ACADEMIC RESEARCH IN MODERN SCIENCE
International scientific-online conference
74
APPLICATION OF FACTOR ANALYSIS TO SELECT OPTIMAL TEST
DEPTHS FOR PROSPECTING AND EXPLORATION WELLS (USING
THE EXAMPLE OF THE BUKHARA-KHIVA REGION)
Ayupova Nodira Abbos qizi
Engineer II category
JV LLC "Geo Research and Development Company"
https://doi.org/10.5281/zenodo.12705208
Geological objects, as a rule, are very complex and diverse, since their
formation is usually due to the action of many different
factors (causes)
.
Therefore, for a more complete specification of geological objects, they are
usually characterized by a set of various features
(parameters)
, and the
measurement results of a set of these features are presented in the form of
multidimensional random variables. In the exploration of complex geological
objects,
factor analysis
allows a deeper understanding of the essence of the
geological object, its genetic characteristics, which is extremely important when
developing a strategy for
prospecting and exploration of mineral deposits.
The results of 178 testing of exploratory wells drilled in the
Urtarabad and
Sarytash fields
were taken as preliminary data for
factor analysis
. The analysis
of the testing results was carried out in the context of assessing the optimality of
the values of its intervals (geological-field task) and the possibility of using
formal mathematical and statistical methods for processing actual data for these
purposes (methodological task).
The object of research is carbonate
deposits of Jurassic age
. Reservoir
rocks are pelitomorphic, fractured limestone, lump-clastic, algal-detrital and
sulfitized. Types of reservoirs:
unevenly porous, fractured, slightly cavernous.
During the analysis, only qualitative indicators of testing were taken into
account. The following results were calculated at intervals every 10 m: -
hydrocarbons - oil, gas, condensate
(HC)
;
water
;
a mixture of water and
hydrocarbons (hereinafter referred to as a
mixture
);
drilling fluid filtrate and
testing without inflow
(
dry
).
For the eliminating the influence of the number of tests on the results of
statistical processing, all data were converted to fractions of units. The
generated test results are shown in Table 1
Analysis of a sample of the results of testing Upper Jurassic carbonates was
carried out on the basis of factor analysis (principal component method).
The main goals of factor analysis are:
1) reduction of the number of data (data reduction);
ACADEMIC RESEARCH IN MODERN SCIENCE
International scientific-online conference
75
0-10.
5
0.5
0
0
2
0.2
3
0.3
10
10-20.
5
0.55555556
0
0
1
0.111111
3
0.333333
9
20.-30.
4
0.36363636
2
0.18181818
2
0.181818
3
0.272727
11
30-40.
5
0.5
2
0.2
2
0.2
1
0.1
10
40-50.
6
0.42857143
2
0.14285714
3
0.214286
3
0.214286
14
50-60.
6
0.6
1
0.1
1
0.1
2
0.2
10
60-70.
4
0.44444444
2
0.22222222
1
0.111111
2
0.222222
9
70-80.
5
0.45454545
2
0.18181818
2
0.181818
2
0.181818
11
80-90.
5
0.45454545
2
0.18181818
2
0.181818
2
0.181818
11
90-100.
7
0.63636364
2
0.18181818
1
0.090909
1
0.090909
11
100-110.
3
0.5
1
0.16666667
1
0.166667
1
0.166667
6
110-120.
3
0.6
1
0.2
0
0
1
0.2
5
120-130.
3
0.5
1
0.16666667
0
0
2
0.333333
6
130-140.
2
0.33333333
2
0.33333333
0
0
2
0.333333
6
140-150.
1
0.33333333
1
0.33333333
0
0
1
0.333333
3
Interval
s, m
Sampling results amount. (th.)/ proportion of units
Total
amount of
tests
HC
WATER
MIX
DRY
2) determining the structure of correlation between data, i.e. classification.
Accordingly, factor analysis is used either as a data reduction method or as a
classification method.
For the initial data (see Table 1), load factors were calculated (Table 2),
which can be interpreted as correlation coefficients between factors and data.
Based on the results of testing productive horizons, a graph was constructed
based on Table 1. (Figure 1)
Table 1
Testing results of Jurassic carbonates
Table 2
Load factors
Testing results FACTOR 1 FACTOR 2
HC
-
0,97264165 0,01086574
WATER
0,70811143 0,21150759
MIX
0,15349558
-
0,64216077
DRY
0,61361303 0,03329669
ACADEMIC RESEARCH IN MODERN SCIENCE
International scientific-online conference
76
Figure 1- Interval results of testing productive horizons
Factor 1 has high loadings for three testing results: “
HC”, “water” and
“dry”.
Factor 2 has a high loading for the result -
“mixture”
. Based on this, factor
1 is identified as testing with a reliable result, and factor 2 as testing with the
production of a hydrocarbon mixture. The dispersion plot of factor loadings (Fig.
2) clearly shows the separation of the test result
“mixture”
from
“HC”
,
“dry”
and “water”. “Mixture”
is the influx of different fluids from the reservoir layers
of the testing interval.
Figure 2-Dispersion diagram of factor loadings
This is explained by the fact that the testing interval may have included
several layers with different fluids. Based on this, it is not possible to
unambiguously determine the nature of saturation of reservoir layers in the
testing interval. Consequently, testing with a result obtained in the form of a
mixture of fluids should be considered apocryphal. The diagram separated “
HC
”
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Pro
p
o
rtio
n
o
n
u
n
its
Testing intervals,m
HC
WATER
MIX
DRY
y = 0,0614x - 0,1043
-0,7
-0,6
-0,5
-0,4
-0,3
-0,2
-0,1
0
0,1
0,2
0,3
-1,5
-1
-0,5
0
0,5
1
fact
o
r
2
factor 1
Diagram of dispersion
Factor=0,0614х-0,1043
DRY
WATER
MIX
HB
Factor 1
ACADEMIC RESEARCH IN MODERN SCIENCE
International scientific-online conference
77
-0,2
-0,1
0
0,1
0,2
0,3
0,4
0,5
0,6
Val
u
e
o
f fact
o
rs
.
Intervals, m
Factor 2
from “
dry
” and “
water
”. That is, the higher the “
HC
” result indicator, the less
Dry” and “
water” and vice versa.
F
igure 3-Diagram of factor values for testing intervals
The diagram of the values of factor 1 (Fig. 3) shows that with an increase in
the testing interval, the probability of obtaining a “
HC
”
increases
. And after
reaching a depth of 90-100 m it decreases. The increase in the probability of
receiving “
HC
” with increasing interval is explained by the transition from the
XV
horizon to the
XV-a horizon
; the maximum number of hydrocarbon inflows
occurs in the interval of
90-100 m
.
The productive testing interval
(up to 90-100 m)
is characterized by the
largest number of testings. Based on the maximum values of
factor 1 up to 0.488
for
“
dry
” and “
water
”, and the increase in the “
HC
” results, it can be assumed
that the choice of an interval of up to
90-100 m
or more for testing is the most
optimal.
In general, this analysis allows us to conclude that the choice of testing
intervals for Jurassic deposits in prospecting and exploration wells is quite
correct for the selected part of the region.
Before analyzing the results of testing of prospecting and exploration wells, two
tasks were set - geological-field and methodological. The methodological
problem has been solved. Mathematical-statistical methods have good prospects
in this area of research.
ACADEMIC RESEARCH IN MODERN SCIENCE
International scientific-online conference
78
Literature:
1. D. Lawley, A. Maxwell Factor analysis as a statistical method // M.: Publishing
House Mir, 1967, 144 p.
2. M. D. Belonin, V. A. Golubeva, G. T. Skublov Factor analysis in geology // M.:
Nedra, 1982, 269 p.
3. J. Kim, C.W. Mueller, W.R. Clark Factor, discriminant and cluster analysis // M.:
Finance and Statistics, 1989, 215 p.
