Estimation of ash, moisture content and detection of coal lithofacies from well logs using regression and artificial neural network modelling

Fuel 177 (2016) 279–287

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Estimation of ash, moisture content and detection of coal lithofacies from well logs using regression and artificial neural network modelling Sayan Ghosh a, Rima Chatterjee b,⇑, Prabhat Shanker a a b

Central Mine Planning and Design Institute Limited, Bilaspur, India Department of Applied Geophysics, Indian School of Mines, Dhanbad, India

h i g h l i g h t s
Hierarchical Cluster Analysis (HCA) is applied for isolation of coal bands.
Multiple regression models are proposed for prediction of coal proximate parameters.
The proximate results are validated with the laboratory data.

a r t i c l e

i n f o

Article history: Received 29 August 2015 Received in revised form 29 February 2016 Accepted 1 March 2016 Available online 16 March 2016 Keywords: Coal Hierarchical clustering Neural network Regression Proximate parameter Korba coalfield

a b s t r a c t Coal core samples and well log data of five exploratory wells of Korba Coalfield (CF), India have been used for prediction of coal facies. The Indian non-coking coal lithofacies are generally classified by analyzing the variation of the geophysical log parameters or by defining the ranges of various proximate parameters (mainly ash % and moisture %) obtained from coal core samples. The objective is to classify each layer as coal, shaly coal and shale depending upon the content of ash % and moisture % of the corresponding layer in coaly horizon. Hierarchical Cluster Analysis (HCA) is applied to classify the non-coal horizons and bands of identified coal seams of each well under the study area based on geophysical log responses: natural gamma ray (NG), high resolution density (HRD) and single point resistance (SPR). Hierarchical clustering separates the zones in a particular coal seam from five wells using the nature of the curve. These zones/clusters are further identified as coal, shaly coal, shale in three wells using regression and multilayer feed forward neural network. The log responses and coal core analyzed proximate parameters of these isolated bands/zones in two wells are used for establishing linear regression and neural network models. The observation shows very satisfactory fit (R2 = 0.84) between ash content and HRD and poor R2(<0.41) between moisture content and log responses. The MLFN model is based on study of two wells using NG, HRD and SPR log responses as inputs and coal proximate parameters, namely, ash and moisture content as outputs to classify the coal lithofacies. The bands within a coal seam are classified on the basis of the ash and moisture content while training as well as the validation of the model. These linear and MLFN models are used to determine the ash % and moisture % in the remaining three testing wells. MLFN predicted results are more closely to the laboratory analyzed proximate parameters as compared to the results obtained from regression modelling. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction The interpretation of coal and non-coal lithofacies from well logs acts as a significant interpretation tool since core and sidewall samples are not usually available. Gas content of coal seams is determined based on several parameters including vitrinite reflectance, maceral content, maturity, coal quality and depth [34]. The

⇑ Corresponding author. E-mail address: [email protected] (R. Chatterjee). http://dx.doi.org/10.1016/j.fuel.2016.03.001 0016-2361/Ó 2016 Elsevier Ltd. All rights reserved.

quality of coal and its rank are determined by the content of carbon, ash, moisture and volatile matter [34]. The quality of coal and its properties generally vary from one coalfield to another [17]. Chatterjee and Paul [7] had discussed the variation of density (1.28 gm/cc to 1.55 gm/cc), gamma ray (20–50 cps) and resistivity (500–1200 ohm-m) values of coal in Jharia coalfield. The density, gamma ray and resistivity values of Manuguru coal of Pranhita– Godavari Valley, Andhra Pradesh ranges from 1.40 to 1.55 g/cc, 20–50 cps and 500–600 ohm m respectively [3]. It is observed that the range for coal density in Bishrampur coalfield varies from 1.28

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Fig. 1. Location of wells under the study area of Korba coalfield. Inset: The location of Korba in India.

Table 1 Generalised stratigraphic sequence of Korba coalfield (after [25]). Age

Formation

Thickness (m)

Lithology

Recent Lower triassic to upper permian

Alluvium Kamthi

0.00–20 >200

Soil and sub-soil Coarse ferruginous sandstones, pebbly sandstone and conglomerate

Lower permian

Upper Barakar Middle Barakar Lower Barakar Karharbari Talchir

>300 >200

Sandstones, shales, carb-shales and coal seams Sandstones of varied grain sizes, shales and carb-shales with thick coal seams (like Kor V & Kor VI)

>300 >150 >250

Predominantly coarse grained to pebbly sannstones with thin banded inferior quality coal Coarse grained to pebbly sandstone with good quality of coal seams (Ghordewa group of seams) Fine grained compact sandstones, tillite and greenish shales.

Archean

Non-conformity

Granite, gneisses, etc.

to 2.05 g/cc [15]. The cut-off ranges of the concerned geophysical log parameters are observed to vary in different Indian coalfields for same lithofacies. Schmitt et al. [32] introduced classification of organic mud-rocks, coal and siliciclastic by using neural network from the well logs. The physical properties of coal like natural radioactivity, single point resistance, electrical conductivity, density, transit travel time, neutron porosity are commonly measured through geophysical logging [24,6]. These are used for determining the type of coal as well as for quantifying coal proximate parameters through laboratory analyses [7,15]. Many researchers have developed several approaches such as statistical [5,31], fuzzy logic [35,29,21,15] and neural network [2,27,4,28,30,22,26] for estimation of coal proximate parameters and lithofacies identification. The Ratija block of Korba Coalfield (CF) shown in Fig. 1 is under the initial stage of exploration. The coal seams including Upper Barakar and Lower Barakar Seams of Korba CF are of highly banded. As noticed from geophysical log and drilling data, most of the seams in Upper and Lower Barakar series include coal, shaly coal and shaly layers of varying thickness. Generally for classifying the lithofacies within and beyond a coal seam of Indian Non-Coking Coalfields, only two of the laboratory proximate parameters such as ash and moisture content are essential and available for wells under study in Korba CF, Chhattisgarh, India. The lithofacies within coal horizons are classified into coal, shaly coal, carbonaceous shale and shale on the basis of sum of ash and moisture percentages of the received coal core (e.g. [8,3,23]). Thus, for extracting the lithofacies from the core

samples, the two proximate parameters namely; ash and moisture content are required for each band. The entire set of four proximate parameters (ash, moisture, volatile matter and fixed carbon) is usually determined for the whole seam to decide the grade of the entire seam packet whereas the layer by layer lithofacies within each seam is governed by the corresponding sum of ash % and moisture % determined in the laboratory. Therefore, the objective of this paper is firstly to isolate the layers/zones using hierarchical clustering analysis (HCA), secondly estimation of ash and moisture content band by band from regression and neural network modelling and finally prediction of coal lithofacies based on predefined cut-off from laboratory analyzed proximate data. Our study is focusing on separation of coal bands occurring at depths from 32 to 198 m from five wells using hierarchical clustering analysis (HCA) and development of regression as well as multilayer feed forward neural network (MLFN) for identification of clustered zone in terms of coal, shaly coal and shale lithofacies. For application of HCA and development of MLFN code in matrix laboratory (MATLAB), Korba CF located at Chhattisgarh State, India (Fig. 1) is chosen for band by band zonation from individual coal seam and detection of coal facies through MLFN modelling.

2. Study area The Korba CF is nearly 4.8 km wide and 64 km long, located in the south central part of the Son–Mahanadi valley, Chhattisgarh

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S. Ghosh et al. / Fuel 177 (2016) 279–287 Table 2 Coal seam correlation from five wells in Korba coalfield. KLB, Korba coalfield Lower Barakar. Seam name

KLB KLB KLB KLB KLB KLB KLB KLB KLB KLB KLB KLB KLB

B C (TOP) C (BOT) D (COMB) E (TOP) E (BOT) F (COMB) G (TOP) G (BOT) H I J (TOP) J (BOT)

WELL KO 1 Surface RL 327.91

WELL KO 2 Surface RL 328.23

WELL KO 3 Surface RL 322.18

WELL KO4 Surface RL 314.47

WELL KO 5 Surface RL 320.59

Roof (m)

Floor (m)

Roof (m)

Floor (m)

Roof (m)

Floor (m)

Roof (m)

Floor (m)

Roof (m)

Floor (m)

– – – 35.64 55.73 60.33 79 88.45

– – – 38.24 57.73 66.24 86.44 90.54

– – – – 36.69 46.71 72.46 97.68

– – – – 39.62 51.4 74.75 99.23

47.05 61.93

108.69 125.42a – –

125.44 146.36 171.72 183.74

127.84 155.2 173.56 197.71

– – – – – – 48.74 78.13 81.02 108.25 129.8 153.55 –

41.8 53.86

107.85 124.22 – –

– – – – – – 45.35 76.93 80.17 106.1 123.77 152.2 –

87.61 98.26 121 – – – – – – –

88.39 106.35 125.71 – – – – – – –

– – – – – – 32.38 62.36 – 88.7 109.66 124.91

– – – – – – 35.49 65.57 – 91.21 114.64 138.69

RL, reduced level, TOP, top of seam, BOT, bottom of seam, COMB, combined seam. a Seam not developed properly.

Fig. 2. Dendogram showing the separation of three clusters for seam KLB E of well KO1 from 29 zones.

Table 3a Definition of the cluster number from three variables (three geophysical logs) as observed from HCA model of coal seams for five wells, Ratija block, Korba coalfield. Range of mean value of HRD log (cps)

Range of mean value of SPR log (Ohm)

Range of mean value of NG log (cps)

Cluster number

16,112–17,835 8965–12,551 18,723–19,627

161–211 83–110 114–355

106–139 166–292 62–125

1 2 3

state is one of the biggest coal producing units of India. This coalfield has large reserve of inferior banded and considerable reserve of superior quality of coal [8]. This CF is a part of the Lower Gondwana formations in the Mahanadi valley covering an area of about 530 sq. km with an estimated reserve of about 10,115 million tonnes [33]. The CF stretches along east–west orientation bounded by longitudes 82°150 –82°550 and latitudes 22°150 –22°300 [25]. The geological stratigraphic sequence of the Korba CF is shown in Table 1. The Precambrian surrounds the entire boundary of the Korba CF including granites, amphibolites and quartzite, whereas the Talcher sediments dominated by green shales, clays and siltstones encompass the northern boundary of the coalfield overlying

the Precambrian metamorphic [8,9]. The Ratija block lies in the western part sharing its eastern block boundary with Dipka, Renki and Hardi blocks as shown in Fig. 1. Coal seams (including coal, shaly coal and shale) encountered at Ratija Block of Korba coalfield exhibit an ash content of 11.0–57.8% and moisture content of 2.4–9.4% as obtained from drilling data. There are about 45 major exploratory including mining blocks in the Korba coalfield. Our study area lies in the Ratija block, southwest part of the Korba coalfield surrounded by Nunbera, Dipka, Renki and Hardi blocks as shown in Fig. 1. Six numbers of seams out of the total 16 numbers of major coal seams of the Korba coalfield occur in Upper Barakar formation with thickness varying from 1 to 71 m whereas the thickness of other 10 seams of Lower Barakar formation ranges from 0.2 to 10 m. Ten numbers of major coal seams of Lower Barakar formation are distinguishable in the Ratija block whose thickness ranges from 0.12 to 13.78 m. This block comprises the Lower Barakar formation containing pebbly sandstones with grey shale and carbonaceous shale with some thin inter-layered and inferior quality of coal seams. The exploration drilling in the Ratija block is still under continuation for estimation of the coal reserve in the block. The well logs of five wells KO1 to KO5 belonging to the study area of Ratija block are considered for coal seam correlation. Nine numbers of coal seams encountered in the five wells are correlated as shown in Table 2.

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Table 3b Lists the cluster number, mean, standard deviation of log responses, number of seam and occurrence of clusters in coal seams for five wells Ratija block, Korba coalfield. Well

SPR (ohm)

NG (cps)

Standard Deviation

Mean

Standard Deviation

Mean

Standard Deviation

16112.3 11536.7 18723.8

405.1 1406.6 1223.6

184.7 109.3 214.8

16.4 257.4 63.1

138.6 275.4 95.22

1 2

17517.5 8965.4

423.3 1343.8

161.2 108.7

80.02 45.9

3

18803.9

1222.1

146.7

3

1 2 3

17835.2 11005.3 19555.8

254.6 753.6 1177.2

211.2 109.4 220.4

4

1 2 3

17834.4 9185.8 18357.5

325.6 1011.2 673.6

169.1 83.45 114.9

5

1 2 3

16376.5 12551.5 19627.9

118.2 2029.5 439.1

210.2 101.7 254.6

KO 1 KO 2

KO

KO

KO

Cluster number

HRD (cps) Mean

1 2 3

No. of seams

Occurrence of clusters in specified seam

15.21 109.3 17.1

1 4 4

KLB F KLB E, KLB F, KLB G and KLB H KLB E, KLB F, KLB G and KLB H

130.6 182.0

56.3 64.1

5 6

37.9

113.6

56.0

6

KLB E M, KLB E B, LOCAL, KLB F COMB, KLB H KLB E T, KLB E M, KLB E B, LOCAL, KLB F COMB, KLB H KLB E T, KLB E M, KLB E B, LOCAL, KLB F COMB, KLB H

115.1 34.7 138.2

106.9 166.7 62.6

6.96 77.1 21.6

1 4 4

KLB I COMB KLB F COMB, LOCAL, KLB H and KLB I COMB KLB F COMB, LOCAL, KLB H and KLB I COMB

109.0 100.01 124.9

8.4 12.9 60.45

1 1 1

KLB B KLB B KLB B

1 4 4

KLB F KLB F, KLB H, KLB I and KLB J KLB F, KLB H, KLB I and KLB J

13.6 27.5 3.14 112.0 6.9 53.0

114.3 291.4 68.8

17.7 142.0 22.6

COMB: Combined.

Table 4 Set of data point assembled from wells KO1 and KO5 by synchronising the cluster zones in geophysical logs and bands in the laboratory core analysed data.

3. Methodology

KLB G

In absence of core samples, the available geophysical log responses such as: NG in cps, high resolution density (HRD) in cps and SPR in ohm from Korba CF have been used to classify the layers/zones in coal seam. Hence the preliminary task in the study is to isolate these layers in each seam and identified as coal, shaly coal or shale depending upon their properties through modelling. Hierarchical cluster analysis [16] has been implemented on the geophysical logs (NG, HRD, SPR and NG). The KLB E seam of well KO 1 is considered typically as an example for clustering analysis and generation of training dataset for modelling in foregoing sections.

KLB H

3.1. Hierarchical Cluster Analysis (HCA)

S. no.

Well name

NG (cps)

HRD (cps)

SPR (ohm)

Ash (%)

Moisture (%)

Seam name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

KO1

20472.65 17552.32 12002.06 17248.21 12135.59 19442.79 18154.64 18306.03 15851.32 11474.26 19890.15 20472.5 16245.68 12716.76 13178.24 9486.47 10361.03 17354.12 10866.76 19065.29

32.21 102.3 178.01 104.57 99.42 102.23 107.87 108.79 124.13 320.27 71.72 69.12 151.74 214.23 270.09 272.69 289.39 182.02 145.04 136.69

397.82 166.75 117.18 159.87 172.2 190.36 246.44 156.2 172.05 80.76 263.91 320.4 175.87 113.18 153.09 77.18 114.38 138.76 108.64 186.13

13.65 36.02 79.94 45.46 74.94 23.54 28.67 34.67 34.56 81.2 13.58 12.85 21.32 76.86 75.25 85.93 83.49 65.73 86.03 37.50

6.73 4.29 1.80 4.51 1.95 6.01 5.52 4.87 5.65 4.57 5.73 6.75 5.98 5.73 6.11 3.98 4.72 2.91 1.48 3.86

KLB D KLB E

21 22 23 24 25 26 27 28 29 30

KO5

19359.56 19475.49 13986.27 19908.38 16460.13 19515.44 16292.94 11116.03 19143.82 20,365

76.38 52.87 190.95 57.76 126.88 39.77 101.79 391.76 99.89 86.4

196.24 198.45 96.82 277.47 170.98 335.15 329.4 106.65 274.76 245.85

41.7 46.9 80.5 20.7 45.4 22.0 41.2 80.5 26.2 23.5

5.0 4.3 2.4 5.6 4.2 6.0 4.7 2.4 4.9 4.5

KLB F

KLB I

KLB H KLB I

KLB J

BOT, Bottom.

The coal seams encountered in the Ratija block are of inferior quality and are usually interbanded with dirt bands like shale with varying thicknesses. The five wells penetrate coal bearing strata, encountered a total of 9 seams and thin interbedded carbonaceous and non-carbonaceous layers except seam KLB H (Table 2). The seam KLB E (11.36 m) encountered in KO1, KO2 and KO4 is the most prominent seam but contains 15 dirt bands in both KO1 and KO2 wells with thickness varying from 0.17 m to 1 m.

HCA is an unsupervised classification, which provides subgroups depending upon criteria from a group of objects [16]. The analysis might be of two types: agglomerative or divisive methods. The agglomerative technique does fusion of number of objects into groups, whereas the divisive technique splits the groups into finer subgroups [16]. For agglomerative approach, the analysis starts from the leaves and proceeds towards the root, while for the divisive technique it is vice versa [12]. The criteria opted in the study for delineating the clusters is the distance between the observations on city metric scale which is a type of Minkowski scale [19]. Minkowski scale:

dst ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn p jXsj  Xtjjp k¼0

ð1Þ

where dst is the distance between two input objects Xs and Xt. When p = 1 in Eq. (1)

dst ¼

n X jXsj  Xtjjp

ð2Þ

j¼0

it becomes in city metric scale. For employing agglomerative approach the distance between two clusters is defined by:

dðr; sÞ ¼

nr ns X 1 1X  distðxri; xsjÞ nr ns i¼1 j¼1

ð3Þ

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Fig. 3. Linear regression relationship between (a) moisture content and HRD, (b) ash content and HRD, (c) ash content and NG, (d) moisture content and SPR, (e) moisture content and SPR and (f) ash content and SPR. HRD, High resolution density, NG, Natural gamma, SPR, Single point resistance.

Fig. 4. (a) Illustrates typical architecture of Multi Layer Feed Forward Network (MLFN) model and (b) showing the mean square error curve with epoch with hidden nodes 3 for training and validation of MLFN model.

where nr is the number of objects in cluster r, xri is the ith object in cluster r. The agglomerative approach is preferred and chosen because for higher resolution and marking out the finer bands; interested part in the dendogram is the lower part of the tree as typically for seam KLB E of well KO1 (Fig. 2). In this dendogram plot, xaxis presents the distance between clusters obtained from Eq. (3) and y-axis presents the zones as 30 individual cases for KLB E seam of well KO1. The dendogram is read from left to right. Vertical lines illustrate joined clusters. The position of the line on the scale indi-

Table 5 Identification of lithofacies from measurement of ash and moisture content of coal core samples, Korba coalfield. S. no.

Range of ash + moisture (%)

Lithofacies

1 2 3

0–40 40–55 More than 55

Coal (cluster number 3) Shaly coal (cluster number 1) Shale (cluster number 2)

cates the distance at which clusters are joined. The cluster analysis for the shown seam generates two numbers of isolated clusters as noticed from the dendogram. Based on this HCA, each of the coal seams from five wells is classified into maximum three zones. The classification of zones with defined cluster number obtained from this analysis is tabulated

Table 6 Performance of models in terms number of neurons in hidden layer, epochs, mean square error (MSE) and correlation coefficient (r2) in training and validation stages. S. no.

Number of neurons in hidden layer

Epochs

MSE

r2 Training stage

r2 Validation stage

1 2 3 4 5

7 6 5 4 3

34 42 32 35 30

0.00131 0.00545 0.00077 0.00219 0.00116

0.9521 0.9601 0.8908 0.9684 0.9987

0.8351 0.9141 0.9023 0.7432 0.9544

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Fig. 5. (a) Correlation between ash % from MLFN model and ash % from laboratory analysed data, (b) correlation between ash % from regression model and ash % from laboratory analysed data, (c) correlation between moisture % from MLFN model and moisture % from laboratory analysed data and (d) correlation between moisture % from regression model and moisture % from laboratory analysed data.

in Table 3a. Table 3b lists the numbers of clusters observed from each of five wells KO1 through KO5. Table 3b indicates the range of mean values and standard deviation of three variables namely; HRD, SPR and NG for cluster classification. Occurrences of clusters classified in coal seams of five wells are provided in this table. For instance, two numbers of clusters are separated in seams KLB E, KLB G, KLB H and three numbers of clusters are observed in seam KLB F of well KO1 (Table 3b). Hence, for the present study, log signatures of zones in coal seams play a dominant role to categorize these classes for prediction of coal, shaly coal and shale.

3.2. Regression and MLFN models An attempt is made to analyze the feasibility of a linear relationship between the geophysical parameters and coal proximate parameters from wells KO1 and KO5 as shown in Table 4 so that the lithofacies can be predicted for each encountered band within every seam in the three test wells; KO2, KO3 and KO4. In each regression plot the geophysical parameter is plotted along x-axis and the laboratory analyzed coal proximate parameter is plotted along y-axis as shown in Fig. 3. The comparison of the goodness of fit (R2) reveals a very good linear model between ash % and HRD whereas poor fits are noticed between moisture % and log parameters. The R2 for best fit linear model between ash % and HRD is observed as 0.84 whereas that of the moisture % with HRD exhibits very low value (0.41). This model is not acceptable to interpret the corresponding lithofacies with a considerable degree of accuracy. Thus, an alternative approach such as: MLFN model is designed other than these two linear models to predict the ash and moisture percentages from well logs for detection of coal facies. The basic input data (including geophysical log and core analyzed data) involved in the network training is considered for designing the multilayer perceptron model. The geophysical input parameters involves HRD (cps), SPR (ohm) and NG (cps) and the desired out-

puts are ash % and moisture % obtained from the proximate analysis of the core samples from two wells namely, KO1 and KO5. The multilayer neuron/perceptron networks constitute one or more hidden layers with a specific number of neurons in each layer. The transfer function generates link between the successive layers. In the current study, three layers are opted: input, hidden and output. These layers are linked with the transfer functions. A general multilayer perceptron network architecture of j hidden perceptron neurons connected to r inputs through weights. W1r,j (input to hidden layer) and through weights W2j,n (hidden layer to output) is shown in Fig. 4(a) [13,20,10]. The input layer is linked with the hidden layer with the hyperbolic tangent transfer function (tansig) which is related to bipolar sigmoid function with output ranging from 1 to 1.

tansigðWÞ ¼ f 1 ¼ f ðwÞ ¼

2 1 1 þ e2w

ð4Þ

where W is the weighted sum of the input. Similarly, the pure linear transfer function (purelin) is used as the transfer function from the hidden layer to the output layer.

f 2 ¼ f ðzÞ ¼ z

ð5Þ

where z is the output of the hidden layer. The multilayer perceptron learning algorithm can be briefly explained as follows [11,13]: (a) The weights W11,1, W11,2, . . ., W1r,j are initialized with initial biases b1(1), b1(2), . . . b1(j) for the first transfer function whereas weights W21,1, W21,2, . . ., W2j,n with initial biases b2(1), b2(2), . . . b2(n) for the second transfer function. (b) The inputs x1, x2, x3 . . ., xr (representing the geophysical log responses) are included for the hidden layer. The desired output (y) while training is represented by the vector d1, d2, . . ., dn (representing the core analyzed ash and moisture content) for the wells KO1 and KO5.

S. Ghosh et al. / Fuel 177 (2016) 279–287

285

Fig. 6. The graphical output exhibiting the geophysical logs, lithofacies derived from MLFN model and laboratory data for seam (a) KLB E TOP, (b) KLB E BOT and (c) KLB F in a well KO2.

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Table 7 The estimated ash % and moisture % from Laboratory (Lab) test, regression and MLFN models.

a b

Well Name

Depth (m)

Thickness (m)

KO2

26.44 28.6 28.9 36.9 38.5 49.6 50.36 56.7 57.7 98.04 73.6 74.6 101.7 127 155.1 184 189.5 213.41

1.02 0.97 0.43 1.2 1.33 0.85 0.3 0.24 1.23 1.05 0.7 0.3 0.4 2.4 0.64 0.24 0.47 0.37

KO3

47 60 106.11 126.36 153.23

KO4

32.16 44.2 46.19 46.43 53.88 54.14 54.29 54.48 54.61 54.91 98.20

Lithofacies from MLFNb

Seam name

4.60 3.38 4.16 3.91 4.95 4.84 3.579 3.95 4.15 5.03 5.01 3.79 4.56 7.83 6.41 4.47 5.45 6.64

CO SH CO SHC CO SHC SH SHC SHC CO CO SHC SHC CO CO SHC CO CO

KLB E TOP

26.50 18.78 23.95 30.66 30.76

3.82 4.59 6.67 6.38 6.28

SHC CO CO CO CO

KLB F LOCAL KLB H KLB I KLB J TOP

41.47 44.12 72.62 33.96 36.24 34.38 73.81 39.86 69.54 44.87 44.31

3.83 4.62 3.65 4.64 4.00 4.19 3.67 3.82 3.64 5.25 4.25

SHC SHC SH SHC SHC SHC SH SHC SH SHC SHC

LOCAL KLB

Estimated from MLFN model (%)

Lab analysed data (%)

Estimated from linear regression (%)

Ash

Moist

Ash

Moist

Ash

Moist

35.2 79.15 30.85 48.2 34.79 42.13 79.30 49.52 38.84 32.30 29.10 45.04 39.08 19.60 21.01 33.36 25.49 13.96

4.81 3.16 5.03 4.50 4.88 4.28 3.16 4.08 4.49 5.01 5.10 4.54 4.49 5.95 5.30 4.93 5.18 5.57

34.82 78.34 30.61 49.81 34.26 44.25 77.51 51.31 38.77 30.93 27.33 46.92 40.58 19.04 21.96 34.72 23.76 13.36

5.09 3.03 5.81 4.35 5.06 3.91 3.00 3.68 4.32 5.20 5.78 4.33 3.97 5.53 5.22 4.24 4.70 5.94

26.68 65.34 33.57 46.70 32.84 42.41 72.04 48.77 49.67 26.33 20.31 42.56 39.35 26.53 32.99 32.13 30.58 22.87

1.62 0.77 2.21a 3.56 1.71

42.56 24.81 11.59a 24.55 24.56

4.69 5.20 5.65a 5.21 5.20

41.50 26.00 20.26 25.69 24.7

4.50 4.50 5.42a 4.77 4.10

0.67 2.08 0.2 0.7 0.26 0.15 0.19 0.13 0.26 2.35 1.97

44.78 42.1 78.12 40.95 42.08 38.5 73.5 38.68 74.09 39.47 45.58

4.69 4.54 3.09 4.65 4.73 4.8 3.4 4.71 3.35 4.59 4.58

42.7 42.0 78.0 39.2 44.6 37.6 75.9 36.8 68.9 38.5 47.4

5.1 4.3 3.0 4.5 4.8 4.1 3.7 4.2 2.9 4.7 3.5

KLB E BOT

KLB G KLB F LOCAL KLB H KLB I KLB J TOP KLB J BOT

KLB C TOP

KLB E TOP

Notifies that the core recovery in the sample is less than 30% of the total thickness of the band. The predicted lithofacies using Table 6 from the estimated ash % and moisture % from MLFN model. BOT, bottom, CO, coal, SH, shale, SHC, shaly coal, Moist, moisture.

(c) The actual output is calculated by using the equation

  X   yk ¼ f 2 W2 f 1 ðW1xÞ þ b1 þ b2

ð6Þ

k = 1–n (refer Fig. 4(a)). The weights and biases for the next epoch are updated according to the Levenberg–Marquardt algorithm [18,32]. The weights and biases in the network are updated in each iteration until the desired target is achieved. The MLFN reads the input and output values in the training data set (Table 4) and changes the value of the weighted links to reduce the difference between the predicted and observed values of ash and moisture content. A complete cycle of forward–backward passes including weight updating in the data set is called an epoch or iteration [14]. Lithofacies (coal, shaly coal and shale) for Indian Non-Coking coal is defined by Bhaskar [3]. The classification on the basis of ash and moisture content of the core samples with pre-defined cluster identity is shown in Table 5 and is implemented in the current study to predict different lithofacies within a coal seam from the estimated ash and moisture data using MLFN model. Single hidden layer with 3–7 neurons are used to minimize the error for each model. Mean square error (MSE) between the predicted and desired output as given in Eq. (7) indicates the performance of models [14].

P MSE ¼

ðyk  oi Þ2 n

ð7Þ

where yk is the predicted output, oi the observed output or target value, and n is the number of training dataset. The performance of the training is monitored by the MSE for each model as listed in Table 6 [1]. It is observed that network is optimized with three neurons at 30 epochs. MSE is reached minimum at 30 epochs with excellent correlation at training and validation stages. Fig. 4(b) illustrates the MSE plot with epoch for training and validation stages considering 3 neurons in the hidden layer. Out of all the combinations, it is obvious that the model with 3 hidden neurons with 30 epochs provides relatively better results, 0.99 and 0.95 as correlation coefficients (r2s). Hence this MLFN model is selected as the final model to be used for estimation of ash and moisture content from other wells namely; KO2, KO3 and KO4. 3.3. Results and discussion Fig. 5(a) and (c) shows the correlation between the MLFN estimated ash % and moisture % and core analyzed data for the three test wells. Fig. 5(b) and (d) show the correlation between regression modelled outputs and core analyzed results respectively (Fig. 5). Correlation between the ash % estimated from regression shows a very good correlation with the laboratory analysed ash % (r2 = 0.85). The ash % obtained from MLFN model is almost similar to that ash % obtained from core sample analyzed in laboratory (with r2 = 0.99). The moisture % obtained from MLFN model shows a significant correspondence with that of the laboratory analyzed

S. Ghosh et al. / Fuel 177 (2016) 279–287

moisture content (with r2 = 0.71). Therefore, the MLFN modelling provides a better assessment of coal lithofacies based on ash and moisture content of Indian non coking coal. Data from three boreholes KO2, KO3 and KO4 are used for prediction of ash and moisture contents and to classify the lithofacies in Indian Non-Coking coal using regression and MLFN techniques (Table 5). The predicted and obtained (from proximate analyses) lithofacies for seams KLB E TOP, KLB E BOTTOM and KLB F of a test well KO2 is presented in Fig. 6 and Table 7 are in close agreement. 4. Conclusions The present results indicates that: (a) The regression model between ash % and HRD indicates a very good fit with R2 = 0.84 whereas model between moisture % and SPR indicates a poor fit with R2 = 0.41. (b) The MLFN predicted ash % and moisture % show excellent to good correlation with the ash % and moisture % obtained from laboratory studies respectively. (c) Model predicted results are validated by the log responses and core analyzed data for three wells under study. The MLFN trained network has been used for prediction of lithofacies for this coalfield. (d) The ash % and moisture content % were used successfully to detect lithofacies in coal seams such as shale, coal and shaly coal for three test wells. (e) The MLFN modelling can be used for any number of seams irrespective of the number of lithofacies within the seam. It will work considerably well if the training of the networks accounts for synchronising the layers in the log responses and corresponding core analysed ash and moisture content.

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