American Oil and Gas Reporter - August 2017 - 75

FIGURE 3
Fuzzy Pattern Recognition
(Number of Stages, Proppant/Foot, Net Thickness and Lateral Length
Versus 30-Day Cumulative Production)
20,000

Number of Stages

Normalized 30-days Cum. (boe)

19,000

Proppant per Foot

18,000
17,000
16,000
15,000
14,000
13,000
12,000
11,000
10,000
21,000

4

20,000

6

8

10
# of Stages

12

14

16

200

400

600

800

1,000
1,200 1,400
Proppant (lbs/ft)

1,600

3,500
4,000
Length (ft)

4,500

1,800

2,000

2,200

Lateral Length

Net Thickness

19,000
Normalized 30-days Cum. (boe)

parameters. The plots on the left of Figure
2 show the discovered patterns when the
wells were divided into three classes
(poor, average and good). The average
number of frac stages for all 140 wells in
the analysis was nine. But the analysis
showed that the poor wells were completed
with an average of 8.5 stages, while the
average and good wells were completed
with 9.6 and 11 stages on average, respectively (top left). This clear trend in
the data was revealed using this simple
intuitive AI-based classification technique.
As shown in the plot second from the
top left, while the average proppant
pumped per foot of lateral length for all
wells was 1,530 pounds, the poor wells
were completed with an average of 1,440
pounds and the average and good wells
were completed with 1,610 and 1,700
pounds on average, respectively. Similar
trends easily can be observed for net
thickness (second from bottom), and
lateral length (bottom).
In AI-based analysis, the concept of
granularity refers to analyses that are
performed in steps as the number of
fuzzy classifications increases. In this
case, the Marcellus wells initially were
divided into the three classes of poor,
average and good. The granularity of the
classification was increased from three
to four (adding a "very good" classification) and then to five (adding an "excellent" classification).
WQA was repeated with each increase
in granularity to see whether the observed
trends would hold. If so, it would indicate
the dominance of these parameters in determining the 30-day cumulative production from this particular asset. Incidentally,
this process can be repeated for longer
production periods to obtain a better understanding how different parameters impact well productivity.
The middle plots in Figure 2 represent
the WQA performed using four classes
of wells and the plots on the right represent the WQA performed using five
classes of wells. The dominance of the
parameters is clear, with the general
trend and patterns remaining the same
as granularity increases.
By increasing the number of classes
to the maximum possible granularity and
integrating them with similar classifications performed on each parameter, the
resulting trends or patterns can be demonstrated in the form of a dotted line (Figure
3) to extract hidden patterns from the
data using fuzzy set theory. When every
measured parameter has been analyzed,
the behavior of the trends can be studied
based on the slope of the lines (demonstrating the impact of each parameter on
the production index).

18,000
17,000
16,000
15,000
14,000
13,000
12,000
11,000
10,000

100

110

120

130
Thickness (ft)

140

150

160

2,000

2,500

3,000

5,000

5,500

FIGURE 4
Key Performance Indicators Influence on 30-Day Cumulative Production

Rank

Feature

% Degree of Influence

1

Net Thickness (ft)

100

2

# of Stages

41

3
4

Easting-End Point
Clusters per Stage

40
39

5

Proppant (lbs/ft)

36

6
7

Lateral Length (ft)
Shot Density (shots/ft)

29
26

8

Max-Prop Conc (lb/gal)

25

9

MD (End)
Treatment Pressure (psi)

24
23

Average TVD

21

Treatment Rate (bbl/m)

21

Clean Vol (bbl/ft)

16

10
11
12
13
14
15

Nothing-End Point

15

16

Langmuir Vol. (bbl/ft)
Azimuth (End)

10
10

17

Porosity %

9

18

Langmuir Pressure (psi)

6

19
20

Slurry Vol (bbls/ft)
TOC %

6
6

21
22

Water Sauration %
Deviation (End)

5
4

23

Inclination (End)

1
AUGUST 2017 75



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