Seismic studies of coal bed methane content in the west coal mining area of Qinshui Basin

International Journal of Mining Science and Technology 23 (2013) 795–803

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International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst

Seismic studies of coal bed methane content in the west coal mining area of Qinshui Basin Zou Guangui ⇑, Peng Suping, Yin Caiyun, Xu Yanyong, Chen Fengying, Liu Jinkai State Key Laboratory of Coal Resource and Safe Mining, China University of Mining & Technology, Beijing 100083, China

a r t i c l e

i n f o

Article history: Received 22 March 2013 Received in revised form 29 April 2013 Accepted 31 May 2013 Available online 3 December 2013 Keywords: Coal bed methane content Amplitude versus offset AVO attribute Correlation coefficient

a b s t r a c t The coal bed methane content (CBMC) in the west mining area of Jincheng coalfield, southeastern Qinshui Basin, is studied based on seismic data and well-logs together with laboratory measurements. The results show that the Shuey approximation has better adaptability according to the Zoeppritz equation result; the designed fold number for an ordinary seismic data is sufficient for post-stack data but insufficient for pre-stack data regarding the signal to noise ratio (SNR). Therefore a larger grid analysis was created in order to improve the SNR. The velocity field created by logging is better than that created by stack velocity in both accuracy and effectiveness. A reasonable distribution of the amplitude versus offset (AVO) attributes can be facilitated by taking the AVO response from logging as a standard for calibrating the amplitude distribution. Some AVO attributes have a close relationship with CBMC. The worst attribute is polarization magnitude, for which the correlation coefficient is 0.308; and the best attribute is the polarization product from intercept, of which the correlation coefficient is 0.8136. CBMC predicted by AVO attributes is better overall than that predicted by direct interpolation of CBMC; the validation error of the former is 14.47%, which is lower than that of the latter 23.30%. CBMC of this area ranges from 2.5 m3/t to 22 m3/t. Most CBMC in the syncline is over 10 m3/t, but it is below 10 m3/t in the anticline; on the whole, CBMC in the syncline is higher than that in anticline. Ó 2013 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction Coal bed methane content (CBMC) is a measure of the quantity of methane stored in coals, and is important for many applications, including the quantitative assessment of methane resources and methane extraction and control. CBMC is usually measured by direct or indirect methods. The direct measurements include the United States Bureau of Mines method, Smith–Williams’ law, etc.; and the indirect measurements include the gas gradient method, isothermal assay method, etc. [1,2]. The measuring samples are distributed randomly or on a sparse grid, for example, the sampling grid of the methane content in Sihe coal mine is usually 200 m  200 m. With advances in research on the relationship between geological factors and CBMC in recent decades, numerical models have been increasingly used for evaluating CBMC in these applications. CBMC is controlled by geological factors such as coal rank, coal thickness, surrounding rock lithology, coal bed depth, structure development, hydro-geological conditions and magmatic activity [3–10]. By analyzing the geological factors that affect CBMC in Qinshui Basin, the BP neural network model was used to predict ⇑ Corresponding author. Tel.: +86 10 62331305.

CBMC. This model contains effective buried depth of coal, moisture, ash and maximum vitrinite reactance [11]. The support vector machine model was also used in which the degree of coal metamorphism, reservoir pressure, temperature and coal quality characteristics are included [12]. The fractal-ARIMA prediction method, based on mathematical statistics theory, was used to predict coal bed gas output in order to improve prediction accuracy [13]. These results remain as a scatter type due to dispersive geological factors. In order to obtain a fine distribution of methane resources, we generally adopt the scatter methane content extrapolation. CBMC is considered to have a functional relationship with these geological factors. These geological factors remain fairly constant in a particular field, whereas they may change considerably, both laterally and vertically, within the field [14]. Thus the distribution of CBMC is difficult to be predicted at locations far from the control point. Seismic exploration technology has been widely used to determine subsurface structures in coal mining. Seismic data usually has a high density in the horizontal direction; for example, the seismic grid in Sihe coal mine is generally 10 m  5 m, which is nearly 800 times denser compared with a 200 m  200 m grid used in borehole exploration. Therefore, seismic data has an advantage in the prediction of CBMC at locations remote from control points.

E-mail address: [email protected] (G. Zou). 2095-2686/$ - see front matter Ó 2013 Published by Elsevier B.V. on behalf of China University of Mining & Technology. http://dx.doi.org/10.1016/j.ijmst.2013.10.003

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

2.1. Structural features Qinshui Basin is located at the southeastern end of Shanxi province; it is a widely shallow syncline with a NNE axis, and surrounded by tectonic belts. On the north side of the basin is the Wutaishan uplift; on the south side is the Zhongtiaoshan uplift; on the east side, the Taihangshan uplift and on the west side, the Huoshan uplift. The terrain of the Qinshui Basin is comprised of many hills and few valleys. Intermountain basins and river valleys are widely distributed in the basin. The uplifts are mainly made up of these strata from Sinian to Ordovician ages. In the basin, Triassic strata and Permian strata are partly exposed, most of which are covered by Neogene and Quaternary strata (Fig. 1). 2.2. Coal-bearing strata The Permian Shanxi formation and the Lower Carboniferous Taiyuan formation are the major coal bearing strata in the Qinshui Coalfield (Fig. 2).

Coloum 1:1000

Maker bed and coal No.

Series

Formation

System

Stratigraphic units

Thickness (m)

(1) The Permian Shanxi formation (P1s). Three coal seams are present, named coal 1 (C1), coal 2 (C2), and coal 3 (C3) from top to bottom. The total average thickness is 5.58 m, and the coal-bearing factor is 11.99%. The C3 seam is very uniform over the whole basin and beneficial to be mined; however the remaining coal seams are so variable that they are not industrially valuable to be mined. The C3 seam is 24.08– 48.53 m below the sandstone (K8); 0–12.80 m above the

Permian

The problem is how to obtain the relationship between CBMC and seismic attributes. There are hundreds of seismic attributes in general, and they can be classified in many different ways, such as amplitude, phase, frequency, etc. [15]. Among them, the amplitude versus offset (AVO) attributes are closely related to oil and gas [16–18,13]. AVO is a variation in seismic reflection amplitude changing with the distance between shot point and receiver. An AVO anomaly is most commonly expressed as rising AVO (negative AVO intercept and gradient-referred to as a class one anomaly) in a gas-sand reservoir, where the hydrocarbon reservoir is ‘‘softer’’ (lower acoustic impedance) than the surrounding shale. An AVO anomaly can also include examples where amplitude with offset falls at a lower rate than the surrounding reflective events. There are similarities and differences when the AVO is employed to detect gas-sand reservoirs and coal-bed methane reservoirs. Natural gas is stored in a sandstone medium in a free state; the relationship between CBMC and P-wave velocity of sandstone is described by Gassmann equations. The sandstone reservoir bearing gas is usually softer than the surrounding rock, so the AVO anomaly shows a negative intercept and negative gradient, which is referred to as a first class AVO anomaly. Methane reservoirs are mainly coal seams, and characterized as fractured media. The methane is mainly composed of adsorbed gas, with part of it in a free state stored in the fracture spaces. The relationship between the methane content and the Pwave velocity of coal seams is poorly understood, as most research has focused on the structure of the coal. Studies have shown that the AVO anomaly of a coal bed is characterized by a decreasing AVO (negative AVO intercept and positive gradient, named a class four anomaly) [3,19]. Based on previous studies, the west mining area in Sihe mine of Qinshui Basin is set as the target area. The AVO response from the log characteristics was analyzed and the seismic amplitude, after relative preserved amplitude processing, was corrected to maintain the relative amplitude characteristics. The AVO attributes were calculated based on AVO theory and the statistical relationship between AVO attributes and CBMC was established and used to predict the CBMC.

Erathem

796

9.25

Siltstone, sandstone

9.00

Siltstone, sandstone Mudstone, sandstone

8.75 6.42

3# Coal

4.90

2. Geological background

ui Sh 1500

Oinxian Xianghuan Changzhi

300

Qi

1 n 100 500 0 500

500

Guxian

1000

Huoshan uplift

Zuoquan

Qinshui

2000

Schematic diagram of Qinshui Basin Schematic diagram of research ares Fault boundary Basin boundary Upper Paleozoic boundary Triassic boundary Depth of Carboniferous

Yangcheng

Fig. 1. Structure map in Qinshui Basin.

P1s 4.85

2.00 0.70 13.50 2.73 0.71 5.50 0.57 5.75 0.42 8.50 1.52 0.90 6.75 0.40 2.75 3.61 0.34 4.00 10.29 2.88 C3 C3t 3.71

K7 5# K5 6#

Mudstone, sandstone Fine-grained stone, siltstone Mudstone, sandstone Coal Siltstone, mudstone Limestone Coal Siltstone, mudstone, limestone

7# Coal Siltstone, mudstone, limestone

8# Coal Siltstone, fine-grained stone

9# Coal K4 Limestone Siltstone, mudstone, limestone

13# Coal Siltstone, mudstone K3 Limestone 14# Coal Mudstone, siltstone K2 Limestone 15# Coal Quartz sandstone

9.02 9.02

Aluminum mudstone

C2 C2b

C Ordovician

ong

zh Tian

on

essi

depr

Ta iha ngs han upl ift

ault

f shan

200 0

g

hon

Jin z

Ba sin

350re0ssion p t de 00 faul 25

150 0 100 5 0 30000

Yangquan 0 10 20 30 km

Carboniferous

Shouyang

Taiyuan

Palaeozoic erathem

P

The study area is the west mining area of Sihe Coal Mine in Qinshui Basin, which is located at the southern end of the Qinshui syncline (Fig. 1), with an E–W extension of 6.0 km and an N–S extension of 1.5 km.

N

P1

Rock

O

Limestone O2

O2

Fig. 2. Typical bore histogram of Sihe coal mine.

797

sandstone (K7). The thickness of the C3 seam is 5.66–6.73 m with an average of 6.07 m. The roof is mainly mudstone and sandy mudstone mixed with minor siltstone and finegrained sandstone; the floor is mainly mudstone and sandy mudstone and in some places, fine grain sandstone or siltstone. (2) The Carboniferous Taiyuan formation (C3t). There are 12 coal seams which are, from top to bottom: coal 5 (C5), coal 6 (C6), coal 7 (C7), coal 8-1 (C8-1), coal 8-2 (C8-2), coal 9 (C9), coal 10 (C10), coal 11 (C11), coal 12 (C12), coal 13 (C13), coal 15 (C15), coal 16 (C16). The total average thickness is 6.50 m and the coal-bearing factor is 6.44%. The C15 is so uniform over the whole basin that it is valuable to be mined; however, the remaining coal seams are not valuable.

Normal

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

P-wave incident

S-wave reflection Rps P-wave reflection Rpp

φ1 θ1

θ

1

Media 1 Vp1 Vs1 ρ1

Interface X

θ2

Media 2 Vp2 Vs2 ρ 2

φ2

Y

Transimtted P-wave Tpp Transimtted S-wave Tps

Fig. 4. Plan wave propagation at the interface between two different media.

2.3. Field structural features of the first medium; and Vp2, Vs2 the P-wave velocity and S-wave velocity of the first medium. Snell’s law states that the ratio of the angles of incidence and refraction is equivalent to the ratio of phase velocities in the two layer media. In geophysics, the law is used in ray tracing to calculate the angles of incidence or refraction. Assume the X-axis to the right as positive, and the Y-axis to the bottom as positive, according to the boundary conditions of displacement and stress, Zoeppritz’s equation can be obtained as shown in the following formula. In seismology, it describes how seismic waves are transmitted and reflected at media boundaries, which are boundaries between two different layers of the earth. It relates amplitudes of P-waves and S-waves at each side of an interface to the angle of incidence of the incoming wave [20].

The basic structural features of the west mining area are consistent with those of the whole region. There is a synclinorium with a NNE strike whose wings are gentle with angles generally less than 10°; several faults and collapse columns exist, which results in the uneven distribution of CBMC (Fig. 3). 3. Basic principles, samples and methods 3.1. Basic principles of AVO In the 1980s, an abnormal phenomenon in seismic records was noted where reflection energy was seen to vary with increasing offsets. Through further study, the phenomenon was considered to be closely related to strata containing oil and gas. At present, extensive research is being carried out on seismic amplitude variation where the sandstone contains oil and gas (primarily in a free state) and gradually reliable mathematical models are being formulated. However, coal bed methane occurs in two forms: adsorbed gas and free state gas, which leads to a very complex relationship between CBMC and AVO. In short, it is an undeniable fact that seismic amplitude variation is closely related to the presence of coal bed methane. When plane waves pass through a boundary between two different isotropic media, wave reflection and refraction takes place as shown in Fig. 4. The relationship between angles of incidence and refraction is described by Snell’s law:

where Rpp is the P-wave reflection coefficient; Rps the converted Swave reflection coefficient; Tpp the P-wave transmission coefficient; Tps the converted S-wave transmission coefficient; Vp1 the P-wave velocity of the upper medium; Vp2 the P-wave velocity of the below medium; Vs1 the S-wave velocity of the upper medium; Vs2 the S-wave velocity of the below medium; q1 the density of the upper medium; and q2 the density of the below medium.

5

27

5 27

300 325

300

1005

300

450 425 400 375

475 450 425 400 375 350

115 325

350

47

SHX-171

325

450 425

5

350

400

45

0

5 42 0 45

300

SHX-172 1003

SHX-170

475

450

375

5 40 0 5 42

400

375

45 0 425

450

425

400

35 0 37

350

325

37 5

375

SHX-179

5

1101

YH-005 SHX-187

5

110

27

YH-012 1002

0907

0906

27

SHX-181

0905

5

YH-006

0904

27

YH-010 1001

1

ð2Þ

where h1 is the angle of P-wave incidence; h2 the angle of P-wave transmission; /1 the angle of S-wave reflection; /2 the angle of Swave transmission; Vp1, Vs1 the P-wave velocity and S-wave velocity

126

q2 V 2s2 V p1 sin 2h2 q1 V 2s1 V p2 q2 V p2 q1 V p1 cos 2u2

    Rpp     Rps    q2 V s2 V p1 cos 2u2  T pp  q1 V 2s1     q2 V s2 T ps  q V p1 sin 2u2  cos u2 sin u2

1

ð1Þ

N

 sin h2 cos h2

27 5

sin h1 sin h2 sin /1 sin /2 ¼ ¼ ¼ V p1 V p2 V s1 V s2

  sin h1 cos u1   cos h1 sin u1   V p1  sin 2u1 V s1 cos 2u1   s1  cos 2u1 V sin u1 V p1     sin h1     cos h1   ¼    sin 2h1    cos 2u 

SHX-161

300 0

0.2 0.4 km

1004 Borehole

1102

Fig. 3. Contour map of coal 3 floor in the west area.

1004 Collapse column Goaf Contour Fault

798

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

Table 1 New methane content data. Well name

Depth (m)

Orifice elevation (m)

Elevation of C3 (m)

Coal thickness (m)

Methane content (m3/t)

YH-010 YH-006 YH-012 YH-005 SHX-187 SHX-179 SHX-181 SHX-170 SHX-171 SHX-172 SHX-161

880.75 821.12 862.31 814.53 804.92 754.83 786.15 654.85 716.36 649.41 688.94

525.25 472.62 451.81 387.53 327.55 275.85 332.40 293.80 361.00 290.03 371.70

355.50 348.50 410.50 427.00 477.37 478.98 453.75 361.05 355.36 359.38 317.24

5.69 5.65 6.10 5.86 6.10 6.35 6.55 6.95 6.35 6.32 6.63

21.05 16.13 15.23 7.96 1.98 4.42 8.71 12.87 16.15 16.72 15.14

Methane content

Density

S-wave velocity

P-wave velocity

such as the Bortfeld approximation, Hilterman approximation, Aki and Richards approximation, Shuey approximation.

Pre-stack gathers

Log analysis

Intercept and gradient analysis of well-log

3.2. Data sampling

Super gather

In this area, there are fourteen boreholes for precise survey which were drilled in the 1990s (labeled in red in Fig. 3) and which have well-log records (density and P-wave logs). There are eleven boreholes for gas extraction, which were drilled in 2000s (labeled in pink Fig. 3) and which have methane content data (Table 1). There are no boreholes for gas extraction in the eastern region, which is exclusively used for coal mining. All samples from the new 11 boreholes were directly collected from drilling according to the Chinese Standard Method GB/T 19222–2003, and carefully packed and returned to the laboratory for experiments. By comparing all methane content data, we discovered that the methane contents of old boreholes are significantly lower than those of new boreholes. The difference is mainly due to different eras, construction teams and measurement technologies. In order to maintain consistency, we adopted all the new boreholes and took old boreholes mainly as references.

Offset scaling

Intercept and gradient analysis of seismic Unreasonable Reasonale

Intercept

Gradient

Polarization product

S-wave impedance

Statistical analysis

Methane content predicted by borehole interpolation

Distribution of methane content

Comparative analysis

Fig. 5. Technical process of CBMC prediction.

3.3. Technical processes Zeoppritz’s equations are too complex to be used in full, so they are simplified into useable forms under different preconditions,

The prediction process of CBMC is shown in Fig. 5 and described as follows:

Table 2 Physical parameters of coal-bearing strata. Rock

P-wave velocity

Coal Mudstone Sandstone

2430 3200 4125

vp (m/s)

S-wave velocity

vs (m/s)

920 1585 2380

-0.10

Shuey linear Shuey

-0.15

Hilterman Bortfeld Zoeppritz

-0.20

Aki & Richard

-0.25 -0.30 0

Velocity’s ratio

1.80 2.34 2.70

2.64 2.02 1.73

vp/vs

5

10

15 20 25 30 Incidence angle (°)

(a) Mudstone

35

40

45

-0.05

Shuey linear Zoeppritz

-0.10

Smith & Gidlo

-0.15 -0.20

Shuey Hilterman

-0.25

Bortfeld

-0.30 -0.35 -0.40

Aki & Richard

-0.45 -0.50 0

5

10 15 20 25 30 35 40 Incidence angle (°)

Poisson’s ratio p 0.416 0.337 0.249

0 Smith & Gidlow

Reflection coefficient of various AVO aquations

Reflection coefficient of various AVO aquations

-0.05

Density d (g/cm3)

45

(b) Sandstone

Fig. 6. Amplitude versus angle analysis of mudstone, sandstone and coal in different approximation.

799

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803 Density (g/cm3) 1.6

P-wave (m/s) S-wave (m/s) 3.9 2000 6000 8000 0 1.5

225 1.0 3 Amplitude

Time (ms)

Synthetic AVO curve of the bottom reflector

0.5

250

275 15

0 Synthetic AVO curve of the top reflector

-0.5 -1.0

300

-1.5 50 100 150 200 250 300 350 400 450 500 Offset (m)

325

Fig. 7. Logging curve of well 1002 and AVO curve of C3 in well 1002.

99

88 86 85 83 81 79 77 76 74 72 70 68 67 65 63 61 59 58 56 54 52 50 49 47 45 43 41 40 38 36 34 32 31 29 27 25 23 22 20 18 16 14 13 11 9 7 5 4 2

Time (ms)

200

250

300

350

400

98

Angle (°)

99

50 100

Time (ms)

98 150

150 200

250 300

(b) Logging velocity

(a) Stack velocity

Fig. 8. Angle of incidence from stack and logging velocity overlapped by super gather.

98

99

100

101

150 200 Coal 3 250 Coal 15 300 350 400 450

(1) Analyzing the logging data, including logging normalization to eliminate the non-geological factors. A pseudo-Swave log was created by using the Castagna’s relationship. (2) A super gather is created from pre-stack data so as to increase the seismic signal-to-noise ratio. A wavelet was extracted from the seismic data. The logs were correlated with the stacked seismic data. (3) An AVO model is created from the well-log and used to compute the AVO curve according to Zoeppritz’s function. The AVO curve from the super gather is extracted and compared with that from well-log. If there is no match, offset scaling will be re-analyzed; if the fit is good, this gives us

Fig. 9. Original CMP gather with 10 m  5 m grid.

98

99

101

100

150 98

99

100

200

101

150 200 Coal 3 250 Coal 15 300

Coal 3 Coal 15

250 300 350 400 450

350 400 450 Fig. 10. New CMP gather with 20 m  20 m grids.

C3_top

C3_top (Gradient)

C3_bot

C3_bot (Gradient)

Fig. 11. New CMP gather after amplitude correction.

800

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803 2.0

1.5

1.5 1.0 0.5

0.5

Amplitude

Amplitude

1.0

0 -0.5 -1.0

0 -0.5 -1.0

-1.5 -1.5

-2.0 50 100 150 200 250 300 350 400 450 500 Offset (m)

50 100 150 200 250 300 350 400 450 500 Offset (m)

(a) Well 906

(b) Well 1002 Synthetic AVO curve of the bottom reflector Actual AVO curve of the bottom reflector

Synthetic AVO curve of the top reflector Actual AVO curve of the top reflector

1.5

1.0

1.0

0.5

Amplitude

Amplitude

2.0 1.5

0 -0.5

0.5 0

-1.0

-0.5

-1.5

-1.0

-2.0 50 100 150 200 250 300 350 400 450 500 Offset (m)

50 100 150 200 250 300 350 400 450 500 Offset (m)

(c) Well 1004

(d) Well 1005 Fig. 12. AVO curves of wells.

a fair amount of confidence, and the AVO curve attributes of the super gather is calculated. (4) The correlation between CBMC and AVO attributes was calculated; the best AVO attribute was chosen and used to calculate CBMC by using the Kriging method; the result is contrasted with that from data direct interpolation. The key is to get the AVO phenomenon in line with the actual situation. The intercept (expressed as A) and gradient (expressed as B) are important parameters for this phenomenon, so this goal can be achieved by comparing them with the synthetic AVO data from logging data. Because the latter is relatively reliable, we can take the synthetic AVO data from logging data as standard and adjust the seismic amplitude. 4. Key points and results 4.1. AVO approximation selection According to the approximations mentioned earlier, models are built to analyze their applicability, which respectively represent

the two cases. One is where sandstone is the rock surrounding the coal seams, and the other is where mudstone forms the surrounding rock. The physical parameters are from the known well log in the Sihe mine, and are shown in Table 2. It shows that coal has relatively high Young’s modulus and Poisson’s ratio, representing a soft material which is easily broken. According to the physical properties of the materials, graphs of amplitude versus angle under different approximations are shown in Fig. 6. The Shuey approximation is the best one as it has better adaptability and shows a better approximation in the mudstone model; its assumptions also require smaller physical property differences. 4.2. S-wave velocity approximation According to the principle of seismic wave propagation, the Pwave, S-wave and the density are used to calculate the intercept and gradient. Only the P-wave velocity and density data are available from the Sihe coal mine logs as the S-wave velocity data is missing. Castagna’s formula is a statistical formula, which is suitable for clastic rocks composed of clay or silt particles [21]. Taking

Table 3 Description of the AVO attributes. AVO attribute

Expression

Physical meaning

Intercept Gradient Pseudo-Poisson’s ratio S-wave impedance Fluid factor

A B AB A+B

Amplitude at normal incidence Gradient of the seismic reflection coefficient Poisson’s ratio as vp/vs = 2 S-wave impedance as vp/vs = 2 Characterization of fluid-rich region

AVO anomaly indicating factor Polarization product Polarization angle difference Polarization coefficient squared

Dv

DF ¼ v pp  b vvps Dvvs s AB M  D/ D/ = /  /trend PN 2 Ai Bi ri ¼ PN i¼12 PN 2 i¼1

Polarization magnitude M¼

Ai

Show abnormal It can highlight bright spots which may have hydrocarbons. Trend difference, /trend is a background angle Squared normalized correlation coefficient from the polarization vector

B i¼1 i

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PN 2 1 PN 2ffi 1 i¼1 Ai þ N i¼1 Bi N

RMS length of the data point cloud

801

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

10.50 SHX-181(8.71)

Y coordinate (km)

10.25

YH-101(21.05) YH-012(15.23)

10.00

YH-006(16.13)

9.75 9.50

SHX-172(16.72) YH-005(7.96) SHX-187(1.98) SHX-171(16.15) SHX-179(4.42) SHX-170(12.87) SHX-161(15.14)

9.25 9.00 8.75 5.75

6.25

6.75

7.25

7.75

8.25

8.75 X coordinate (km)

9.25

9.75

10.25

10.75

11.25

(a) Intercept distribution 10.50 SHX-181(8.71)

Y coordinate (km)

10.25

YH-101(21.05) YH-012(15.23)

10.00

YH-006(16.13) 9.75 9.50

SHX-172(16.72) YH-005(7.96) SHX-171(16.15) SHX-187(1.98) SHX-179(4.42) SHX-170(12.87) SHX-161(15.14)

9.25 9.00 8.75 5.75

6.25

6.75

7.25

8.25

7.75

8.75 X coordinate (km)

9.25

9.75

10.25

10.75

11.25

Color key 87.4 85.7 84.0 82.3 80.6 78.9 77.2 75.5 73.8 72.1 70.4 68.8 67.1 65.4 63.7 62.0 60.3 58.6 56.9 55.2 53.5 51.8 50.2 48.5 46.8 Color key 204 198 193 188 182 177 172 166 161 156 150 145 140 134 129 124 118 113 108 102 97 92 86 81 76

(b) Gradient distribution Fig. 13. Gradient distribution of C3 in the west mining area.

4.3. AVO response of the actual well model

Table 4 Correlation coefficients between AVO attributes and CBMC. AVO attributes in the west mining area

Correlation coefficients

Polarization product from A Polarization angle difference A  sinB A A + B (S-wave impedance) A  B (Pseudo-Poisson’s ratio) Polarization coefficient squared B  sinA B AB Polarization magnitude

0.8136 0.7004 0.5773 0.5773 0.5317 0.5128 0.5118 0.4690 0.4690 0.3526 0.3080

into account the main coal-bearing strata, which are mainly composed of clastic rocks, the transverse wave velocity was calculated from Castagna’s formula as follows:

V s ¼ 0:86 V p  1172

Based on the theoretical model, the AVO response of coal 3 from each well was analyzed. According to borehole logging data, the coal and the surrounding rock were described as follows: P-wave velocity of the coal seam ranges from 2055 to 2700 m/s; S-wave velocity ranges from 1766 to 2298 m/s; and the density is 1.2– 1.8 g/cm3. P-wave velocity of the mudstone ranges from 2600 to 3200 m/s; S-wave velocity ranges from 2234 to 2750 m/s; and the density is 2.0–2.36 g/cm3. P-wave velocity of the sandstone ranges from 3000 to 3700 m/s; S-wave velocity ranges from 2578 to 3180 m/s; and the density is 2.2–2.58 g/cm3. Although the velocity for the surrounding rock is faster than that of the coal seam, the Poisson’s ratio is lower. Therefore, the theoretical AVO response of the roof is characterized by a negative intercept and a positive gradient. From Fig. 7, the amplitude variation is basically consistent with AVO features; the absolute reflection amplitudes of the coal seam roof reduce with an increase in the offset (or incident) angle.

where Vp is the P-wave velocity; Vs the S-wave velocity, both unit m/s. Polarization product from A

4.4. Establishment of velocity field and angle gather 7500

5000

2500

Measured points 2.5

5.0

Regression line 7.5 10.0 12.5 15.0 Methane content (m3)

17.5

20.0

Fig. 14. Relationship between CBMC and polarization product attribute.

To extract the intercept and gradient values of the equation, the incidence and transmission angles are required. According to Snell’s theorem, the angle information can be obtained from the interval velocity. There are two main methods to establish the interval velocity: first, by conversion from the RMS velocity and secondly, directly from the logging process. This study shows that the second method is better in accuracy and effectiveness (Fig. 8). It is mainly due to higher resolution of the logging process in the vertical direction, and therefore the velocity field is more consistent with underground conditions.

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G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

Based on the velocity field, angle gather is generated. According to the analysis, the incident angle is mainly 0–40°, and reaches 45° in the place of flexible geometry.

4.7. Relationship between AVO attributes and CBMC Shuey’s approximation (Shuey, 1985) is:

RðhÞ 

4.5. Super gather for SNR improvement The designed fold number is 12 in the 10 m  5 m grid, and is sufficient for post-stack data but insufficient for pre-stack data regarding the SNR. Therefore, a larger grid analysis was created out in order to improve the SNR. This 20 m  20 m grid is considered reasonable, in which the fold number is 96 and the SNR is greatly improved in common middle point (CMP) (Figs. 9 and 10).

  1 DV p Dq þ q 2 Vp þ

! 1 DV p V 2 DV s V 2 Dq 1 DV p 2  4 s2  2 s2 sin h þ 2 Vp 2 Vp Vp Vs Vp q 2

 ðtan2 h  sin hÞ

ð3Þ

In which:

V p ¼ ðV p1 þ V p2 Þ=2 V s ¼ ðV s1 þ V s1 Þ=2

4.6. Comparative analysis of the AVO curve In general, amplitude varies with offset which is mainly caused by geometrical spreading, attenuation and other factors in seismic processing; the relative amplitude characteristics of the seismic data should be preserved (Fig. 11). After processing, the seismic data near the wells were selected in order to analyze the amplitude fidelity. First, the amplitude characteristics from the logging record and seismic data were separately analyzed, then the consistency between them was investigated (Fig. 12). If the AVO curve of the actual seismic data had a large difference from the AVO curve from the well data, further processing was adopted to maintain consistency. Usually, the AVO curve obtained from the logging record is consistent with the result mentioned above, but is not the case for the seismic data. So the former serves as a standard for analyzing AVO phenomenon.

q ¼ ðq1 þ q2 Þ=2 DV p ¼ V p2  V p1 DV s ¼ V s2  V s1 Dq ¼ q2  q1 h ¼ ðhi þ ht Þ=2 ht ¼ arcsin½ðV p2 =V p1 Þ sin hi  It is also expressed as follows: 2

2

RðhÞ  A þ B sin h þ C sin h tan2 h

ð4Þ

  1 DV p Dq A¼ þ q 2 Vp

ð5Þ

Fig. 15. Methane content distribution of the west mining area (m3/t).

Table 5 Comparison of prediction effect. Well name

MC (m3/t)

MC1v (m3/t)

MC1e (%)

MC2 (m3/t)

MC2v (m3/t)

Mc2e (%)

YH-010 YH-006 YH-012 YH-005 SHX-187 SHX-179 SHX-181 SHX-170 SHX-171 SHX-172 SHX-161 Average

21.05 16.13 15.23 7.96 1.98 4.42 8.71 12.87 16.15 16.72 15.14 12.40

18.09 22.65 11.59 9.25 3.23 5.35 10.67 13.8 15.35 16.28 21.23 13.41

14.06 40.42 23.90 16.21 63.13 21.04 22.50 7.23 4.95 2.63 40.22 23.30

20.51 15.75 14.88 7.90 2.51 4.43 8.68 12.81 16.06 17.07 14.66 12.30

19.45 14.58 13.52 7.70 1.88 2.87 4.25 11.22 15.97 14.90 13.45 10.89

7.60 9.61 11.21 3.28 5.16 35.07 51.17 12.82 1.13 10.92 11.19 14.47

MC is the coal bed methane content; MC1v the MC validation prediction by direct interpolation of CBMC; MC1e the validation error by direct interpolation of CBMC; MC2 the MC predicted by AVO attributes; MC2v the MC validation prediction by AVO attributes; and MC2e the validation error by AVO attributes.

G. Zou et al. / International Journal of Mining Science and Technology 23 (2013) 795–803

B¼

C¼

1 DV p V 2 Dq V 2 DV s  2 s2  4 s2 2 Vp Vp q Vp Vs

ð6Þ

1 DV p 2 Vp

ð7Þ

According to the Shuey approximation, many AVO attributes representing physical conditions can be derived [22–24]. By AVO inversion, we obtained the AVO attributes shown in Table 3, such as the intercept and the gradient (Fig. 13). From the correlation coefficients between CBMC and AVO attributes (Table 4), these coefficients are greater than 0.3 according to the correlation analysis; the best one is 0.8136, and its corresponding attribute is polarization angle (Fig. 14).

4.8. Prediction results of CBMC The distribution of CBMC in this whole area was predicted using a dense grid controlled by the Kriging method (Fig. 15). The methane contents are in the range of 2.5–22 m3/t in the west mining area. In order to check the effectiveness of the forecast, we separately took each known borehole as an unknown one and predicted the methane content of each borehole. Then, we compared these results predicted from direct interpolation of CBMC and this new method. From the table below, we can see that the prediction error of CBMC by AVO attributes is within 5%, which is lower than that by direct interpolation (Table 5). 5. Conclusions The distribution of CBMC in the west mining area was obtained with a new method. The key technologies in these processes were analyzed, such as the AVO approximation selection, the S-wave velocity approximation, amplitude correction, velocity field and angle gathers, attributes analysis, and so on. The best AVO attributes were chosen by correlation coefficient analysis. The distribution of CBMC was predicted using the Kriging method, and the result is compared with direct interpolation of CBMC. Finally, we draw the following conclusions. (1) The Shuey approximation has better adaptability in all current approximation methods. (2) Expanded binning improves the signal-to-noise ratio of prestack data. Taking the AVO response from logging as the standard for calibrating the amplitude distribution facilitates a reasonable distribution of AVO attributes. (3) AVO attributes were calculated, such as polarization product from intercept, gradient, polarization angle difference, polarization magnitude, A  sinB, aA + bB, and so on. Some attributes have closely linear relationship with CBMC. The worst attribute is polarization magnitude, of which the correlation coefficient is 0.308, and the best attribute is polarization product from A, for which the correlation coefficient is 0.8136. (4) CBMC predicted by AVO attributes is better than that by direct interpolation of CBMC, and the validation error by AVO attributes, is 14.47%, which is lower than direct interpolation of CBMC, 23.30%. (5) CBMC of this area ranges from 2.5 m3/t to 22 m3/t. Most CBMC in the syncline is above 10 m3/t, but in the anticline is below 10 m3/t; on the whole, the methane content is higher in the syncline than that in the anticline.

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Acknowledgments The research project is supported by the National Basic Research Program of China (Nos. 2009CB219603, 2010CB226800, 2009CB724601 and 2012BAC10B03), the National Natural Science Foundation of China (Major Program) (Nos. 50490271 and 40672104), the National Natural Science Foundation of China (General Program) (No. 40874071), the National Science & Technology Pillar Program in the Eleventh Five-Year Plan Period (Nos. 2012BAB13B01 and 2012BAC10B03), and the Key Grant Project of Chinese Ministry of Education (No. 306002). References [1] Yang ZB, Qin Y, Gao D. Type and geological controls of coalbed methane bearing system under coal seam groups form Bide Santang basin, Western Guizhou. J China Univ Min Technol 2011;40(2):215–9. [2] Li J, Li X, Yang L, Lei C. A method for prediction of methane content in coal seams. Coal Geol Explor 1998;26:31–3. [3] Gamson PD, Beamish B, Johnson DP. Coal microstructure and micropermeability and their effects on natural gas recovery. Fuel 1993;72(6):87–99. [4] Li M. Coal-bed gas exploration in Qinshui basin and its geological analysis. Nat Gas Ind 2000;20:24–6. [5] Lian C, Zhao Y, Li H, Qu F, Ma S, Cai F, Zhang J. Main controlling factors analysis and prediction of coalbed gas content. J China Coal Soc 2005;30:726–9. [6] Zhang X, Mu G, Meng F, Su X. Coal bed gas geological characteristics of No. 2 coal in Rujigou district, Ningxia. Coal Geol Explor 2005;33:33–6. [7] Zhao M, Song Y, Su X, Liu S, Qin S, Hong F. Differences for geochemical controlling factors between coal-bed and conventional natural gases. Pet Explor Dev 2005;32:21–4. [8] Lin X, Su X. Analysis of occurrence characteristics of Coal-bed Gas in Shuangquan mine field of Anyang coal mining area. Min Saf Environ Prot 2007;34:18–21. [9] Yao Y, Liu D, Tang D, Huang W, Tang S. A comprehensive model for evaluating coal bed methane reservoirs in China. Acta Geol Sinica 2008;82:1253–70. [10] Yao Y, Liu D, Tang D, Tang S, Yao H, Huang W. Preliminary evaluation of the coalbed methane production potential and its geological controls in the Weibei coalfield, southeastern Ordos Basin, China. Int J Coal Geol 2009;78:1–15. [11] Meng Z, Tian Y, Lei Y. Prediction models of coal bed methane content based on BP neural networks and its applications. J China Univ Min Technol 2008;37(4):456–61. [12] Liu A, Fu X, Wang K, Peng L, Zhou B. Prediction of coal bed methane content based on support vector machine regression. J Xi’an Univ Sci Technol 2010;30(3):309–13. [13] Wang Y, Lu J, Yin J, Shi Y. What else can seismic prospecting do for methane exploration and production. In: 2009 Asia Pacific Coalbed Methane Symposium and 2009 China Coalbed Methane Symposium. Xuzhou: China University of Mining and Technology Press; 2009. p. 579–83. [14] Xu G, Peng SP, Deng XB. Hydraulic fracturing pressure curve analysis and its application to coalbed methane wells. J China Univ Min Technol 2011;40(2):173–8. [15] Satinder C, Kurt JM. Seismic attributes for prospect identification and reservoir characterization. America: Society of Exploration Geophysicists Publishers; 2010. [16] Peng X, Peng S, Zhan G, Lu Z. P-wave azimuthally AVO analysis of fracture detection in coal bed and its application to engineering. Chin J Rock Mech Eng 2005;24(16):2960–5. [17] Cui R, Qian J, Chen T, Mao X, Li R, Liu W, Gao J, Cui D. Locating the distribution of coalbed methane enriched area using seismic P-wave data. Coal Geol Explor 2007;35(6):54–7. [18] Sun B, Sun F, Yang M. Application of AVO technology in predication of coalbed methane rich area. In: 2009 Asia Pacific Coalbed Methane Symposium and 2009 China Coalbed Methane Symposium. Xuzhou: China University of Mining and Technology Press; 2009. p. 568–73. [19] Peng S, Chen H, Yang R, Gao Y, Chen X. Factors facilitating or limiting the use of AVO for coal-bed methane. Geophysics 2006;71:C49–56. [20] Yang XL, Zhang YL. Numerical simulation on flow rules of coal bed methane by thermal stimulation. J China Univ Min Technol 2011;40(1):89–94. [21] Castagna JP, Batzle ML, EASTWood RL. Relationships between compressional wave shear wave velocityies in clastic silicate rocks. Geophysics 1985;50:571–81. [22] Keho TH, Lemanski S, Ripple R, Raja BT. The AVO Hodogram: using polarization to identify anomalies. Lead Edge 2001;20(11):1214–24. [23] Mahob PN, Castagna JP. AVO hodograms and polarization attribute. Lead Edge 2002;21(1):18–27. [24] Paul V, Marianne R. AVO attributes analysis and seismic reservoir characterization. First Break 2006;24:41–52.