Mining Science and Technology (China) 21 (2011) 743–747

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Mining Science and Technology (China)

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Application of micro-seismic facies to coal bed methane exploration

Li Donghui a,⇑, Dong Shouhua a, Zhang Cong b, Deng Shuaiqi a, Li Shujie c

a School of Resources and Geoscience, China University of Mining & Technology, Xuzhou 221008, Chinab Huabei Oilfield CBM Branch Company, Jincheng 048000, Chinac School of Economics and Management, Changchun University of Science & Technology, Changchun 130022, China

a r t i c l e i n f o

Article history:Received 10 February 2011Received in revised form 12 March 2011Accepted 15 April 2011Available online 3 November 2011

Keywords:Micro-seismic faciesCoal bed methaneWaveform classificationGas rich area

1674-5264/$ - see front matter � 2011 Published bydoi:10.1016/j.mstc.2011.04.001

⇑ Corresponding author. Tel.: +86 13775982623.E-mail address: ldhyingxiang@163.com (D. Li).

a b s t r a c t

A neural network is applied to high-quality 3-D seismic data during micro-seismic facies analysis to per-form the waveform analysis and training on single reflection events. Modeled seismic channels are estab-lished and the real seismic channels are classified. Thus, a distribution of micro-seismic facies having ahigh precision over a flat surface was acquired. This method applied to existing geological data allowsthe distribution of areas rich in coal bed methane to be clearly defined. A distribution map of themicro-seismic facies in the research area is shown. The data accord well with measured methane con-tents, indicating that the analysis using micro-seismic facies is reliable and effective. This method couldbe applied to coal bed methane exploration and is of great importance to future exploration work and toan increase in the drilling success rate.

� 2011 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction

Conventional methods of interpreting seismic data use mainlythe intensity of the seismic reflection. The concept of ‘‘seismicfacies’’ introduces the use of such information as the amplitudeversus frequency, the geometric shape of consecutive events, orthe interval velocity for analysis of the seismic data. ‘‘Seismic fa-cies’’ are a comprehensive representation of interfaces at all lev-els. The lithology and characteristic sets of sedimentary systemsaffect seismic profiles and seismic facies analysis refers to aninterpretation of the environmental background and lithofaciesaccording to the seismic data. In conventional seismic faciesanalysis a qualitative description is provided from indicationsof seismic facies, i.e., the structure and the architecture of theseismic reflection and the shape of a seismic facies unit. Mostattention has been paid to the composite features of multipleseismic reflection events [1]. Therefore, the vertical time wasof considerable interest in conventional seismic facies analysisbut the significance of single reflection events was generally ig-nored. Although related to sediment the precision and correct-ness of a single reflection could not meet the requirements forfine coal bed methane reservoir description. Accordingly, theapplication of micro-seismic facies analysis for coal bed methaneexploration is discussed herein.

Elsevier B.V. on behalf of China Un

2. Micro-seismic facies analysis

Micro-seismic facies analysis refers to the study of subtlechanges in waveform characteristics from a seismic reflectionevent that reflects the lateral extent of interfaces and stratum con-tinuity. The purpose is the determination of lateral variations inthinner underground reservoirs from differences in microfacies,lithology, reservoir characteristics, or the fluid components of thesedimentary reservoir. Such micro-seismic facies acquired throughanalysis allow the identification and distribution of favorable res-ervoirs in complicated shapes such as rivers, sand bars, or fanbodies.

The analysis differs from conventional seismic facies analysisdone when reservoirs have a larger time thickness. A neural net-work analysis has been used for micro-seismic facies analysis [2].The variability of the seismic waveform signals was used for train-ing and estimation within a focused interval of a 3-D seismic datavolume was the output. A set of synthetic seismic channels wasconstructed through signal analysis, learning, and iteration. Then,these were compared to real seismic data. The iteration was re-peated after adaptation tests and error treatment and the synthe-sized channels were changed to optimize correlation betweenthe model channels and the real ones. Finally, the representative,discrete synthesized seismic waveform signals (i.e., the seismicmodel channels) that reflect the variation in all seismic channelsover the 3-D data volume were established. These have the signif-icance of containing information from the various sedimentary fa-cies. The fit between real seismic channels and the model ones wasdetermined and classes of fit were plotted in different colors. In

iversity of Mining & Technology.

Fig. 1. Demarcation of the geologic horizon of the reflection wave group based on synthesized records.

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this way a micro-seismic facies distribution with indications ofsedimentary facies over the 3-D data volume was acquired [3].

3. Procedure

A map of the micro-seismic facies can be constructed by select-ing subject reservoirs and timing windows, creating the modelchannels, and then forming a facies map specifically [4–8]:

(1) A work area was set. Seismic and logging data were loadedand horizon picking was conducted based on 3-D automatictracking.

(2) A time window for an objective interval was established. Anobjective interval for the micro-seismic facies analysis waschosen and a range and size was selected appropriate forthe geological purpose of the study. During seismic explora-tion of coal fields attention is generally paid only to the lith-ological information at the roof, the floor, and the inner partof the coal seam. Hence, it is inadvisable to select a sectionhaving an extremely large scale. The subject reservoir forthe micro-seismic facies analysis in this study was seamnumber 3 and an interval 15 ms above and below this seamwas adopted as the research reservoir.

(3) Model channels were created. The classes of model channelsand the iteration times were determined through tests. Theclass numbers refers to the number of classes of micro-seis-mic facies that will be classified over the interval. The num-ber should be chosen according to the specific geologicaltask at hand. Seismic exploration of coal fields requiresmicro-seismic facies analysis to identify abnormal geologicbodies or wash zones in the seams, areas rich in coal bedmethane, and other potential problems for mining. Thesegeological tasks are relatively simple so only a few classeswill be enough; for instance the number of classes rangesbetween 5 and 10.

The selection of iteration times follows a general rule thatthe neural network should converge, that the computingtime should be short, and that the work efficiency shouldbe improved. In practice, 10–20 iterations guarantees con-vergence of the neural network and 20–40 iterations is pre-ferred for the final interpretation. 7 classes were adopted inthis work and 35 iterations were performed.

(4) The results were plane mapped. The results from seismicwaveform classification were plane-classified. The wave-forms of the same class were displayed in the same color,while those of other similar classes were displayed in other,similar, colors.

(5) The geological connotations of the classified micro-seismicfacies were given based on the data and analysis presented.

4. An example

4.1. Description of the research area

The strata in the research area were the Shanxi group of thelower Permian system (P1s) and the Taiyuan group of the UpperCarboniferous system (C3t). The total thickness is 136.02 m andthere are 15 layers to the seams. Seam number 3, in the Shanxigroup, and seams number 9 and 15, in the Taiyuan group, is mine-able seams. The demarcation of synthesized seismic records wasused to determine that wave T3 was the reflection wave formedfrom seam number 3. This wave, T3, was the subject reservoir forthis study. Wave group T3 generally exhibits two strong phases,good continuity, and waveform stability and was extremely easyto identify in the time section data. The reflection wave from seamnumber 15 (T15) shows considerable phase intensity, good wave-form stability, and a basic continuity of tracking. The reflectionwave (T9) from seam number 9 has less phase intensity and lackscontinuity in waveform variation (see Fig. 1).

4.2. Division of the micro-seismic facies

The process of waveform classification of micro-seismic facieswas used to divide the micro-seismic facies in area E1. Modeledseismic channels were developed by means of a self-organizedneural network [9–12]. Templates made of these channels wereused to reflect the various morphologies of the seismic signals inthe subject reservoir. Different colors were used to denote the var-ious waveforms. When dividing the micro-seismic facies variousparameters were selected in a way that variation of the sedimen-tary environment was reflected in a regular manner. These param-eters included the size of the time window, the control horizon,and the waveform classes. An interval 15 ms above and belowseam number 3 (the subject of this study) was adopted as the timewindow and 7 model channels (see Fig. 2) were used. These

Fig. 2. Seven model channels.

Fig. 3. Distribution of micro-seismic facies: area E1.Fig. 4. Bit time of seam number three: producing area E1.

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channels represent the waveform types of the seismic data and re-flect variation in the waveform data. Comparing the model chan-nels to the real ones indicates that the optimum result isachieved after 35 times iteration. A map of the micro-seismic faciesforms eventually during this process (Fig. 3). Demarcation andanalysis of the results from analysis of existing data give a rangeof areas rich in coal bed methane. This is the part delimited bythe black line in the distribution map of the micro-seismic facies.

4.3. A geological interpretation of the micro-seismic facies map

The amount of coal bed methane depends on a number of com-peting geological factors. The first are those affecting methane gen-eration (the type of coal-bearing strata were the seam developedand the richness of organic matter, the microscopic components,and the types and coalification of organic matter). Other factors af-fect the sequestration of gas, such as the degree of erosion, the bur-ial depth, the effect of underground water on the geologic structureand the type of wall rock and coal bearing formation existent dur-ing the geologic evolution.

The research area is a highly gassy mine and gas discharge wasconsiderable during mining. The gas content averages 9.03 m3/tand was observed to be as high as 18 m3/t. The mine gas dischargecame from the number three seam, which accounted for 97% of themine gas discharge. Gas from adjacent seams was much less. There

was no spontaneous combustion and the coal dust did not explode.Seam number 3 is of the Shanxi group (P1s) and the paleo-geographic environment for its generation was a coastal alluvialplain. The seam slope is from 2� to l0�. Conditions were favorablefor exploitation and the coal type was a low to mid grey, highstrength blind coal. The coal quality varied little throughout theseam. The seams were stable with little change in thickness inthe studied area. The seams in the vicinity of the Changdian syn-cline are relatively thick with a maximum thickness of 7.5 m. Theseams in the southern Zhengcun anticline are thin with the thin-nest being 5.7 m thick. Hence this belongs to the class of stable,mineable seams.

The immediate roof is generally composed of sandy mudstone,or siltstone, and frequently is accompanied by a pseudo top of thincarbonaceous mudstone or shale. The pseudo top and the immedi-ate roof were rather frangible and broke into pieces, or layers, dur-ing mining, the main roof was relatively stable and did not breakeasily and consisted of fine or middle sized sandstone. The floorwas a black mudstone, sandy mudstone, or siltstone, with partsbeing fine sandstone.

The seam contained low to mid grey, blind coal with low sulfur,medium phosphorous, high heat content, high ash melting temper-ature, and high physical strength. It had good heat stability, wasstrongly slagging, and satisfactory for clean-coal recovery withhigh fixed coal content.

Fig. 5. 3-D geological structure of area E1.

Fig. 6. Coal thickness in area E1.

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The structure of this area was relatively simple and included theZhengcun anticline, the Changdian syncline, and anticline number67 moving from west to east. There was little fracture and no mag-matism. In addition the hydrological and geological conditions ofthe fields were simple.

The analysis of gas richness, the coal reservoirs, the cap rock,the geologic background of gas control, and other factors impliesthat the amount of coal bed methane in the research area wasmainly affected by the burial depth, the strata thickness, and thegeological structure of the seams.

Figs. 4–6 show the fitting of the distribution of micro-seismicfacies. The correctness in the divisions of rich or poor coal bedmethane areas based on micro-seismic facies is verified by prior

research efforts and the measured methane content in field bores[13–15]. This shows that the micro-seismic facies technique canbe applied to coal bed methane exploration.

5. Conclusions

Identification of features in conventional seismic facies analysisis based on the correlation of multiple reflection events. Micro-seismic facies analysis with the neural network method allows astudy of the distribution of micro-seismic facies to be conductedon single reflection events. This method is superior to conventionalseismic facies analysis in both the vertical and the horizontal

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directions: The reliability and rapidness of reservoir depiction isimproved. Contents of measured coal bed methane and prior re-search results accord well with the micro-seismic facies analysis.This indicates that the micro-seismic facies analysis has a satisfac-tory practicality and validity.

Acknowledgments

This work is supported financially by the National Key Project(No. 2008ZX05035-005-003), the National Basic Research Programof China (No. 2009CB219603). The author is grateful to theseorganizations.

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