Deng, J., Zhang, D., et al., 2013. Fracability evaluation of shale-gas reservoirs. Acta Pet. Sin. 34 (3), 523e527.
Contents lists available at ScienceDirect
Journal of Natural Gas Science and Engineering journal homepage: www.elsevier.com/locate/jngse
A novel experimental approach for fracability evaluation in tight-gas reservoirs Daobing Wang a, b, *, Hongkui Ge a, b, Xiaoqiong Wang a, b, Jianbo Wang a, b, Fanbao Meng a, b, Yu Suo a, b, Peng Han a, b a b
Unconventional Natural Gas Research Institute, China University of Petroleum, Beijing 102249, People's Republic of China State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, People's Republic of China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 November 2014 Received in revised form 26 January 2015 Accepted 28 January 2015 Available online
Hydraulic fracturing is an effective stimulation method for the economic development of tight-gas reservoirs in which extremely low matrix permeability requires complex fracture networks. Petrophysical/mechanical experiments and XRD/SEM analyses demonstrate that volcanic sedimentary rock is characterized by developed natural fracture, strong brittleness, stress sensitivity, AE activity, weak anisotropy, and fluid sensitivity. Fracability index is often utilized as a key parameter to evaluate the ability to generate fracture networks. In this study, a new systematic experimental approach and a new mathematical model are established for the comprehensive evaluation of the fracability of tight-gas formations. These two methods integrate natural fracture, stress sensitivity, rock anisotropic nature, AE activity and crack density. The results indicate that the fracability index (FI) and the calculated crack density (CRD) are positively linearly correlated. The method is successfully applied to evaluate the fracability of the Yingtai gas field in northeast China. © 2015 Elsevier B.V. All rights reserved.
Keywords: Tight-gas reservoir Fracability evaluation Fracture network Crack density Velocity anisotropy Stress sensitivity
1. Introduction The interest in unconventional gas reservoirs has rapidly increased worldwide in recent years due to the combined technology of horizontal well drilling and multi-staged hydraulic fracturing (Liu, 2013). Tight-gas reservoirs are characterized by very low matrix permeability. They need hydraulic fracturing technology to provide sufficient permeability and economic productivity. The conventional hydraulic fracturing technique often produces two symmetric fractures and struggles to achieve the economic development of tight-gas reservoirs. Stimulated reservoir volume (SRV) fracturing is an efficient stimulation method that can generate complex or “tree” fracture networks (Fig. 1). Therefore, stimulation challenges associated with a tight gas alter the fracture design concept from a simple fracture to a complex fracture. Fracability is the capability of shale gas plays to effectively carry out hydraulic fracturing and generate fracture networks (Chong et al., 2010). Fracability index is a key parameter to evaluate
* Corresponding author. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, People's Republic of China. E-mail address: [email protected] (D. Wang). http://dx.doi.org/10.1016/j.jngse.2015.01.039 1875-5100/© 2015 Elsevier B.V. All rights reserved.
whether oil and gas reservoirs can effectively produce complex fractures during treatment. Currently, many researchers use brittleness index to directly evaluate fracability. Rickman et al. (2008) proposed a brittleness index formula based on the Poisson ratio and Young's modulus, using their crossplot to evaluate the brittleness of Barnett shale. A lower Poisson ratio corresponds to a higher Young's modulus and a more brittle rock in which it is easy to generate complex fracture. Sondergeld et al. (2010) adopted the proportion of quartz-carbonate-clay to calculate fracability index. The most brittle rock has the most quartz and least clay. Therefore, the brittleness formula is BI ¼ Qz/(Qz þ Ca þ Cly), where Qz is the fractional quartz content, and Ca is the calcite content by weight in the rock. In fact, in addition to quartz, feldspar and dolomite, which are often neglected when evaluating shale brittleness, are also brittle components in shale gas plays (Wang and Gale 2009; Altamar, 2013; Fang et al., 2014). Considering the dolomite component, the brittleness index can be corrected as BI ¼ (Qz þ Dol)/(Qz þ Dol þ Ca þ Cly þ TOC), in which Dol is dolomite content, Ca is the calcite content, and TOC is the total organic carbon content by weight. Fracability is affected not only by rock brittleness but also by natural fracture, diagenesis and other factors. Tang et al. (2012) established a comprehensive mathematical model to calculate
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Fig. 1. Various hydraulic fracture morphologies: from simple fracture transition to tree fracture network.
shale fracability index using analytic hierarchy process. The model was successfully applied in the region of Southeast Chongqing, Southwest China. Guo et al. (2013) used soundless crack agents to expand the borehole and produce artificial fractures and then evaluated crack density by fractal dimension. Yuan et al. (2013) evaluated the shale fracability based on the brittleness index, fracture toughness and rock mechanical properties and established three-dimensional distribution figures of shale fracability. Jin et al. (2014) integrated both brittleness and energy dissipation during hydraulic fracturing. The formation with the higher fracability index is considered to be the better fracturing candidate. Heterogeneities in shale fracability play an important role in optimizing the hydraulic fracture parameter (Jahandideh and Jafarpour, 2014). An optimization approach for hydraulic fracturing design under spatially variable shale fracability was developed, and the results of several numerical experiments showed that the proposed method is effective and suitable. Mullen and Enderlin (2012) defined a new fracability index, the complex fracability index (CFI), that integrated the sedimentary fabric, mineral distribution, pre-existing weakness planes and present-day stress state. The CFI methodology is suitable for both shale gas plays and tight sandstone driven by the data that is available. From the above study, fracability is not completely equal to brittleness; it is related to many factors. The geometry and complexity of hydraulic fracture stimulation treatments is largely controlled by the heterogeneous and anisotropic nature of rocks (Mullen and Enderlin, 2012). It is difficult and time-consuming to define a uniform mathematical formula for fracability index considering all of the factors. Investigations of fracability index have mainly concentrated on shale gas reservoirs, while the evaluation of fracability is rare for tight gas reservoirs. Herein, we adopt a new systematic experimental approach integrating natural fracture, stress sensitivity, rock anisotropic nature, AE activity and crack density for the comprehensive evaluation of the fracability of tight gas formations.
volcanic breccia. Its pore type includes matrix dissolved pore, phenocryst dissolved pore, gas pore and intragranular dissolved pore. The lithology of the yc2 reservoir is mainly sandstone, conglomerate and tuffaceous sandstone. Its pore type includes intragranular dissolved pore, intergranular pore and intragranular pore. The reservoir space types are mainly dissolved pore (90%) and intergranular pore (7%). The yc1 formation has mainly high-angle tectonic fracturing extending along the south and north directions. The crack density is low, less than three metres per crack, and is mainly distributed in the top position of the volcanic reservoir. High-angle tectonic fractures also exist in the yc2 formation, but the fractures are small (widths of 5~50 microns), almost filled and less developed. Because of large difference in planar distribution, the developed zones are mainly concentrated near Well Longshen303 and Well Longshen202. The average porosity and permeability of the yc1 formation are 9.6% (middle porosity) and 0.36 md (extra-low permeability reservoir), respectively. The average porosity and permeability of the yc2 formation are 7% (extra-low porosity) and 0.05 md (tightgas reservoir), respectively. These two reservoirs are both dry gas reservoir with normal temperature and pressure systems. 2.2. Sample preparation The volcanic rock samples were cored from gas wells in the Yingtai Gas Field (Jilin Province, northeast China) at depths of 1900~4500 m. All samples were cored and polished to cylinders of 25 mm in diameter and 50 mm in length (Fig. 2). Their basic physical parameters are shown in Table 1. The main rock type is volcanic sedimentary rock (tuff gritstone) and sandstone. Their structures are relatively stable. Intergranular pores are preserved relatively well. Secondary pores are developed and are dominated by intragranular dissolved pores, or intergranular pores, followed by matrix dissolved pores. Among the secondary fractures, structural fractures are predominant, followed by dissolved fractures, which were shown to be partially open by thin section analysis. The granularity is coarse (>0.5 mm), as demonstrated by casting thin
2. Experimental samples and procedures 2.1. Geologic features The Yingtai Gas Field is located in the Songliao Basin, Jilin Province, northeast China. The overall structural feature is two east-dipping nose-like structures located in the north and south and divided by NWeSE subsags. North-east and north-south faults are developed. The gas reservoir is buried at a depth of 3000~5000 m. The complex lithology of tight-gas reservoirs mainly contains two sets of reservoirs: volcanic reservoirs in the first member of the Yingcheng Formation, termed yc1; and pyroclastic sedimentary reservoirs in the second member of the Yingcheng Formation, termed yc2. The lithology of the yc1 reservoir is mainly rhyolite, tuff and
Fig. 2. The volcanic sedimentary rock samples from Jilin Oilfield in northeast China.
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Table 1 Basic physical parameters of rock samples. Well name
Core number
Depth (m)
Weight (g)
Diameter (mm)
Length (mm)
Density (g/cm3)
Permeability (103 mm2)
Porosity (%)
1-1 1-2 2-1 3-1 4-1 5-1 5-2 5-3 6-1 6-2 7-1 7-2 7-3 7-4 8-1 8-2 9-1
C608-1-1 C608-2-1 C607-2-2 C7-1-2 C10-2-1 L8-1-2 L8-2-1 L8-4-1 L309-3-2 L309-7-2 L306-1-2 L306-2-1 L306-3-2 L306-4-4 L1-1-1 L1-2-1 L208-1-1
1917.04-1918.28 2455-2457 2728.85-2730.27 1862.20-1866.00 2862.65-2863.45 3575.97-3577.9 3581.87-3587.51 3836.3-3838.54 4287.56-4289.12 4294.63-4296.46 3687.10-3688.28 3831.50-3833.28 3833.28-3835.00 4046-4047.83 2408.58-2410.44 2410.55-2412.55 4422.70-4424.65
65.96 60.49 63.30 67.88 69.72 67.57 66.01 63.56 66.14 60.01 61.34 61.29 60.27 67.86 65.19 63.16 63.77
24.93 25.05 24.95 24.89 24.92 24.86 24.83 24.90 25.35 24.77 24.85 24.89 24.85 24.82 24.86 24.83 24.87
51.40 52.81 53.04 52.88 54.13 53.97 52.86 51.98 50.94 49.53 49.97 50.28 48.84 55.53 52.36 50.51 48.82
2.63 2.32 2.44 2.64 2.64 2.58 2.58 2.51 2.57 2.51 2.53 2.51 2.54 2.53 2.57 2.58 2.69
0.0022 0.00282 0.002766 0.001212 0.0413 0.014224 0.0014 0.0006 0.0012 0.007504 0.000747 0.0021 0.001312 0.0067 0.001194 0.002459 0.008343
5.95 1.23 2.46 1.11 6.85 7.37 1.97 1.26 3.58 3.6 0.616 7.8 4.24 0.687 5.95 3.74 7.58
section testing (Fig. 3). Pore-throat analysis statistics using mercury intrusion show that most of the pores are less than 0.1 micron in size, reflecting the complex pore structure characteristics. The porosity is in the range of 0.56%~7.83% with an average of 2.9% in the Yingtai area. In the gas pay, porosity is in the range of 0.67% ~5.91% with an average of 2.27%. The Klinkenberg permeability is in the range of 0.0006e0.0413 mD with an average of 0.0079 mD and most being below 0.01 mD.
2.3. Experimental equipment and procedure An ultrasonic velocity measurement system composed of an Olympus 5077 PR pulse-generator, oscilloscope and V157 shear wave transducer with a 5-MHz dominant frequency was employed at room temperature and atmospheric pressure (Fig. 4). The waveform sampling rate was 3.125 MHz. To improve the signal-tonoise ratio, we adopted a shear wave couplant and U-shape tools to bring the core ends into close contact with the transducers. Compressional wave velocities (P waves) were conducted by handy press under uniaxial stress at a rate of 2 MPa/min up to 60 MPa. The P waves were measured along the direction parallel to the stress using a classic ultrasonic pulse transmission technique between an emitting and a receiving transducer (Nano30) with a dominant frequency of 250 kHz. For each velocity measurement, 100 acoustic waveforms were stacked to increase the signal-tonoise ratio. The arrival times were determined by cross-
Fig. 4. The oscilloscope and pulse-generator.
correlation to a reference waveform so that the relative error in velocity measurements is estimated to be lower than 1%. Acoustic emission was recorded by an ASC digital oscilloscope (Cecchis) under uniaxial stress. The preamplifier gain was 40 db, the sampling rate was 10 MHz, and the analogue filter bandwidth was 100 kHze2 MHz. The threshold value was set to 8 mV. Twenty acoustic waveforms were stacked in order to increase the signal-tonoise ratio.
Fig. 3. Casting thin section results: the observation of a natural but undeveloped fracture with the characteristics of a tight reservoir. The red scale is 628.52 mm in the top left corner of the figures.
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Fig. 5. Vp and Vs velocities of 17 core samples in ascending order. Vp(L) and Vs(L) represent the compressional wave velocities along the longitudinal direction, and Vp(D) and Vs(D) represent the shear wave velocities along the diametrical direction.
3. Experimental results 3.1. Elastic velocity and anisotropy at room temperature and atmospheric pressure The elastic velocities of 17 core samples were obtained along the longitudinal and diametrical directions (Fig. 5). Vp is in the range of 3.5~6 km/s, and Vs is in the range of 2~3.4 km/s. Both show significant variation, which is possibly related to the mineralogical composition and heterogeneity as the clay content is only 9.6% in Well 5-3, while it reaches 30% in Well 5-1 according to the XRD results. A higher clay content corresponds to a lower wave velocity (Han, et al., 1986), which is in line with Fig. 6. Thomsen (1986) proposed a characterization method of weak anisotropic medium parameters ε and g, which represent Vp and Vs anisotropy, respectively. They are defined as follows: Fig. 7. (a) Brittleness index of 17 core samples in ascending order along the longitudinal and diametrical directions. BI(L) and BI(D) represent brittleness index along the longitudinal and diametrical directions, respectively. (b) Brittleness index calculated from the XRD results.
Vp ðLÞ Vp ðDÞ 100 Vp anisotropy ¼ min Vp ðLÞ; Vp ðDÞ Vs anisotropy ¼
jVs ðLÞ Vs ðDÞj 100 minfVs ðLÞ; Vs ðDÞg
(1)
(2)
Unapparent bedding planes are observed in the 17 core samples, they are simply viewed as weak anisotropic media. The Thomsen parameters ε and g were calculated from formulae (1) and (2), and the results are shown in Fig. 6 in ascending order. In Fig. 6, the blue horizontal line corresponds to a value of 10, which distinguishes between weak and strong anisotropy. With the exceptions of Wells 9-1 and 5-3, most core samples show weak anisotropy. Well 5-3 has strong anisotropy because of the observed natural fracture. Fig. 6. Vp and Vs velocity anisotropy parameters of 17 core samples in ascending order. If the value is above the blue line (corresponding the vertical coordinate value of 10), anisotropy is strong; conversely, anisotropy is weak. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
3.2. Brittleness analysis The brittleness is usually evaluated by brittleness index (BI), which is calculated from the XRD mineralogical composition or
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Fig. 8. Compressional wave velocity (Vp) changes with stress.
rock mechanical parameters (Rickman et al., 2008; Sondergeld et al., 2010). The dynamic Young's modulus and Poisson ratio are calculated from Vp, Vs and density data (Fjar et al., 2008). According to Rickman's formula (2008), BI is obtained in the longitudinal and diametrical directions (Fig. 7a). In this study, the BI for most core samples is greater than 40 along the two directions, indicating that these samples are highly brittle. According to the XRD results, BI was also calculated by quartz, clay and carbonate contents (Sondergeld et al., 2010), as shown in Fig. 7b. The rock mineral and clay mineral analyses were conducted by XRD for a total of 21 samples. In the Yingtai area, the average clay content is 30.4%, while the content of mixed-layer minerals of illite and smectite reaches 58%. The majority of their BI values is greater than 40, which is consistent with the results shown in Fig. 7a. The higher quartz and feldspar contents lead to the much stronger brittleness, which is beneficial for the production of complex fracture networks.
Table 2 Stress sensitivity of Vp for nine volcanic sedimentary rocks. Core number
Slope of Vp-stress curve
degree of stress sensitivity
C10-2-1 C608-1-1 L208-1-1 L8-4-1 L306-1-2 L306-3-2 L306-4-4 L309-3-2 L309-7-2
2.414 2.362 4.341 4.3517 2.6979 0.0716 2.1398 3.7357 2.4
Strong Strong Very strong Very strong Strong Very weak Strong Strong Strong
3.3. Stress sensitivity measurements with velocities The changes in compressional wave velocity with stress in the longitudinal direction were measured for a total of nine core samples. The changes in Vp of four cores are shown in Fig. 8, and the fitting line was obtained using the least squares method. With increasing stress, Vp increases almost linearly due to closed natural fracture and compressed pore space. The slope of the fitting line represents the sensitivity of stress to velocity; a larger slope corresponds to a more sensitive stress: if the slope is greater than 4, the stress sensitivity is very strong; if it is between 2 and 4, the stress sensitivity is strong; if it is between 1 and 2, the stress sensitivity is weak; and if it is less than 1, the stress sensitivity is very weak. The statistical results are listed in Table 2. Among the nine core samples, only one has a very weak stress sensitivity. Six samples have strong stress sensitivities, and two samples have very strong stress sensitivities. Natural fractures are observed in most of the nine samples, which is main reason for their strong stress sensitivities. At the same time, a very weak stress sensitivity reflects that the matrix is very tight. The fluid sensitivity to velocity for seven core samples was measured along the longitudinal direction. After sample preparation, the samples were air dried at room temperature and then dried at an elevated temperature of 90 C in an oven for 24 h. First, the samples were saturated with distilled water for 24 h, 48 h or 72 h for comparison with dry samples. After long-time imbibition, a uniaxial test is conducted on these samples at a constant stress rate of 2 MPa/min up to 30 MPa. The results of four samples are shown in Fig. 9. After water imbibition, the P wave velocities of volcanic sedimentary rock increase compared to dry rock, but the dependence of velocity on stress is weak (the slope is almost horizontal),
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Fig. 9. Fluid sensitivity with velocities.
and the changes in wave velocity are small; thus, fluid sensitivity is weak. After 72 h of water imbibition, Vp deceases, possibly due to the production of microfractures during the long immersion time. 3.4. Acoustic emission AE activities along the longitudinal direction were recorded in a total of seven core samples (Table 3). AE activity number is defined as AE number/stress. The AE numbers for four core samples are 2e4, and three samples have AE activity numbers of 5~11. The AE activities of the L306-3-2 and L306-4-4 samples from Well 7-3 and Well 7-4, respectively, are quite different. The AE activity number of L306-3-2 sample is greater than 10. This discrepancy might be due to the different mineralogical compositions. The XRD results show that the quartz and clay contents in L306-3-2 are 52.9% and 23.2%, respectively, whereas they are 39% and 43%, respectively, for L3064-4. The AE activities of samples L1-1-1 and L306-4-4 are stronger, which is favourable for the generation of fracture networks; thus, the fracability is better. The XRD results show that the average quartz content in the Yingtai Area is 41%. This high quartz content and natural fractures to produce stronger AE activities (Lockner et al., 1992). The cores containing natural fractures emit many AE events in the early loading stage. AE events gradually increase in the late stage because of sliding friction between quartz particles. When approaching the failure stage, the AE events dramatically increase. Larger AE activity numbers correspond to greater production of complex fractures, which is conducive to SRV fracturing. 4. Discussion 4.1. Natural fracture density inversion Rock is unusually subject to three mutually orthogonal principle stresses underground: one vertical stress and two horizontal
stresses. In the process of tectonic movement and hydraulic fracturing, if the vertical principle stress is less than the two horizontal principle stresses, it will produce horizontal fracture. Conversely, vertical fracture will be generated. The presence of natural fracture is a sign of in situ stress inhomogeneity. Its development zone is usually a weak stress zone. Therefore, natural fracture reduces the tensile strength of rock, which causes in-situ changes in stress near the wellbore and affects the generation and extension of cracks. Therefore, to a certain extent, more developed natural fractures correspond to a better fracability. Natural fractures are mechanically weak links that could improve the stimulation effect and decrease breakdown pressure by 50%. During hydraulic fracturing treatment, natural fractures and induced fractures interact. Fracturing fluid leaks into reservoirs through natural fractures, which boosts the resulting pressure to produce induced fractures. These generated induced fractures reopen natural fractures, facilitating fluid entry into reservoirs. In shale gas reservoirs, natural and induced fractures together constitute the high-speed channel of shale gas output (Tang et al., 2012). The difference between the maximum and minimum horizontal principal stress is a key factor in whether SRV fracturing can be achieved. From the above description, natural fracture density can reflect stress inhomogeneity and fracability. Rock is a two-phase body consisting of a solid matrix and fluid in the pores and cracks. Its elasticity is an effective elasticity of the body. Increasing crack damage is of vital importance to changes in the effective elastic parameters. The effective elastic properties of rock depend on two critical elastic parameters: crack density and crack aspect ratio. Effective medium theory can effectively predict the relationship between rock damage and elastic properties. The simplest inversion method of crack density is non-interactive assumption (NIA) theory, which can directly calculate crack density and aspect ratio according to elastic wave velocity (Fig. 10) guen, 2003). According to NIA (Kachanov, 1993; Schubnel and Gue theory, crack density is calculated by following formulae:
D. Wang et al. / Journal of Natural Gas Science and Engineering 23 (2015) 239e249 Table 3 AE activity parameters.
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Table 4 Calculated crack density compared with permeability and porosity.
Core number
Depth (m)
AE number
Stress (MPa)
AE Activity number
Natural facture
Failure
C10-2-1 L1-1-1 L8-2-1 L306-3-2 L306-4-4 L309-3-2 R2
2862.65-2863.45 2408.58-2410.44 3581.87-3587.51 3833.28-3835.00 4046-4047.83 4287.56-4289.12 /
299 487 221 769 362 411 238
80 60 100 74 100 80 80
3.74 8.12 2.21 10.39 3.62 5.14 2.98
Yes No Yes Yes Yes Yes No
No No No Yes No Yes No
Permeability range (mD) 0.00014e0.0171 porosity range 0.56~7.83% calculated crack density range 0.07~0.44
Average permeability (mD) 0.0026 average range 2.27% average crack density 0.1
Because its wave velocity anisotropy is very weak (Section 2.1), volcanic sedimentary rock is considered to be an isotropic medium, and NIA theory is applicable to calculate the natural fracture density. The inversion results of natural fracture density are shown in Fig. 11. The results are consistent with permeability and porosity, indicating that the inversion result is reasonable, Table 4. The casting thin section experimental results also reflect the presence of natural fractures in the Yingtai Area. Natural fracture is beneficial to fracability. 4.2. Fracability index model 4.2.1. Relationship between stress sensitivity and natural fracture The scatter plot of calculated crack density vs. the slope of stress sensitivity with velocity (Section 2.3) is plotted in Fig. 12. The fitting
Fig. 10. Coordinate system of plane coin-like circular crack; c is crack radius, w is crack width, and z ¼ w/c is crack aspect ratio. q is the included angle between the normal direction of the crack plane and axis 3 (0~90 ). The normal directional coordinate of ! the crack is n . 4 is in the range of 0 to 2p radian (Wang, 2012).
DensityðGÞ ¼
ðG0 =G 1Þð1 þ yÞ h 1 5y
16 1 y20 h¼ 9ð1 y0 =2Þ
(3)
(4)
where y is the initial Poisson's Ratio, G is the initial bulk modulus, and G0 and y0 are the bulk modulus and Poisson's Ratio when the crack is just closed, respectively.
Fig. 11. Inversion results of natural fracture density.
Fig. 12. The relationship between crack density and stress sensitivity coefficient.
Fig. 13. The relationship between crack density and FI calculated using the new model.
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curve is a straight line, indicating that they have a strong positive correlation. The regression equation is y ¼ 0.0143x þ 0.0125, and the correlation coefficient (R2) is 0.8858. This again verifies that the crack density is reasonable. Simulated natural fracture density and AE activity are also positively correlated; more natural fracture development corresponds to stronger AE activity, which is conducive to producing complex fracture networks. 4.2.2. Comprehensive fracability index model Based on the above discussion, fracability index (FI) is not only related to brittleness index (BI) but also to natural crack density (CRD), the sensitivity of velocity to stress (Str) and acoustic emission activity number (AE). The current brittleness index cannot completely evaluate FI. We integrate all of the factors into a new model to evaluate the FI. To create very complex fracture networks and maximum the SRV, the formation should have high BI, CRD, Str and AE. Thus, the mathematical model for FI is defined as follows:
FI ¼
X
Xid
(5)
i
Xid ¼
Xi Ximin Ximax Ximin
(6)
where Xid presents each of the normalized impacting factors of fracability, Xi presents each impacting factor of fracability such as BI, CRD, Str and AE, and Ximax and Ximin are the maximum and minimum values of Xi, respectively. From equation (5), fracability is the sum of normalized brittleness index (BId), natural crack density (CRDd), the stress sensitivity coefficient Vp(Strd) and AE activity number (AEd). Each of the normalized impacting factors of fracability is in the range of 0e1, and FI is thus in the range of 0e4. The formation with FI ¼ 4.0 is the best fracture candidate, and the formation with FI ¼ 0 is the worst fracture candidate (Jin et al., 2014). A larger FI corresponds to easier complex fracture network formation. With the new model presented above, we calculated the FIs of nine core samples and plotted the relationship curve between FI and CRD (Fig. 13). FI and CRD exhibit a positive linear correlation, which is consistent with Fig. 12. This indicates that the new FI model is suitable. A higher FI value indicates a better fracability. Among the nine samples, the maximum FI values are 2.145 and 2.043 for the Well 5-3 and Well 9-1 reservoirs, respectively; thus, these two wells have the best fracability. 4.2.3. Model verification 4.2.3.1. Laboratory validation. To verify the new model, 90 shale outcrop samples from southwest China were tested in the laboratory. According to the same method described above, their compressional and shear wave velocities, stress sensitivity coefficients of Vp and natural fracture densities were measured, and the corresponding FIs were then calculated using the new model. The relationship curves of the 90 core samples determined by the new model are shown in Fig. 14(a)e(d). The curve shown in Fig. 14(a) indicates a very weak linear correlation (R2 ¼ 0.025) between BI and FI. This again indicates that brittleness is not
Fig. 14. The relationship curves of 90 core samples determined using the new model: (a) the relationship between brittleness index (BI) and fracability index (FI); (b) the relationship between stress sensitivity coefficient and FI; (c) the relationship between natural crack density and FI; and (d) the relationship between crack density and fracability stress sensitivity coefficient.
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Fig. 15. (a) Formation microscanner image (FMI) logging maps of Well 9-1 at a depth of 4420e4429 m. Natural fracture density is 3.5 pieces per metre, fracture width is 5.8 mm, and fracture porosity is 0.0026%. (b) The top structural maps of the Yingtai fault depression in the yc2 formation.
equivalent to fracability. A formation with a high brittleness might not be good for fracturing because other factors such as more developed natural fractures might be lower, possibly leading to a lower FI. However, the curves shown in Fig. 14(b)e(d) all have
strong positive linear correlations (R2 ¼ 0.71, 0.77 and 0.93, respectively). Therefore, by using the new model, better statistical regularities are obtained from the data of 90 shale core samples. This demonstrates that the new model is reliable.
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4.2.3.2. Field application. The FMI logging results of Well 9-1 at a depth of 4420e4429 m are shown in Fig. 15(a); this well is located at the Yingtai fault depression (Fig. 15(b)). Many developed natural fractures are observed in Well 9-1, which is consistent with its great natural fracture density and very strong velocity sensitivity to stress. The natural fracture density is 3.5 pieces per metre, the fracture width is 5.8 mm, and fracture porosity is 0.0026%. Stimulated reservoir volume (SRV) fracturing is carried by means including high pumping rate (8e12 m3/min), small-size proppants (40e70 mesh), low-viscosity slick-water injection and fibreassisted diverting materials in order to form complex fracture networks and enhance the stimulation volume. Pump pressure fluctuation is identified under constant slick-water injection, which is due to many failure processes (Moridis et al., 2014). Every time failure occurs, pump pressure drops during the treatment. Natural gas production is 35,000 cubic metres per day after the stimulation treatment. In addition, the gas output of its offset well 9-2 reaches 76,000 cubic metres per day. This indicates that two wells both have better fracability, which agrees with the FI predicted by the new model (2.145; Fig. 13). 5. Conclusions 1) A new method for fracability evaluation is established: the brittleness, natural fracture, stress sensitivity and AE activity number are integrated to evaluate the ability to generate complex fracture. Preliminary experimental results show that this comprehensive approach for evaluating fracability is feasible. 2) Brittleness and mineral composition analysis results show that the brittleness of volcanic sedimentary rock is generally strong in the Yingtai area. 3) Wave velocity and elastic properties demonstrate that the overall wave velocity anisotropy of volcanic sedimentary rock is weak in the Yingtai area, although there is strong microheterogeneity. 4) SEM analysis and natural fracture development index show that natural fractures are developed in the volcanic sedimentary rock in the Yingtai area, which makes stress sensitivity very strong. This influences the permeability and productivity and is important in selecting a reasonable working system. 5) After water imbibition, the compressional wave (P wave) velocities of volcanic sedimentary rock increase compared to dry rock, but the sensitivity of velocity to stress is weak, and the wave velocity changes are small; thus, fluid sensitivity is weak. 6) Due to its higher quartz content, volcanic sedimentary rock produces more acoustic emission events. The cores containing natural fractures generate many acoustic emission events in the early loading stage, and AE events gradually increase in the late stage because of quartz particle sliding friction. Approaching failure, AE events dramatically increase. A greater AE activity coefficient corresponds to a stronger ability to produce complex fractures, which is conducive to fracture network formation. 7) Overall, the ability to form complex fractures is strong in the Yingtai area, especially in the Well 5-3 and Well 9-1 reservoirs. Acknowledgements This study was supported by the National Science Foundation of China (Grant No. 41304141), the Strategic Leading Science and Technology Project of Chinese Academy of Sciences, China (Grant No. XDB10050203), Project supported by the Scientific Research Foundation for the Introduced Talent of China University of Petroleum (Beijing) (Grant No. YJRC-2012-03 and No. YJRC-2013-18), Science and Technology Innovation Fund Research Project of CNPC
(Grant No. 2013D-5006-0213) and China's Ministry of Science and Technology (973 program, Grant No. 2015CB250900). Abbreviations AE BI CRD FI G0 G h Str Vp Vp(L) Vp(D) Vs Vs(L) Vs(D)
n n0 Xi Xid Ximax Ximin
acoustic emission activity number brittleness index crack density fracability index shear modulus of rock matrix initial shear modulus intermediate variable which is related to poisson ratio of rock matrix n0 stress sensitivity coefficient of compressional wave velocities compressional wave velocity compressional wave velocity along the longitudinal direction compressional wave velocity along the diametrical direction shear wave velocity shear wave velocity along the longitudinal direction shear wave velocity along the diametrical direction initial poission ratio poission ratio of rock matrix every impacting factor of fracability such as BI, CRD, Str and AE every normalized impacting factors of fracability such as BI, CRD, Str and AE the maximum value of Xi the minimum value of Xi
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