Estimation of shale volume from well logging data using Artificial Neural Network
46
Journal of Mining and Earth Sciences Vol. 62, Issue 3 (2020) 46 - 52
Estimation of shale volume from well logging data
using Artificial Neural Network
Duong Hong Vu*, Hung Tien Nguyen
Faculty of Oil and Gas, Hanoi University of Mining and Geology, Vietnam
ARTICLE INFO
ABSTRACT
Article history:
The existence of shale has a major effect on reservoir quality because it
reduces the rock’s both the porosity and permeability. There are several
types of shale, and they can be distributed in the sand in four different
ways: laminated, structural, dispersed, or any combination of these. Each
of them has various features and physical properties. Therefore, shale
volume estimation is one of the most important and challengin tasks to be
solved information evaluation. There are many equations proposed to
calculate shale volume from Gamma - ray log; however, none of them
couldbe considered the best methodthat can be applied to all case studies.
This study aims to propose a new approach to estimate shale volume from
well - logging data. Gamma - ray and other logs were used as input data
for an artificial neural network (ANN) to predict the shale volume. We
apply this technique to the 1143 data set of the ocean drilling program
(ODP) in the East Sea. The authors compared the result to core data and
recognized that utilization of several logs and ANN gives a better
estimation than conventional methods (more accurate and can reflect the
trend of actual shale volume).
Received 11th Feb. 2021
Accepted 25th May 2021
Available online 30th June 2021
Keywords:
Neural network,
Volume of shale,
Well logging.
Copyright © 2021 Hanoi University of Mining and Geology. All rights reserved.
solution effects. Thus, the images are usually
calibrated by well - logging data (Wu et al., 2017).
1. Introduction
Submarine geological images are often built
from the processing and analysis of geophysical
data (Braitenberg et al., 2006; Huang and Wang,
2006; Ding et al., 2013; Gozzard et al., 2018; Ding
et al., 2018). However, the marine geophysical
data is of low resolution and non - unique of
This work is often time - consuming, subjective,
and lacks quantification. To overcome these
challenges, now computational tools such as
machine learning (ML) and artificial intelligence
(AI) are being used more and more in data
processing, including geophysical processing.
Specifically, the application of these tools for the
analysis of well log data has been published by
Saumen Maiti et al. (2007), Bosch et al. (2013),
Dekkers et al. (2014), and Aarushi Gupta and
Utkarsh Soumya (2020).
_____________________
*Corresponding author
E - mail: [email protected].vn
DOI: 10.46326/JMES.2021.62(3).06
Duong Hong Vu, Hung Tien Nguyen /Journal of Mining and Earth Sciences 62(3), 46 - 52
47
In the East Sea, there are also similar studies
which were proposed by Karmakar et al. (2018);
Tse et al. (2019). The results obtained by these
authors show the effectiveness when applying
these ML and AI techniques to solve geological
structure interpretation missions. Shale has a
major effect on reservoir quality because it
reduces both porosity and permeability of the
rock. There are several types of shale, and they
can be distributed in the sand in four different
ways: laminated, structural, dispersed or any
combination of these. Each of them has various
features and physical properties. Therefore shale
volumeestimationis one of themostessential and
challenging tasks to be solved in formation
evaluation. So far, many mathematical models
have been proposed to calculate shale volume
from Gamma - ray log. However, the results are
not always accurate due to complicated geological
conditions. This study aims to propose a new
approach to estimate shale volume from well log
data based on the application of an artificial
neural network. We apply this technique tothe
1143 data set, the ocean drilling program (ODP)
in the East Sea (Figure 1).
2. Methodology
In this study, a new ANN model was
developed to predict Vsh from well logging data.
To demonstrate the effectiveness of the model,
the prediction result is compared with traditional
methods.
Normally, estimating Vsh from the gamma -
ray log still remains the most preferred approach.
The procedure is easy, straighforward and likely
to give reasonable results. There are two
commonly used equations introduced by Clavier
et al. (1971) and Steiber (1973).
2
(1)
(
)
VshClavier=1.7 - √3.38 − (퐼퐺푅 + 0.7)
퐼퐺푅
VshStieber
=
(2)
3−2퐼퐺푅
In which IGR shaliness index is defined by a
function of the GR ((gamma - ray)) gamma ray log
signal: (a) record of the response of the GR log for
a nearby known shale body and a nearby known
clean rock.
퐺푅푙표ꢀ − 퐺푅퐶푙푒푎푛푅표푐푘
퐼퐺푅 =
(3)
퐺푅푠ℎ푎푙푒 − 퐺푟퐶푙푒푎푛푅표푐푘
Figure 1. Research area. (source: Google maps).
48
Duong Hong Vu, Hung Tien Nguyen /Journal of Mining and Earth Sciences 62(3), 46 - 52
An ANN network using back - propagation
3.1. Data preprocessing
training algorithm (BPNN) and logsig activation
function was proposed to predict the volume of
shale from well - logging data. We used data from
well A and B to train the ANN model, then used
data of well C to evaluate the accuracy of the
prediction model. Finally, the proposed ANN
model was used to predict Vsh from input data of
well C; then its performance was compared to
Clavier and Steiber model.
Abnormal data can be considered as noise
because they can negatively influence the ANN
model and may restrict the model in its
generalization. The data set of three wells are
tested for anomaly by Z - score outlier detection
algorithm where a threshold Z - Score of 3 is
selected (Tripathy et al., 2013). Any data points
above this threshold are marked as an outlier and
excluded from the training data. The Z - score is
the score given to the participant as per their
performance:
3. Data sets
In this study, we used data sources from
z = |Xi - Xmean|/ SD
(4)
International
Ocean
Discovery Program
there are three wells OPD - 184 - 1143 A, B, and C
(Figure 1), for brevity in this article we named
them well A, B, C, respectively (Figure 1).
The data used in this study include physical
and lithological parameters measured in a
borehole such as the longitudinal wave velocity
Where: Xmean - the mean value of the data; SD
- the standard deviation of the data; SD - the
standard deviation of the data.
To simplifytheinterpretationof thez -scores,
the following agreements were made as:
z < 2 implies the result is satisfactory.
2 < z < 3 implies the result is questionable.
z > 3 implies the result is unsatisfactory.
To reduce volatility and eliminate statistical
noise, the dataset is further processed and
smoothened by a low - pass second – order
Butterworth filter (Selesnick and Burrus, 1998).
Figure 2 shows the example comparing raw
and smoothened ((P - wave)) Pwave velocity data
of well A.
(Vp), gamma
-
ray, density, magnetic
susceptibility, and volume of shale (Vsh) in the
core sample (Table 1).
Table 1. Summary of well log data.
Parameters
Number of core
Well A
242
Well B
118
Well C
74
Top
24.05
44.05
382.35
Depth (m)
Bottom 399.05 249.49 499.75
Min
Max
1550.56 1556.63 1714.9
2047.57 1803.72 2163.2
Vp (m/s)
GR (API)
Mean 1733.11 1623.25 1877.8
Stdev
Min
119.30
20.25
54.75
37.43
6.85
56.99
26.50
54.00
41.04
5.74
77.35
24.75
49.00
33.15
4.36
Max
Mean
Stdev
Min
1.37
1.47
1.61
Max
1.82
1.66
0.09
1.00
54.00
14.66
8.89
1.83
1.65
0.08
5.50
29.50
18.73
6.80
1.93
1.77
0.07
3.20
14.50
8.04
2.10
Density
(g/cc)
Mean
Stdev
Min
Figure 2. Pwave velocity smoothened.
Magnetic
susceptib -
ility (SI)
Max
3.2. Data analysis
Mean
Stdev
Min
The selection of input parameters for the
training process is an important step, which
determines the accuracy of the ANN model. To
decide which parameter to be used as input data,
the interrelationships between parameters were
0.00
5.00
5.00
Max
Mean
Stdev
98.00
72.39
26.89
98.00
78.36
24.02
94.00
60.89
30.58
Vsh (%)
Duong Hong Vu, Hung Tien Nguyen /Journal of Mining and Earth Sciences 62(3), 46 - 52
49
studied using cross plots as shown in Figure 3. A
regression coefficient closer to 1 represents a
positive correlation and closer to - 1 represents a
negative correlation between variables. From
Figure 3, we can see that all parameters: P - wave
velocity (m/hr), GR (API), Density (g / cc),
Magnetic suscept. (SI) are suitable and can be
retained in the ANN model development.
train the network, 15% is used for testing and,
15% for the validation. Four parameters: P - wave
velocity (m/hr), GR (API), Density (g/cc),
Magnetic suscept(SI) areconsideredas input data
and the output value of the ANN model is the
volume of shale. The calculated output from ANN
after a cycle (or iteration) is compared with the
actual output given in the sample dataset (Vsh of
core) to trace the error. This error is propagated
back to output neurons and hidden neurons so
that these neurons adjust their weights. This
bidirectional propagation is carried out
repeatedly, until the error reaches a minimum
value less than a certain allowable value or until
the number of loops reaches a predetermined
value. The accuracy of the data model is
demonstrated by the root mean square which
serves as a metric to score the predictions of the
ANN model with the expected result.
4. Predict Vsh model development
An ANN network using back - propagation
training algorithm (BPNN) and logsig activation
function was proposed to predict the volume of
shale from well logging data (Figure 4). In this
study, we used data from well A and B to train the
ANN model, then used data from well C to
evaluate the accuracy of the prediction model.
A training data set of 316 samples from data
of two wells A and B includeseveral parameters:
longitudinal wave velocity, gamma - ray, density,
magnetic susceptibility from well logging data,
and volume of shale from core data. This database
is divided into 3 sets: 70% of the sample is used to
2
∑
(푉
−푉
)
푠ℎ
푎푐푡푢푎푙
푠ℎ
푝푟푒푑ꢁ푐푡
(5)
√
RMSerror
=
푛
Figure 3. Crossplot between well logging parameters.
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Duong Hong Vu, Hung Tien Nguyen /Journal of Mining and Earth Sciences 62(3), 46 - 52
Determining the number of neurons in the
5. Results and discussions
hidden layer is a challenging step in model design,
and there is no rigid rule to do it. In this study, to
determinetheoptimal numberof hiddenneurons,
different scenarios were carried out with variable
numbers of neurons in the hidden layer and tests
for their effect on the final prediction (Figure 5). It
is worth noting that the number of neurons in the
hidden layer should be chosen carefully since too
many neurons in the hidden layer can lead to
overfitting, making the network lose its
generalization. Therefore, we decided to use 30
neurons in one hidden layer, Figure 5 shows that
the ANN model with one hidden layer including
30 neurons given the RMSE = 0.002 and R2 =
0.956.
The proposed ANN model was used to
predict Vsh from input data of well C, then
compare its performance with Clavier and Steiber
model. It can be seen that ANN model predictions
are much more accurate than the other two
traditional models, as confirmed by both RMSE
and R2 in Table 2. It is observed from Figure 6 and
Figure 7 that prediction from the ANN model
follows the trend of actual Vsh.
Table 2. Model performance comparison.
Model
RMSE
0.2616
0.29
R2
0.022
0.019
0.92
1
2
3
Clavier model
Steiber model
ANN model
0.0037
Figure 5. (a, b) - Optimum number of neural in hidden layer.
Figure 6. Vsh prediction by ANN for well C.
Duong Hong Vu, Hung Tien Nguyen /Journal of Mining and Earth Sciences 62(3), 46 - 52
51
Figure 7. Vsh prediction by Clavier, Steiber model for well C.
Meanwhile, Vsh calculated by Clavier and
References
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Author contributions
The author Duong Hong Vu proposes ideas
and contributes to the manuscript. The author
Hung Tien Nguyen constructs the manuscript and
contributes to the material analyses. The authors
both declare no conflict of interest.
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