Journal of Petroleum Science and Engineering · Accepted 15 March 2015 Keywords: high production...

High production indexes and the key factors in coalbed methaneproduction: A case in the Hancheng block, southeastern OrdosBasin, China

Junlong Zhao a,b, Dazhen Tang a,b, Hao Xu a,b,n, Yumin Lv c, Shu Tao a,b

a School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, PR Chinab Coal Reservoir Laboratory of National Engineering Research Center of Coalbed Methane Development & Utilization, Beijing 100083, PR Chinac New Energy Research Center of China National Offshore Oil Corporation Research Institute, Beijing 100027, PR China

a r t i c l e i n f o

Article history:Received 3 June 2014Accepted 15 March 2015

Keywords:high production indexesproduction characteristicskey factorsHancheng blockCBM development

a b s t r a c t

An understanding of gas well productivity in coal reservoirs is vital for the recovery of coalbed methane(CBM). The Hancheng Block has become one of the most productive CBM areas in China. Relatively fewstudies have investigated the productivity and key controls of CBM wells in the coal reservoir. In thiswork, with an analysis of the production characteristics of the CBM wells that have been producing formore than 2 yr in the Hancheng Block of the southeastern Ordos Basin, the correlations between variousfactors and numerous gas production data were analyzed by correlation scatter diagrams. The effect ofsix factors (the burial depth, the thickness of the coal seam, the ratio of the critical desorption pressure tothe initial reservoir pressure, the gas content, the permeability and the effect of fracturing) on gasproduction was evaluated quantitatively based on the grey system theory, which is suitable for solvingthe complex interrelationships between multiple factors and variables. The results indicate that theHancheng Block is in the early stage of development. The factors affecting gas production of the CBMwells are, in decreasing order, the ratio of the critical desorption pressure to the initial reservoir pressure,the volume of the fracturing fluid per meter, the gas content, the volume of the fracturing sand permeter, the thickness, the permeability and the depth. Among these factors, the ratio of the criticaldesorption pressure to the initial reservoir pressure determines frequently the degree of difficulty of thewater drainage and decompression; the gas production potential (the thickness� the gas content) andthe gas production deliverability (the gas production potential� the permeability) could characterize thecomprehensive effect of the thickness of the coal seam, the gas content and the permeability on gas wellproductivity. Higher ratio of the critical desorption pressure to the initial reservoir pressure, higher gasproduction potential and deliverability are more favorable for gas productivity. Hydraulic fracturing hasalways been an indispensable part of the development of CBM fields. In combination with gas wellproduction performance, the parameter indexes of high production CBM wells are presented as follows:the buried depth range should be 400–700 m; the thickness of the coal seam should not be lower than5 m; the ratio of the critical desorption pressure to the initial reservoir pressure should be greater than0.3; the gas content should be greater than 6 m3/t; the volume of the fracturing fluid per meter shouldnot be less than 40 m3/m; the volume of fracturing sand per meter should not be less than 3 m3/m; and1% KCl active water fracturing fluid should be used as the fracturing fluid.

& 2015 Elsevier B.V. All rights reserved.

1. Introduction

In recent decades, coalbed methane (CBM) has become animportant source of clean energy and a strategic supplement toconventional natural gas (Al-Jubori et al., 2009). As the largest

consumer and producer of coal in the world (Dai et al., 2012; Songet al., 2012), China has the third largest CBM reserves, behind thoseof Russia and Canada (Yun et al., 2012). With the rapid developmentof CBM industrialization, lower costs and higher gas production perwell in CBM commercial development have gradually becomeimportant topics in CBM research.

Many researchers have used geological analogy methods orstatistical methods to study the production characteristics and factorsthat influence well production in different CBM basins or blocks.Kaiser et al. (1994) conducted a comparison of two CBM basins in the

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n Corresponding author at: School of Energy Resources, China University ofGeosciences (Beijing), Beijing 100083, PR China.

E-mail address: [email protected] (J. Zhao).

Please cite this article as: Zhao, J., et al., High production indexes and the key factors in coalbed methane production: A case in theHancheng block, southeastern Ordos Basin, China. J. Petrol. Sci. Eng. (2015), http://dx.doi.org/10.1016/j.petrol.2015.03.005i

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United States, the prolific San Juan Basin and the marginally devel-oped SandWash Basin, which indicated that the coal distribution andrank, the gas content, the permeability, the ground-water flow, aswell as the depositional and structural setting, were critical factors inCBM producibility. Walter and Ayers (2002) reviewed two comple-tely different CBM systems in the San Juan Basin and PowderRiver Basin and noted that the key parameters to controlling CBMresources and producibility were the thermal maturity, the maceralcomposition, the gas content, the coal thickness, the fracture density,the in-situ stress, the permeability, the burial history and the hydro-logic setting. Scott (2002) proposed that the gas content distributionin coal seams is affected by many hydrogeological factors that couldbe grouped into the following three categories: the gas generation,coal properties and reservoir conditions. On the basis of previousresearch results, Su et al. (2005) dissected the interaction of the sixgeological factors affecting CBM well production in detail as follows:the tectonics and structure, the stratigraphy and sedimentology, thecoal rank, the permeability, the gas content and the hydrodynamics.Gentzis et al. (2008) reported the production characteristics of CBMwells in the Fenn Area and Corbett Creek Area in Canada andproposed that the largest effect on gas production volumes is rootedin the application of horizontal drilling technology, especially multi-stage horizontal drilling technology. Through the comprehensiveanalysis of gas and water variation tendency and the main controllingfactors at different production stages based on data from the PanheBlock in the Qinshui Basin, Yang et al. (2008) considered the gascontent, the tectonics, the burial depth and the reservoir fracturecharacteristics to be the important factors affecting CBM production,and high production wells are usually located in fractured area. Sanget al. (2009) analyzed the geological factors that influence productionand the production mechanism of CBM wells in the Qinshui Basin,revealing that the coal reservoir structure is the basic factor affec-ting the productivity of CBM wells whereas the gas content andthe permeability are direct geological factors controlling CBM well

production. Taking the Fanzhuang Block in the southern part of theQinshui Basin as an example, Chen et al. (2009) showed that a highproduction well has a high ratio of the critical desorption pressure tothe initial reservoir pressure and vast amounts of sand and fluid arerequired in CBM fracturing production which could influence the gasproduction of a single well. In suggesting development proposals, Taoet al. (2012) compared the gas–water production characteristics of 57CBM wells in the southern Qinshui Basin for 1.5 yr and analyzed thegeological and engineering factors that affect the productivity changein the CBM wells in this area. Lv et al. (2012) evaluated the effects ofvarious factors on the temporal and spatial productivity variationsand determined that hydraulic fracturing, gas content and perme-ability, and the structural and hydrogeological conditions have thegreatest effect.

Both the geological and engineering factors could affect the pro-ductivity of CBM wells. The earlier studies predominantly focused onthe basins and areas with a higher level of development, and theproduction characteristics and key factors in different areas varygreatly. In the Hancheng Block, where the development level is lowerand most of the production wells do not reach the desired goal, onlya few scholars have analyzed the main control factors of CBM wellproductivity in recent years. Zeng et al. (2012) found that interlayerinterference, production intensity and braise blocking are the mostimportant factors controlling CBMwell production in the block basedon the pilot production data over 1 yr. Kang et al. (2012) comparedand summarized the production characteristics of CBM wells in theFanzhuang and Hancheng Mining Areas, which are typical high andmiddle rank coal fields, respectively, and analyzed the effects ofperforating thickness, number of perforated layers, unloading tech-nology and stimulation treatment on gas production. Based on theanalysis of CBM wells production performance in the HanchengBlock, Shao et al. (2013) built four productivity modes of CBM wellsand suggested four aspects of an optimization method for a reason-able production system. However, the influencing factors of CBM

Fig. 1. Map of the Ordos Basin and the position of the study area.

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well production in the Hancheng Block are complex, and the keycontrol factors are not clear. The previous research achievementspredominantly focused on the analysis of engineering factors insteadof geological factors.

The goal of this study is to determine and understand the keyfactors affecting CBM well production in the Hancheng Block. Thecorrelations between the geological and engineering factors andnumerous production data were analyzed comprehensively andsystematically using correlation scatter diagrams. The various factorsaffecting the production performance of CBM wells were determinedquantitatively through the grey correlation theory. The results shouldfacilitate establishing a reasonable production system and enhancingCBM recovery.

2. Overview of the Hancheng mining area

The Hancheng Mining Area is in Shanxi province, on thesoutheastern margin of the Ordos Basin in China, with an area of1120 km2 (Xue et al., 2012). As a result of the regional tectonics,inside the west-dipping monocline, there are numerous small-scale high-angle faults, and most of them could cut through thesurface. The Hancheng Mining Area contains an estimated1.7�1012 m3 of total CBM reserves, and more than 88% of thereserves were buried less than 1000 m deep (Ma and Yin, 2002).The Hancheng Block is located in the southern section of thismining area (Fig. 1).

The main coal-bearing sequences in the mining area occur in thePermian Shanxi Formation and Carboniferous Taiyuan Formation

(Fig. 2). The Shanxi Formation is approximately 35–115 m thick andis deposited mainly in a shallow water delta. The main mineable coalseam is the No. 3 seam, which has a general thickness of 1–2 m and aburial depth of 300–1200 m over the entire area. The coal seamstructure is simple. The Taiyuan Formation is approximately 26–87 mthick, predominantly deposited in a coastal plain. The No. 5 andNo. 11 seams are the main mineable coal seams with complexstructures. Among them, the No. 5 coal seam has a general thick-ness of 1–6 m and a common burial depth of 600–1100 m; in mostof the stable distribution area, it parallels the No. 3 coal seam. TheNo. 11 seam has a general thickness of 2–6 m and a burial depth of600–1100 m over the entire area, and it is on one side of a wedge (onthe other side is the No. 3 coal seam) in the spatial stable distributionarea. The injection/falloff well tests demonstrate that most of the coalreservoirs are underpressured and the pressure coefficient is gen-erally 0.6–0.95 (Table 1). According to the tests on the explorationwells, the gas content of these coal seams generally ranges from 6.89to 13.60 m3/t, and 83% of these coal seams are unsaturated. The coalrank in the mining area ranges from low volatile bituminous to semi-anthracite (Yao et al., 2013).

3. Productivity characteristics of the CBM wells in theHancheng block

CBM development typically undergoes the following threestages: water drainage and decompression, desorption and gasproduction and gas production exhaustion (McKee and Bumb,1987; Schraufnagel, 1993; Xu et al., 2013; Zhao et al., 2014). During

Fig. 2. Stratigraphic column of the Permo-Carboniferous coal-bearing strata.

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the CBM production process, three corresponding flow regimestypically occur, as follows: single-phase water flow, two-phase gasand water flow and single-phase gas flow (Ates and Barron, 1998;Colmenares and Zoback, 2007; Xu et al., 2014). In this unsaturatedCBM field, multi-layer drainage is the major production mode, andmost of the production wells in the Hancheng Block must experi-ence a long water drainage stage and unstable gas production stage.Therefore, to analyze the production expediently, the productiontime of the selected wells is over 2 yr (so far, all the selected wellshave produced continuously more than 2 yrs and most of them areconcentrated in about 3 yr as well as the longest production time isclose to 4 yr), which indicates that the CBM wells have ended thewater drainage stage and unstable gas production stage, andentered the continuous and stable gas production stage on thewhole and their positions are relatively concentrated.

The average gas and water production column-curves of 36 CBMwells are plotted in Fig. 3, which shows that the average gas productionof 22 wells is less than 1000m3/d; among the 22 wells, six produce nogas, and the gas production of 13 wells is 500–1000m3/d. The averagegas production of nine wells is between 1000 and 1500m3/d, and fivewells produce gas at more than 1500m3 per day. Thewater productionon the whole from the CBM reservoir in the block ranges from 1.1to 27.1 m3 per day.

4. Effects of the factors on the gas production in theHancheng block

Many factors could affect CBM development, and the averagegas production is the most reliable short-term indicator of thevarious factors (Ellard et al., 1992; Sparks et al., 1993; Pashin,1997). Because the key factors are different in different areas, thegoal of this study is to determine and understand the key factorsaffecting CBM well production in the Hancheng Block. The wholeresearch process could be described as follows: (1) investigatingthe potential influencing factors extensively including the coalrank, the burial depth, the coal thickness, the gas content, the ratioof the critical desorption pressure to the initial reservoir pressure,the permeability/porosity, the ground-water flow, the depositionaland structural setting, the fracturing effect, etc. (2) Excluding theparameters which have little influence on the gas production andoptimizing the parameters which have larger changes and couldbe quantized. Here, the previous researches indicated that thecoal rank in the Hancheng Block is similar (Yao et al., 2013), thedepositional and structural conditions change little (Xue et al.,2012), and the ground-water flow is weak due to the aquicludesbetween the coal seams and aquifers (Yao et al., 2013), whichdemonstrated that these factors have little influence on gas pro-duction. Thus, the effects from the coal rank, the depositional andstructural conditions, and the ground-water flow were excluded.Then, six parameters (the burial depth, the thickness, the gascontent, the ratio of the critical desorption pressure to the initialreservoir pressure, the permeability, the fracturing effect) wereoptimized, which have larger changes and could be quantized.(3) Preliminary analyzing of the correlations between the opti-mized parameters and numerous production data with the corre-lation analysis which is suitable for solving the simple inter-relationship between the single factor and variable. (4) Furtherquantitatively determining the key factors affecting the productionperformance of CBM wells through the grey correlation theorywhich is suitable for solving the complex interrelationshipsbetween multiple factors and variables. The workflow has beenprovided in Fig. 4. And this method for the CBM well productivityanalysis also could also be applied to other blocks or basins.Moreover, high production indexes were also proposed on thebasis of a statistical assessment of the average gas production datafrom 36 vertical wells in the block.

Table 1Results of the injection/falloff well tests in the Hancheng Mining Area.

Wellname

Coalseam

Burialdepth

Reservoirpressure

Pressurecoefficient

Permeability(mD)

(m) (MPa)

Well 1 3# 345.70 2.80 0.81 1.61Well 1 5# 375.55 2.39 0.64 2.19Well 1 11# 418.85 2.65 0.63 0.02Well 2 5# 637.50 5.76 0.90 0.05Well 2 11# 672.40 4.12 0.61 0.001Well 3 11# 1007.00 9.60 0.95 0.02Well 4 5# 529.50 4.63 0.87 0.03Well 5 3# 1255.50 11.87 0.95 0.37Well 5 11# 1323.10 9.22 0.70 0.01Well 6 3# 722.10 6.41 0.88 0.35Well 6 11# 787.85 6.96 0.88 1.19

Fig. 3. The gas and water production characteristics of the CBM wells.






4.1. Burial depth

In the Hancheng Block, the middle depth of the target coalseams was considered the burial depth, which was used to analyzethe correlation of the burial depth and gas production. Fig. 5 showsthat the burial depth of the target coal seam ranges from 400 to770 m based on 36 data points, and the distribution of the high andlow productionwells is relatively scattered, indicating that there is apoor relationship between the burial depth and daily gas produc-tion. Overall, the average gas productivity decreases slightly withthe increase of the buried depth; the CBM wells that were per-forated in the deep coal seam, which has a depth of more than700 m, generally produced gas at less than 1500 m3/d. This resultindicates that the CBM seepage condition of the shallow coal isbetter than that of the deep coal because of the better developmentof the pore–fissure system and the CBM in the shallow coal is easierto be developed because of the easier water fracturing, waterdrainage and reservoir decompression. Thus, a coal seam with adepth of from 400 to 700 m tends to have a much greater potentialfor producing CBM.

4.2. Thickness

The coal seam is the prerequisite for CBM accumulation, and athicker coal seam could result in more gas production because ofthe richer gas source and the stronger ability to supply gas underthe identical conditions in the same area (Pashin, 1991, 1997). Fig. 6indicates that, with an increase in the perforated coal thickness, thegas production of CBM wells tends to decrease, which is contrary to

the above conclusion. This phenomenon contradicts the perceptionthat thick instead of thin coal reservoirs tend to produce very largequantities of CBM. The following reasons could explain this contra-diction: (1) the first is that the reservoir pressure can be depressur-ized in the thin seams during the initial pumping period; (2) theinitial permeability of the CBM reservoir is low and the verticalheterogeneity of permeability in thickness coal seams is remarkablebecause of the partings and large interlayer spacing in the coalseams (Jin et al., 2004); (3) a fracturing operation might open thesandstone seams close to the coal reservoirs, resulting in the coalseams communicating aquifers. These situations inevitably lead to alimited drainage area, resulting in poor productivity of the thickperforation interval during the initial development period. In theHancheng Block, 5–11 m is the advantageous thickness for coalseams because the gas production of most wells is more than1500 m3/d.

4.3. Ratio of the critical desorption pressure to the initial reservoirpressure

In the injection/falloff well test data shown in Table 1, anobvious positive linear relationship between the initial reservoirpressure and the burial depth exists in the Hancheng Mining Area,which could be expressed as

p¼ 0:0088h�0:4581 ðR2 ¼ 0:8953Þ ð1Þ

where h is the burial depth of the coal seam, m; and p is the initialreservoir pressure, MPa.

Fig. 4. Workflow of the key factors analysis on CBM well productivity.






The initial reservoir pressure of the coal seam could be com-puted by Eq. (1), and the result indicates that the initial reservoirpressure in the Hancheng Block varies, with a range of 3–7 MPa.The correlation of the initial reservoir pressure and the gasproduction is not remarkable. As a whole, greater initial reservoirpressure is associated with lower gas production (Fig. 7).

During production, when successive gas appears in the well-head as a sign of the initial CBM desorption, the flowing bottomhole pressure at this moment could be regarded as the approx-imate critical desorption pressure of the CBM reservoir. With theburial depth, the dynamic fluid level and the casing pressure, thecritical desorption pressure could be calculated (Tao et al., 2012).Fig. 8 shows that the change range of the critical desorption pre-ssure is larger and is from 0.7 to 3.5 MPa, which correlates wellwith the gas production. With the increase of the critical deso-rption pressure, the gas production increases obviously, andespecially from 1.6 to 3.5 MPa, all of the wells produce CBM atmore than 1500 m3 per day.

The ratio of the critical desorption pressure to the initialreservoir pressure is more significant to CBM development. If theratio is higher, the wells are more economical (Cui and Bustin,2005; Wang and Ward, 2009). Fig. 9 shows that the ratio lay in therange of 0.1–0.8, on the basis of 36 points, and an obvious positivecorrelation exists between the ratio and the daily gas production.A coal seam with a critical desorption pressure to initial reservoirpressure ratio of less than 0.3 tends to have less potential forproducing CBM (o1000 m3/d). The number of high productionwells increases as the ratio increases (40.3).

4.4. Gas content

In the American evaluation index system for CBM resources andreserves, the gas content represents the CBM productivity (Ross et al.,2009). A low gas content is equivalent to a low gas saturation in theidentical structural setting. A small degree of unsaturation couldnecessitate prolonged dewatering before a large reservoir volumecould reach the critical desorption pressure (Price and Ancell, 1993;Pashin, 2010). With the data on the petrophysical properties of thereservoir and the isothermal adsorption experiments, the gas contentof the corresponding development wells could be calculated. The gascontent of the coal seams in the Hancheng Block is generally lowerthan that (22–25 m3/t) in the Qinshui Basin coal seams (Lv et al.,2012). The maximum gas content of this block could only reach12 m3/t. Fig. 10 shows an obvious positive correlation of the gascontent and gas production. The gas content of the high productivitycoal seam ranges from 6 to 12 m3/t. All the coal seams with a gascontent of less than 6 m3/t have low productivity (o1000 m3/t). Thereason for this phenomenon is that greater gas content contributes tolarger CBM resources in a certain productive section, which ensuresgood performance in the wells.

Fig. 5. Scatter map of the average gas productivity and the burial depth (the areawith the average gas rate of more than 1500 m3/d is marked by the star).

Fig. 6. Scatter map of the average gas productivity and the thickness (the area withthe average gas rate of more than 1500 m3/d is marked by the star).

Fig. 7. Scatter map of the average gas productivity and the initial reservoir pressure(the area with the average gas rate of more than 1500 m3/d is marked by the star).

Fig. 8. Scatter map of the average gas productivity and the critical desorptionpressure (the area with the average gas rate of more than 1500 m3/d is marked bythe star).

Fig. 9. Scatter map of the average gas productivity and the ratio of the criticaldesorption pressure to the initial reservoir pressure (the area with the average gasrate of more than 1500 m3/d is marked by the star).






4.5. Permeability

Fractures are the main channels for CBM production, and CBMreservoir permeability could reflect the degree of smoothness of thechannels (Moore, 2012). The permeability could represent the gasproductivity of CBM wells to some degree. Typically, higher perme-ability is associated with a larger pressure drop funnel extension,which results in larger effective drainage radius and higher gasproductivity. According to the injection/falloff well tests, the perme-ability of the CBM reservoir is generally less than 1 mD (Table 1),which indicates that the coal seam in the Hancheng Block is atypical low-permeability CBM reservoir. The permeability of theproducing wells could be predicted by the relationship of the welltest permeability and the burial depth. Fig. 11 shows that therelationship between permeability and gas production is a positivecorrelation. The permeability of the wells, which produce gas at1500 m3 per day, is greater than 0.4 mD.

4.6. Hydraulic fracturing

Because of the low permeability of the coal reservoir, a CBM wellcould not achieve an adequate production rate unless reservoirstimulation techniques are performed to increase the permeabilityof the target seam (Wang et al., 2009). Most CBM wells are sti-mulated by hydraulic fracturing, which is the most widely appliedmeasure to enhance CBM recovery worldwide. Hydraulic fracturinghas always been an integral part of the development of CBM fields(Rahman et al., 2007). The total volumes of fracturing fluid andfracturing sand are two important parameters characterizing thefracture scale in the fracturing process. The larger the scale of thehydraulic fracture is, the more fracturing fluid and sand are needed.

During the fracturing process, whether the fracturing sandvolume is appropriate directly determines whether the fracturingoperation is successful. Generally, a larger fracturing sand volume

is more beneficial to increasing the fracture length, width and flowconductivity, which contributes to high gas production. Fig. 12 isthe scatter map of the average gas productivity and the fracturingsand volume. It indicates that the fracturing sand volume ofthe high gas productivity wells (41500 m3/d) is more than28 m3. Seven wells with less than 28 m3 have low productivity(o1000 m3/d), and one well with more than 37 m3 produces nogas. One immediate explanation of this finding could be that thesupport fracture conductivity is too low if the fracturing sandvolume is not high enough, resulting in an inferior fracturingeffect. On the one hand, the proppant particles are crushed underthe long-term effects of the in-situ stress, and along with themovement of the fluid, some effective seepage channels arejammed. On the other hand, the non-Darcy flow from the fractur-ing also could reduce fracture conductivity to a certain extent. Ifthe fracturing sand volume is excessive, the fracturing sand couldnot enter deep into the ground effectively, revealing that a sandplug always occurs during fracturing, which might stop the pumpsbecause of the excessive pressure.

Enhancing the injection rate of the fracturing fluid by improvingthe displacement, which increases the inner pressure of a hydraulicfracture, is an important measure to enlarge the width of thefractures and offset the fluid loss in the coal seams. Fig. 13 showsthat gas production increases as the cumulative injection increases,revealing that the increase in the cumulative injection broadens thefracture scale, which results in higher productivity of the CBM wells.All of the wells with a gas production of more than 1500 m3/d weretreated by injecting more than 350 m3 of fluid during fracturing. Thegas production was no more than 1000 m3/day for most of the wellsinjected with less than 350 m3 of fluid.

However, the volume of fracturing sand and fracturing fluid wouldbe decided by the coal thickness. In order to avoid the effect from coalthickness, the fluid volume and the sand volume per unit thickness areconsidered some of the better parameters to characterize the fracture

Fig. 10. Scatter map of the average gas productivity and the gas content (the areawith the average gas rate of more than 1500 m3/d is marked by the star).

Fig. 11. Scatter map of the average gas productivity and the permeability (the areawith the average gas rate of more than 1500 m3/d is marked by the star).

Fig. 12. Scatter map of the average gas productivity and the fracturing sand volume(the area with the average gas rate of more than 1500 m3/d is marked by the star).

Fig. 13. Scatter map of the average gas productivity and the fracturing fluid volume(the area with the average gas rate of more than 1500 m3/d is marked by the star).






scale. Figs. 14 and 15 show that both the fracturing sand per meter andthe fracturing fluid per meter have an approximate positive correlationwith the average gas rate, which is consistent with the fracturing fluidand the fracturing sand (Figs. 12 and 13). However, the correlationcoefficients are higher than using fracturing sand and fluid. Thevolume of fracturing sand and fracturing fluid per meter for the wells,which produce gas at 1500 m3 per day, is greater than 3m3/m and40m3/m, respectively.

Optimization of the fracturing fluid is another important aspectfor improving the fracturing effect. In the Hancheng Block, thefracturing fluids mainly can be divided into two types. One is clean

fracturing fluid (conventional clean fracturing fluid and dt-1) andthe other is active water fracturing fluid (conventional active waterfracturing fluid and 1% KCl). The characteristic comparisons of cleanfracturing fluid and active water fracturing fluid are described inTable 2 and the laboratory test results of 1% KCl active waterfracturing fluid and dt-1 clean fracturing fluid are shown in Table 3.

Although the clean fracturing fluid has stronger sand-carryingability and fracture-making ability than active water fracturing fluid(Tables 2 and 3), choosing the active water fracturing fluid as themain fracturing fluid is more reasonable from the perspective ofcost and reservoir protection, especially 1% KCl which costs only 1/3of dt-1 clean fracturing fluid and 1/8 of conventional clean fractur-ing fluid. But even more important, the field application effectshows the enormous difference between them. The gas break-through proportion of the 25 wells using 1% KCl as the fracturingfluid is 100%, and 13 wells produce gas at more than 1000 m3 perday. Of five wells with clean fracturing fluid and active water usedas the fracturing fluid, three wells produce no gas and two wellsproduce gas at less than 1000 m3/d. Thus, in the Hancheng Blok,using 1% KCl as the fracturing fluid contributes to high productionduring fracturing.

5. Grey correlation analysis and the effect of key factorson CBM well productivity

The relationships between CBM well productivity and depth,thickness, ratio of the critical desorption pressure to the initialreservoir pressure, gas content, permeability and effect of hydrau-lic fracturing have been analyzed. The key factors affecting CBMwell productivity should be determined. To determine the pre-dominant factors, grey correlation analysis, which needs to obtainthe correlation degrees of all the factors, was introduced toquantify and evaluate the influence of the magnitude of thevarious factors on CBM well productivity.

5.1. Grey correlation analysis

As an important part of the grey system theory, grey correla-tion analysis is frequently used to determine whether a close rela-tionship exists between array curves, based on their similarity(Deng, 1989; Kayacan et al., 2010). The purpose of grey correlationanalysis is to quantify the correlation degrees of various factorsand present the key factors that affect the system develop-ment by computing the correlation coefficient and the correlation

Fig. 14. Scatter map of the average gas productivity and the volume of fracturingsand per meter (the area with the average gas rate of more than 1500 m3/d ismarked by the star).

Fig. 15. Scatter map of the average gas productivity and the volume of fracturingfluid per meter (the area with the average gas rate of more than 1500 m3/d ismarked by the star).

Table 2Characteristic comparisons of clean fracturing fluid and active water fracturing fluid.

Type Characteristic

Molecular weight Filter cake and tailings Friction Sand-carrying ability Efficiency of fracture making Cost Permeability damage rate

Clean fracturing fluid Low No Low High High High HighActive water fracturing fluid Low No High Low Low Low Low

Table 3Laboratory test results of 1% KCl active water fracturing fluid and dt-1 clean fracturing fluid.a

Performance index 1% KCl active water fracturing fluid dt-1 Clean fracturing fluid

Molecular weight 300–400 300–400Concentration (%) 0.2 0.2Viscosity (CP) 1.0 15.0Sand-carrying ability (s) 2.8 28–35Friction (%) 100 30–40Permeability damage rate basing on five manufactured coal cores (%) 12.53–13.29 15.82–25.01

a The test temperature is 18 1C.






degree. The more similar the curves are, the larger the value of thecorrelation degree is between the curves, and vice versa. Thedetailed introduction of grey correlation analysis is stated bysection Appendix A.

For the Hancheng Block, the reference array {x0(n)} and compara-tive arrays {xi(n)} should be established firstly. The reference arrayconsists of the gas production data of all the CBM wells while thecomparative arrays are grouped from the various influencing factorsderived from the corresponding well sites, including the depth,thickness and other factors. After nondimensionalizing the referencearray {x0(n)} and comparative arrays {xi(n)}, the correlation coeffi-cient ε0i(k) between the reference array and the comparative arraysin the time of n¼k (point k on the curves) could be computed byEq. (A1). Then, the correlation degree r0i of the comparative arrays tothe reference array could be obtained by Eq. (A4) at any time (anypoint on the curves) at the length of array N (N¼36). Finally, thecorrelation degrees should be ranked. In this case, the values andphysical significances of these parameters are presented in Table 4.Here, in order to further illustrate the calculating processes, theburial depth and gas production rate were chosen as an example:(1) the reference array {x0(n)} is the gas production rate and thecomparative array {x1(n)} is the burial depth; (2) the reference array{x0(n)} and the comparative array {x1(n)} are nondimensionalizedthrough dividing by the maximum values in the correspondingarrays, respectively; (3) the absolute differences Δ0i(k) between thereference array {x0(n)} and the comparative arrays {x1(n)} and thecorrelation coefficient ε0i(k) are computed with Eqs. (A2) and (A3)where Δmax is 0.8515; (4) the correlation degree value r0i (0.5166) iscomputed by obtaining the average value of all of the correlationcoefficients (Table 5).

The result of the above calculations (Table 6) shows that thefactors controlling the gas production of the CBM wells are, indecreasing order, the ratio of the critical desorption pressure to theinitial reservoir pressure, the volume of fracturing fluid per meter,the gas content, the volume of fracturing sand per meter, thethickness, the permeability and the depth, which suggests that theratio of the critical desorption pressure to the initial reservoirpressure is the most important factor in determining well pro-ductivity, whereas depth is the least important factor.

Compared with the regression analysis, the decreasing order ofthese correlation degrees from the grey correlation analysis issomewhat different (Table 6), but these results are not contra-dictory. In the regression analysis, the gas content is on the firstplace. However, its value in grey correlation analysis is very close tothe first factor (ratio of critical desorption pressure to initialreservoir pressure). This fact is in compliance with the physics ofphenomena. It should be noticed the very large point's dispersion

(cloud) in all the scatter maps. The regression analysis couldpreliminarily analyze the factors of CBM well productivity whilethe grey correlation analysis could further quantitatively determinethe key factors on the basis of the internal relations of variousfactors. The reason for this difference is that the grey correlationanalysis is more reliable than the unary linear regression analysisconsidering the characteristics of these two methods. The unarylinear regression analysis only considers whether the relationshipbetween the independent variable (influencing factors) and thedependent variable (gas production rate) could be approximatelyrepresented by a straight line, which is easily affected by samplessize and their typical distribution (cloud). For example, if a parabolicrelation exists between the independent variable and the depen-dent variable, the unary linear regression is not appropriate and thecorrelation coefficient is very low. Thus, the actual change law ishard to be found. Moreover, the sample number also has a largeeffect on the regression analysis. On the contrary, the grey correla-tion analysis provides a quantitative measure for the changes ofsystem development and is very suitable for dynamic processes andmultifactorial analyses. According to the geometry similarity ofarray curves, whether the various factors are strongly linked couldbe judged without considering the amount and the distribution ofsamples. The more similar the curves are, the larger the value of thecorrelation degree is, and vice versa. The grey correlation analysisnot only is particularly applicable to the situation where thestatistical information is lacking and the statistical data grey scaleis large, but also could reduce the calculation time effectively for theanalysis of selected variables.

5.2. Comprehensive analysis of the key factors affecting CBM wellproductivity

The result of the grey correlation analysis above indicates thatboth the geological factors and engineering factors are the keyfactors affecting CBM wells productivity in the Hancheng Block.Among the factors, the three most critical factors are, in decreasingorder, the ratio of the critical desorption pressure to the initialreservoir pressure, the volume of fracturing fluid per meter andthe gas content. Hydraulic fracturing has always been an integralpart of CBM development in the Hancheng Block.

In the process of CBM development, the ratio of the criticaldesorption pressure to the initial reservoir pressure frequentlydetermines the degree of difficulty of the water drainage anddecompression in the CBM reservoir. For the Hancheng Block, theratio with the highest correlation degree (0.7379) and higherfitting degree (0.5411), has the most important effect on gas wellproductivity, revealing that this block is in the early stage of

Table 4Values and physical significances of parameters used in the grey correction analysis.

Parameter Physical significance Value

n Samples number Well number{x0(n)} Reference array, the average gas rate array ranking as the well names H1�H36 Average gas rate of the wells H1�H36{xi(n)} Comparative arrays, the influencing factors arrays ranking as the well names H1�H36 Depth, thickness and other factorsρ Resolution coefficient 0.5Δmax Maximum value of the absolute differences in the arrays Needing to be calculatedΔmin Minimum of the absolute differences in the arrays 0xi(k) Parameter value on the curves of comparative arrays at the time of k (n¼k, point k on

the curves)Values of depth, thickness and other factors of the wells at the timeof n¼k

x0(k) Parameter value on the curves of reference array at the time of n¼k Values of average gas rate of the wells at the time of n¼kΔ0i(k) Absolute differences between the reference array and the comparative arrays at the

time of n¼kNeeding to be calculated

ε0i(k) Correlation coefficient of the comparative arrays to the reference array at the time ofn¼k

Needing to be calculated

r0i Correlation degree of the comparative arrays to the reference array Needing to be calculatedN Length of array, the total number of wells 36s Number of the correlation coefficients 1–36






development, compared with the development time (2–4 yr).Therefore, the reservoir pressure goes against gas desorption duringthe water drainage and decompression stage, which is furtherproved by the relationship between initial reservoir pressure andaverage gas rate (Fig. 7). If the ratio of the critical desorptionpressure to the initial reservoir pressure is higher, which demon-strates that the critical desorption pressure is closer to the initialreservoir pressure and the pressure difference is smaller, CBM ismore easily produced.

Generally, gas content is the material basis and permeability isthe prerequisite for CBM seepage and production in a reservoir. Gasproductivity increases with the increase of permeability and gascontent. In addition, a coal seamwith a larger thickness contributesto more controlled reserve of CBM wells, which results in strongergas supply capability and higher gas production. However, in theHancheng Block, less thickness correlates with greater gas produc-tion (Fig. 6), which is contrary to the common sense. Therefore, twoparameters, the gas production potential (the thickness� the gas

Table 5An example of the grey correlation analysis between the burial depth and gas production rate.

Well name k Actual data Nondimensional arrays Δ0i(k) ε0i(k) r0i

Reference array {x0(n)} Comparative array {x1(n)} Reference array {x0(n)} Comparative array {x1(n)}Gas production rate/ Burial depth / Gas production rate Burial depth(m³ d�1) (m)

H1 1 558.40 488.50 0.2450 0.6505 0.4055 0.5122 0.5166H2 2 215.90 456.75 0.0947 0.6082 0.5135 0.4533H3 3 254.80 484.00 0.1118 0.6445 0.5327 0.4442H4 4 1654.40 439.50 0.7258 0.5852 0.1406 0.7517H5 5 1094.90 518.25 0.4804 0.6901 0.2097 0.6700H6 6 510.20 637.75 0.2238 0.8492 0.6254 0.4050H7 7 843.50 666.00 0.3701 0.8868 0.5167 0.4517H8 8 801.20 634.25 0.3515 0.8445 0.4930 0.4634H9 9 1492.50 630.00 0.6548 0.8389 0.1841 0.6982H10 10 725.98 714.50 0.3185 0.9514 0.6329 0.4022H11 11 1130.90 422.50 0.4962 0.5626 0.0664 0.8650H12 12 1155.50 478.00 0.5070 0.6365 0.1295 0.7667H13 13 1528.00 668.50 0.6704 0.8901 0.2198 0.6596H14 14 2008.00 400.50 0.8810 0.5333 0.3477 0.5505H15 15 1193.50 523.00 0.5236 0.6964 0.1728 0.7113H16 16 2279.30 409.25 1.0000 0.5449 0.4551 0.4834H17 17 0.00 499.00 0.0000 0.6644 0.6644 0.3905H18 18 0.00 504.25 0.0000 0.6714 0.6714 0.3880H19 19 502.30 478.00 0.2204 0.6365 0.4161 0.5057H20 20 893.80 524.50 0.3921 0.6984 0.3063 0.5816H21 21 620.10 533.50 0.2721 0.7104 0.4383 0.4927H22 22 1461.80 674.25 0.6413 0.8978 0.2565 0.6241H23 23 585.40 683.50 0.2568 0.9101 0.6533 0.3946H24 24 1318.70 751.00 0.5786 1.0000 0.4214 0.5025H25 25 2018.30 533.50 0.8855 0.7104 0.1751 0.7086H26 26 1037.70 656.50 0.4553 0.8742 0.4189 0.5041H27 27 248.98 645.50 0.1092 0.8595 0.7503 0.3620H28 28 741.10 609.25 0.3251 0.8113 0.4861 0.4669H29 29 1412.80 659.00 0.6198 0.8775 0.2577 0.6230H30 30 937.40 619.00 0.4113 0.8242 0.4130 0.5076H31 31 809.70 666.00 0.3552 0.8868 0.5316 0.4447H32 32 0.00 625.25 0.0000 0.8326 0.8326 0.3384H33 33 707.10 632.25 0.3102 0.8419 0.5317 0.4447H34 34 0.00 583.50 0.0000 0.7770 0.7770 0.3540H35 35 0.00 613.50 0.0000 0.8169 0.8169 0.3426H36 36 0.00 639.50 0.0000 0.8515 0.8515 0.3333Maximum 2279.30 751.00 — — 0.8515 — —

Table 6Results and comparison from the grey correction analysis and unary linear regression analysis.

Rank Grey correction analysis Unary linear regression analysis

Factors Correlationdegree

Factors Fitting relationship Goodness offit

1 Ratio of critical desorption pressure to initial reservoirpressure

0.7379 Gas content y¼199.45x�448.15 0.5605

2 Volume of fracturing fluid per meter 0.7231 Ratio of critical desorption pressure to initialreservoir pressure

y¼2785x�319.76 0.5411

3 Gas content 0.7023 Volume of fracturing fluid per meter y¼15.108xþ101.98 0.20394 Volume of fracturing sand per meter 0.6950 Volume of fracturing sand per meter y¼196.97xþ61.434 0.15905 Thickness 0.6502 Thickness y¼�96.19xþ1651.8 0.15136 Permeability 0.6318 Permeability y¼577.09xþ482.17 0.04527 Burial depth 0.5166 Burial depth y¼�1.0849xþ1477.8 0.0272






content) and the gas production deliverability (the gas productionpotential� the permeability), are introduced to characterize thecomprehensive effect of thickness, gas content and permeability.Figs. 16 and 17 indicate that the gas production potential anddeliverability have good positive correlations with the gas produc-tion of the wells. High gas production potential and deliverabilityare favorable for gas productivity. But, with the continuous devel-opment of CBM wells, the gas production rate would be controlledgradually by the whole coal seams, which indicates that the gasproduction deliverability with high permeability and high gassupply capacity would be more important than the gas productionpotential in the future.

In short, to achieve efficient development of CBM, a compre-hensive consideration of coal reservoir geological effects, includingthe ratio of the critical desorption pressure to the initial reservoirpressure, the gas production potential and the gas productiondeliverability, is necessary for optimizing the reservoir. And then,matching the reservoir with perfect hydraulic fracturing construc-tion could further increase the gas well productivity.

6. Conclusions

1) According to the result of the grey correlation analysis, thefactors controlling the gas production of the CBM wells in theHancheng Block are, in decreasing order, the ratio of the criticaldesorption pressure to the initial reservoir pressure, the volumeof fracturing fluid per meter, the gas content, the volume offracturing sand per meter, the thickness, the permeability, andthe depth.

2) The ratio of the critical desorption pressure to the initial reservoirpressure frequently determines the difficulty level of the water

drainage and decompression of the CBM reservoir. If the ratio ishigher, which indicates that the critical desorption pressure iscloser to the initial reservoir pressure, the CBM is more easilyproduced. The gas production potential and deliverability char-acterize the comprehensive effect of thickness, gas content andpermeability on gas productivity. High gas production potentialand deliverability are favorable for gas productivity. Hydraulicfracturing has always been an indispensable part in the develop-ment of CBM fields.

3) Overall, the Hancheng Block is in the early stage of development.In combination with the gas well production performance, thecorresponding parameter indexes of reasonably high productionCBM wells were suggested, as follows: the buried depth rangeshould be 400–700 m; the thickness of the coal seam should notbe lower than 5 m; the ratio of the critical desorption pressure tothe initial reservoir pressure should be greater than 0.3; the gascontent should be greater than 6 m3/t; the volume of thefracturing fluid per meter should not be lower than 40 m3/m;the volume of the fracturing sand per meter should not be lowerthan 3 m3/m; and 1% KCl should be used as the fracturing fluid.

Acknowledgments

This work was financially supported by the Key Project of theNational Science & Technology (Grant no. 2011ZX05038-001), theNational Natural Science Foundation Project (Grant no. 41272175), theBeijing Higher Education Young Elite Teacher Project and ChinaPostdoctoral Science Foundation funded project (Grant no. 2014M561020). The authors are grateful to anonymous reviewers and theeditor Dr. Vural Sander Suicmez for their careful reviews and detailedcomments that helped improve the manuscript substantially.

Appendix A. Grey system theory and its application

A.1 Grey system theory and its calculation steps

As an important part of the grey system theory, grey correlationanalysis is frequently used to determine whether a close relation-ship exists between array curves, based on their similarity (Deng,1982, 1989; Kayacan et al., 2010). The purpose of grey correlationanalysis is to understand the things' feature by quantifying thecorrelation degrees of various factors and presenting the key factorsthat affect the system development. According to the geometrysimilarity of array curves, whether the various factors are stronglylinked could be judged without considering the amount anddistribution of samples. The grey correlation analysis is not onlyparticularly applicable to the situation where the statistical infor-mation is lacking and the statistical data grey scale is large, but alsocould reduce the calculation time for the analysis of selectedvariables effectively.

In the process of system development, if the change trends oftwo factors are almost the same or similar, namely synchronouschange degree is high, it could be considered that a large correla-tion degree exists between the two curves. The more similar thecurves are, the larger the value of the correlation degree is, andvice versa. Thus, the grey correlation analysis provides a quanti-tative measure for the changes of system development and is verysuitable for dynamic process analysis.

The application of grey correlation analysis predominantlyincludes five steps and two computations, which are introducedas follows (Fig. A1):

(1) Determining the reference array {x0(n)} and comparativearrays {xi(n)}

Fig. 16. Scatter map of the average gas productivity and the gas productionpotential (the area with the average gas rate of more than 1500 m3/d is markedby the star).

Fig. 17. Scatter map of the average gas productivity and the gas productiondeliverability (the area with the average gas rate of more than 1500 m3/d ismarked by the star).






The reference array could reflect the behavior characteristics ofthe system while the comparative arrays could affect thebehavior characteristics of the system.

(2) Nondimensionalizing the reference array {x0(n)} and compara-tive arrays {xi(n)}Due to the different physical significances of various factors inthe system, the array dimensions are often different, whichmakes it hard to compare the arrays and obtain the correctconclusion. Therefore, it is necessary to nondimensionalize thereference array and comparative arrays before the grey corre-lation analysis is implemented.

(3) Computing the correlation coefficient ε0i(k)In the grey system theory, the essence of the correlationdegree is the geometry difference degree of the array curves.If the reference array {x0(n)} has a number of comparativearrays {xi(n)}, the correlation coefficient ε0i(k) between thereference array and the comparative arrays at time n¼k (pointk of the curves) could be computed by Eq. (A1):

ε0iðkÞ ¼ΔminþρΔmax

Δ0iðkÞþρΔmaxðA1Þ

where ρ is the resolution coefficient, ρ40, and the value isusually 0.5; Δmax and Δmin are the maximum and the minimumvalue, respectively, of the absolute differences in the arrays;because the comparative arrays are orthogonal after conver-sion, generally, Δmin is equal to 0. Δ0i(k) is the absolutedifference between the reference array {x0(n)} and the com-parative arrays {xi(n)} at time k:

Δ0iðkÞ ¼ x0ðkÞ�xiðkÞ�� ðA2Þ

Thus, the correlation coefficient ε0i(k) could be expressedsimply by

ε0iðkÞ ¼0:5Δmax

Δ0iðkÞþ0:5ΔmaxðA3Þ

(4) Computing the correlation degree r0iSince the correlation coefficient is the absolute differencebetween the reference array {x0(n)} and the comparative arrays{xi(n)} at any time (any point on the curves), the value is non-unique and the information is too dispersive to compareintegrally. It is necessary to calculate the average value of allof the correlation coefficients as the expression of the correla-tion degree of two arrays at each time, and then the correlation

degree r0i is expressed as follows:

r0i ¼1N

XN

s ¼ 1

ε0iðsÞ ðA4Þ

where r0i is the correlation degree between the reference array{x0(n)} and the comparative arrays {xi(n)}; N is the array length;and s is the number of correlation coefficients.

(5) Ranking the correlation degree r0iThe close degree of various factors is mainly described by thesize order of the correlation degree, except for the size itself.The correlation order {x} is the permutation of comparativearrays {xi(n)} to the same reference array {x0(n)}, whichreflects the advantages and disadvantages of the comparativearrays to the reference array. If r0i4r0j, it could be regarded asarray {xi(n)} is better than array {xj(n)} to the same referencearray {x0(n)} and denoted by {xi(n)}4{xj(n)}.

A.2 Comparison with the traditional regression analysis

The general abstract system often contains many factors thatdetermine the system's development trend, such as the key factorsanalysis of the CBM well productivity. Here, we need to determinewhich factor is the most important and which one comes second.Although the regression analysis in mathematical statistics can beused as a method of abstract system analysis, it has the followingdrawbacks: (1) the regression analysis requires a large amount ofdata and the shape of fitting curve is easy to be affected by thenumber of samples and their distribution. If the data quantity islittle, it will be difficult to find out the statistical regularity; (2) theregression analysis requires the samples to obey a typical prob-ability distribution and the relationship is linear between factorsdata and system characteristics data as well as it has nothing to dowith each other between various factors. Generally speaking, thisrequirement can't be achieved; (3) the calculation quantity of theregression analysis is usually large resulting in that the calculationrequires a computer; (4) the quantitative results may not matchwith the qualitative analysis results which results in the relation-ship between the system and the regularity to be misrepresentedand become upside down; (5) the regression analysis is usuallyapplicable to the simple analysis of a single factor which cannotreflect the intrinsic relationships between independent variablesand the dependent variable. When the regression analysis is usedto analyze the multifactorial and non-linear case, the error is large;(6) the ultimate goal of the regression analysis is to establish anempirical regression equation and realize the prediction of thedependent variable. But the relationship is diverse and unpredict-able and different methods make a great difference in results.Thus, ranking the factors with the goodness of fit from theregression analysis to determine the key factor is unscientificand not accurate. On the contrary, the grey correlation analysisovercomes the shortcomings and the insufficiency of the regres-sion analysis above through analyzing the dynamic process of thesystem. The method has a small calculation quantity, which isespecially suitable for multifactorial and non-linear analysis.Therefore, the result from the grey correlation analysis is morereasonable and accurate.

References

Al-Jubori, A., Johnson, S., Boyer, C., Lambert, S.W., Bustos, O.A., Pashin, J.C., Wray, A.,2009. Coalbed methane: clean energy for the world. Oilfield Rev. 21, 4–13.

Ates, Y., Barron, Y., 1998. Effect of gas absorption on the strength of coal. Min. Sci.Technol. 6, 11–12.

Chen, Z.H., Wang, Y.B., Yang, J.S., Wang, X.H., Chen, Y.P., Zhao, Q.B., 2009. Influencingfactors on coal bed methane production of single well: a case of Fanzhuang

Fig. A1. Workflow of the grey correction analysis.



http://refhub.elsevier.com/S0920-4105(15)00112-6/sbref1









block in the south part of Qinshui basin. Acta Pet. Sin. 30, 409–416, in Chinesewith English abstract.

Colmenares, L.B., Zoback, M.D., 2007. Hydraulic fracturing and wellbore completionof coalbed methane wells in the Powder River Basin, Wyoming: implicationsfor water and gas production. AAPG Bull. 91, 51–67.

Cui, X., Bustin, R.M., 2005. Volumetric strain associated with methane desorptionand its impact on coalbed gas production from deep coal seams. AAPG Bull. 89,1181–1202.

Dai, S., Ren, D., Chou, C.L., Finkelman, R.B., Seredin, V.V., Zhou, Y., 2012. Geochem-istry of trace elements in Chinese coals: a review of abundances, genetic types,impacts on human health, and industrial utilization. Int. J. Coal Geol. 94, 3–21.

Deng, J.L., 1989. Introduction to grey system theory. J. Grey Syst. 1, 1–24.Ellard, J.S., Roark, R.P., Ayers, W.B., 1992. Geologic controls on coalbed methane

production: an example from the Pottsville Formation (Pennsylvanian Age),Black Warrior Basin, Alabama, U.S.A. In: Proceedings of the 1992 Symposium onCoalbed Methane Research and Development in Australia, Townsville, Australia,pp. 45–61.

Gentzis, T., Goodarzi, F., Cheung, F.K., Laggoun-Défarge, F., 2008. Coalbed methaneproducibility from the Mannville coals in Alberta, Canada: a comparison of twoareas. Int. J. Coal Geol. 74, 237–249.

Jin, Z., Zhang, X., Zhao, K., Liu, R., Wang, C., 2004. Control of Late Carboniferous-Early Permian sea level fluctuation on coal reservoir heterogeneity, Taiyuan,Shanxi. Petrol. Explor. Dev. 31, 44–49, in Chinese with English abstract.

Kaiser, W.R., Hamilton, D.S., Scott, A.R., Tyler, R., Finley, R.J., 1994. Geological andhydrological controls on the producibility of coalbed methane. J. Geol. Soc. 151,417–420.

Kang, Y.Y., Shao, X.J., Wang, C.F., 2012. Production characteristics and affectingfactors of high-mid rank coalbed methane wells: taking Fanzhuang andHancheng mining areas as examples. Pet. Explor. Dev. 39, 728–732.

Kayacan, E., Ulutas, B., Kaynak, O., 2010. Grey system theory based models in timeseries prediction. Expert Syst. Appl. 37, 1784–1789.

Lv, Y.M., Tang, D.Z., Xu, H., Luo, H.H., 2012. Production characteristics and the keyfactors in high-rank coalbed methane fields: a case study on the FanzhuangBlock, Southern Qinshui Basin, China. Int. J. Coal Geol. 96–97, 93–108.

Ma, D.M., Yin, Q.J., 2002. Analysis of major factor influencing the coalbed methanein Hancheng area. J. Xi'an Univ. Sci. Technol. 22, 162–165, in Chinese withEnglish abstract.

McKee, C.R., Bumb, A.C., 1987. Flow testing coalbed methane production wells inthe presence of water. SPE Form. Eval. 2, 599–608.

Moore, T.A., 2012. Coalbed methane: a review. Int. J. Coal Geol. 101, 36–81.Pashin, J.C., 1991. Regional analysis of the Black Creek-Cobb coalbed methane target

interval, Black Warrior basin, Alabama. Bulletin/Geological Survey of Alabama:Geological Survey of Alabama, Tuscaloosa, Ala, 127.

Pashin, J.C., 1997. Productivity of coalbed methane wells in Alabama. 1997International Coalbed Methane Symposium. The University of Alabama, Tusca-loosa, Alabama, USA, pp. 65–74.

Pashin, J.C., 2010. Variable gas saturation in coalbed methane reservoirs of the BlackWarrior Basin: implications for exploration and production. Int. J. Coal Geol. 82,135–146.

Price, H.S., Ancell, K.L., 1993. Performance characteristics of high permeabilitysaturated and undersaturated coals. In: Proceedings of the 1993 InternationalCoalbed Methane Symposium. The University of Alabama, Tuscaloosa, Alabama,USA, pp. 497–509.

Rahman, M.K., Suarez, Y.A., Chen, Z., Rahman, S.S., 2007. Unsuccessful hydraulicfracturing cases in Australia: investigation into causes of failures and theirremedies. J. Pet. Sci. Eng. 57, 70–81.

Ross, H.E., Hagin, P., Zoback, M.D., 2009. CO2 storage and enhanced coalbedmethane recovery: reservoir characterization and fluid flow simulations of

the Big George coal, Powder River Basin, Wyoming, USA. Int. J. Greenh. Gas Con.3, 774–786.

Sang, S.X., Liu, H.H., Li, Y.M., Li, M.X., Li, L., 2009. Geological controls over coal-bedmethane well production in southern Qinshui basin. Proc. Earth Planet. Sci. 1,917–922.

Scott, A.R., 2002. Hydrogeologic factors affecting gas content distribution in coalbeds. Int. J. Coal Geol. 50, 363–387.

Schraufnagel, R., 1993. Coalbed methane production: hydrocarbons from coal.AAPG Stud. Geol. 38, 341–359.

Shao, X.J., Wang, C.F., Tang, D.Z., Sun, Y.B., Xu, H., 2013. Productivity mode andcontrol factors of coalbed methane wells: a case from Hancheng region. J. ChinaCoal Soc. 38, 217–276, in Chinese with English abstract.

Song, Y., Liu, H.L., Hong, F., Qin, S.F., Liu, S.B., Li, G.Z., Zhao, M.J., 2012. Synclinereservoir pooling as a general model for coalbed methane (CBM) accumula-tions: mechanisms and case studies. J. Pet. Sci. Eng. 88-89, 5–12.

Sparks, D.P., Lambert, S.W., McLendon, T.H., 1993. Coalbed methane well flowperformance controls, Cedar Cove Area, Warrior Basin, U.S.A. In: Proceedings ofthe 1993 International Coalbed Methane Symposium, Birmingham, Alabama,pp. 529–548.

Su, X.B., Lin, X.Y., Liu, S.B., Zhao, M.J., Song, Y., 2005. Geology of coalbed methanereservoirs in the Southeast Qinshui Basin of China. Int. J. Coal Geol. 62, 197–210.

Tao, S., Wang, Y.B., Tang, D.Z., Xu, H., Lv, Y.M., He, W., Li, Y., 2012. Dynamic variationeffects of coal permeability during the coalbed methane development processin the Qinshui Basin, China. Int. J. Coal Geol. 93, 16–22.

Walter, B., Ayers, Jr., 2002. Coalbed gas systems, resources, and production and areview of contrasting cases from the San Juan and Powder River basins. AAPGBull. 86, 1853–1890.

Wang, X.J., Ward, C.R., 2009. Experimental investigation of permeability changeswith pressure depletion in relation to coal quality, In: Proceedings of the 2009International Coalbed Methane Symposium University of Alabama.

Wang, P., Mao, X.B., Lin, J.B., Du, C.Z., 2009. Study of the borehole hydraulicfracturing and the principle of gas seepage in the coal seam. Proc. Earth Planet.Sci. 1, 1561–1573.

Xu, H., Tang, D.Z., Tang, S.H., Zhao, J.L., Meng, Y.J., Tao, S., 2014. A dynamic predictionmodel for gas–water effective permeability based on coalbed methane produc-tion data. Int. J. Coal Geol. 121, 44–52.

Xu, B.X., Li, X.F., Haghighi, M., Du, X.Y., Yang, X.Z., Chen, D., Zhai, Y.Y., 2013. Ananalytical model for desorption area in coal-bed methane production wells.Fuel 106, 766–772.

Xue, G.W., Liu, H.F., Li, W., 2012. Deformed coal types and pore characteristics inHancheng coalmines in Eastern Weibei coalfields. Int. J. Min. Sci. Technol. 22,681–686.

Yang, X.C., Jie, M.X., Wang, G.Q., 2008. Influencing factors on CBM production inPanhe trial production area. Nat. Gas Ind. 28, 99–101, in Chinese with Englishabstract.

Yao, Y.B., Liu, D.M., Qiu, Y.K., 2013. Variable gas content, saturation and accumula-tion characteristics of Weibei coalbed methane pilot-production field in South-east Ordos Basin, China. AAPG Bull. 97, 1371–1393.

Yun, J., Xu, F.Y., Liu, L., Zhong, N.N., Wu, X.B., 2012. New progress and futureprospects of CBM exploration and development in China. Int. J. Min. Sci.Technol. 22, 363–369.

Zeng, W.T., Chen, S.H., Xu, F.Y., 2012. Controlling factor analysis and suggestions onCBM drainage in Hancheng Block. China Pet. Explor. 2, 79–84, in Chinese withEnglish abstract.

Zhao, J.L., Tang, D.Z., Xu, H., Meng, Y.J., Lv, Y.M., Tao, S., 2014. A dynamic predictionmodel for gas-water effective permeability in unsaturated coalbed methanereservoirs based on production data. J. Nat. Gas Sci. Eng. 21, 496–506.







































































































Journal of Petroleum Science and Engineering · Accepted 15 March 2015 Keywords: high production...

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