Engineering, 2013, 5, 877-880
Published Online November 2013 (http://www.scirp.org/journal/eng)
http://dx.doi.org/10.4236/eng.2013.511107
Open Access ENG
Performance Evaluation for Gas Production
Units Based on ANP
Jianhong Gou1,2, Anqi Li3, Zhibin Liu1, Xinhai Kong4, Haohan Liu1,5*
1School of Graduate, Southwest Petroleum University, Chengdu, China
2The No. 1 Gas Production Plant, PetroChina Changqing Oilfield Company, Yinchuan, China
3PetroChina Changqing Oilfield Company, Xi’an, China
4Department of Petroleum Engineering, Guang’an Vocational & Technical College, Guang’an, China
5Sichuan College of Architectural Technology, Deyang, China,
Received August 25, 2013; revised September 25, 2013; accepted October 5, 2013
Copyright © 2013 Jianhong Gou et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
In view of the existing situation of gas field development, one kind of method to evaluate the production performance
of gas production units (GPUs) was presented in this paper. Among the commonly used indicators of gas field devel-
opment, we select 11 indicators from the three aspects of production task, gas reservoir management, and production
technology. According to the principle of analytic network process (ANP), this paper introduced one kind of new
method to get the weights of indicators. By means of the method of TOPSIS, it is easy to obtain the rankings for all the
GPUs through calculating the weighted Eu clidean distance between each GPU and the positive or negative ideal point.
This evaluation method could constantly improve the management level of gas production units and deepen the delicacy
management of gas field deve lopment .
Keywords: Gas Production Unit (GPU); Performance Evaluation; Analytic Network Process (ANP); TOPSIS
1. Introduction
The oil and gas field companies mostly take the man-
agement concept of “Benchmarking” during the process
of gas field development [1]. According to the dynamic
analysis of gas reservoir development, the technical sec-
tion provides a kind of development scheme and sets
some feasible goals that should be achieved. And the gas
production units (GPUs) must achieve the production
goals in accordance with the development requirements
[2]. Currently, the development department in the proc-
ess of gas field development evaluates the production
performance of GPUs based on their own statistics data
and the assessment results calculated by themselves [3].
It means that the evaluation accuracy is not high enough
and the crosswise contrast is not enough. In order to
make the development department accurate and timely,
and grasp the current situation of development and man-
agement of GPUs, it needs to establish a relatively per-
fect evaluation system to really respond to the manage-
ment level, efficiency, and development effect of GPUs,
promoting the delicacy management of gas field develop-
ment. In this paper, we first present the evaluation indi-
cators and their computing methods. In order to reasona-
bly decide the weight of each indicator, this method of
ANP is introduced. Next, we introduce the method of
TOPSIS to decide the comprehensive ranking of GPUs
and use the Euclidean distance to describe the proximity
between two GPUs.
2. Use Evaluation Indicators and Their
Computing Methods
Through the analysis, the production performance eva-
luation indicators of GPU are divided into three aspects
of production task, gas reservoir management, and pro-
duction technology [1-5] (see Figure 1), including 11
indicators in the following.
2.1. Production Task
The production task [4,5], denoted as B1, contains the
completion rate of gas production (C11) , the completion
ate of water injection (C12), and the completion rate of r
*Corresponding a uthor.

J. H. GOU ET AL. 878
Figure 1. Hierarchical relationship of the evaluation indicators.
Measures (C13),.
(1) Completion rate of gas production.
110 100%Cvv ,
where is the actual gas production, is the planned
gas output, unit: m3.
v0 v
(2) Completion rate of inhibiter injection.
120 100%Cqq ,
where is the actual amount of filling, is the
planned amount of filling, unit: “tons”.
q0 q
(3) Completion rate of measures.
130 100%CNN,
where is the actual number of measures, is the
arranged number of measures, unit: “times”.
N0 N
2.2. Gas Reservoir Management
The gas reservoir development [6,7], denoted as B2, con-
tains the utilization rate of gas well (C21), the time utili-
zation rate of gas production (C22) and the qualified rate
of single well production allocatio n (C23).
(1) Utilization rate of gas well.
210 100%Cnn ,
where is the actual number of open wells, is the
total number of gas wells.
n0 n
(2) Time utilization rate of gas production.
220 100%Ctt

,
where is the actual time of gas production, is the
calendar time of gas productio n, unit: day.
t0 t
(3) Qualified rate of single well production allocation.
23 100%Cmn

,
where is the number of qualified wells, is the
actual number of open wells.
m n
2.3. Production Technology
The gas production technology [8-12], denoted as B3,
contains the non-normal shut frequency of single well
(C31), the qualified rate of dew point of trunk line (C32),
the utilization rate of working time of potentiostat (C33),
the consumption rate of methanol of gas wells (C34) and
the consumption rate of triethylene glycol of gas stations
(C35).
(1) Non-normal shut frequency of single well.
31 100%CLn

,
where is the number of non-normal shut, is the
actual number of open wells, unit: times per one well.
Ln
(2) Qualified rate of dew point of trunk line.
320 100%Ccc

,
where is the qualified number of monitored dew
points, is the total number of monitored dew points,
c
0
c
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J. H. GOU ET AL. 879
unit: times.
(3) Utilization rate of working time of potentiostat.
330 100%CTT ,
where is the actual working time of potentiostat,
is the calendar working time of potentiostat, unit: hour.
T0 T
(4) Consumption rate of me t han ol o f gas wells.
340 100%CQQ,
where is the actual consumptio n of methanol, is
the total annual b udget, unit: tons.
Q0 Q
(5) Consumption rate of triethylene glycol of gas sta-
tions.
35 100%CVv ,
where is the consumption of triethylene glycol, unit:
kg; is the gas production, unit: m3.
V
v
3. Production Performance Evaluation for
Gas Reservoir Management Units
Assume that there are GPUs and evaluation in-
dicators, the decision data matrix is denote d by
mn. According to the method of TOPSIS, the
comprehensive ranking procedure for GPUs consists of
the following steps.
m n

ij
Xx
Step 1: Standardize the decision data matrix. The stan-
dardized decision data matrix is denoted by


ij
Yy
mn

,
and the transformation formula are given in the follow-
ing.
(a) When the jth indicator is the benefit type,

 
min
max min
ij ij
i
ij
ij ij
i i
xx
y
x
x


(b) When the jth indicator is the cost type,

 
max
max min
ij ij
i
ij
ij ij
i i
xx
y
x
x


(c) When the jth is the targe t type,
 

0
00
1max max,min
ij
ij
ij ij
i i
xx
y
x
xx x



Step 2: Determine the weights of indicators. The
weight vector

12
,,,
n


  

ij
Zz
can be obtained by
ANP. Further more, we could calculate the weighted
decision matrix , where .
mn ijj ij
Step 3: Determine the positive ideal vector and the
negative ideal vector. Respectively, denoted by
and
zwy

12
,,,
n
zzzz



12
,,,
n
zzzz

 
, where

max
j
ij
zz
i and


min
j
ij
i
Step 4: Calculate the Euclidean distance from the
positive ideal vector and the negative ideal vector. The
Euclidean distance between the i-th RMU and the posi-
tive ideal vector is denoted by
zz
.

2
1
n
iik
k
dzz



k
The Euclidean distance between the i-th RMU and th e
negative ideal vector is denoted by

2
1
n
iik
k
dzz



k
Step 5: Calculate the relative closeness to the positive
ideal vector. The relative closeness can be defined as


*,1,2,,
iiii
f
dddi m


Step 6: Decide the ranking according to the value of
*
i
f
. The bigger the closeness shows the better the rank-
ing.
4. Example Analysis
The statistical data of 7 gas production units (GPUs) of
an oilfield in the year of 2012 are listed in Table 1. Ac-
cording to the basic data in Table 1 , we could obtain the
evaluation results.
Setp 1: Build the network structure of the evaluation
indicators (see Figure 2).
Setp 2: Calculation the weights of the evaluation indi-
cators. All judgment matrixes are as follows:
All calculations are done by the Super Decisions soft-
ware. From the limit matrix, we can obtain the weights of
all the evaluation indicators listed in the following.
11 1213
21 2223
31 3233
34 35
0.44173; 0.11055;0.26540;
0.05395; 0.03732; 0.01015;
0.03642; 0.00507;0.00716;
0.02944;0.00282
ccc
ccc
ccc
cc





Step 3: Production performance evaluation for GPUs.
Figure 2. The network structure of the evaluation indica-
ors. t
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J. H. GOU ET AL.
Open Access ENG
880
Table 1. The statistical data of 7 GPUs in 2012.
GPUs C11 C12 C13 C21 C22 C23 C31 C32 C33 C34 C35
1 0.92 0.929 0.98 0.989 0.87 0.885 0.239 0.92 0.95 1.1038 0.36
2 0.93 0.786 0.97 0.964 0.88 0.878 0.196 0.95 0.98 1.0383 0.22
3 0.99 0.667 0.98 0.977 0.86 0.986 0.345 0.90 0.99 1.0451 0.32
4 0.92 0.857 0.99 0.912 0.75 0.851 0.126 0.88 0.97 1.0369 0.35
5 0.93 1.000 0.97 0.879 0.64 0.975 0.360 0.98 0.96 0.9844 0.34
6 0.89 1.000 0.97 0.968 0.70 0.906 0.016 0.96 0.98 0.9732 0.47
7 0.78 0.793 0.95 0.892 0.82 0.824 0.514 0.85 0.94 1.0184 0.22
5. Conclusions
1. A relatively perfect evaluation method is established
to really respond to the management level, efficiency,
and development effect of GPUs, which can promote the
delicacy management of gas field development.
2. Some practically feasible evaluation indicators and
their computing methods are firstly presented through
analyzing the actual situation in the process of gas field
development.
3. We can decide the comprehensive rankings of GPUs
through calculating the weighted Euclidean distance be-
tween every GPU and the positive or negative ideal
RMU by means of the method of TOPSIS.
4. A practical example is illustrated to explain the fea-
sibility of this method.
REFERENCES
[1] J. Y. Liu, “Geological Foundation of Oil and Gas Field
Development,” Petroleum Industry Press, Beijing, 2006.
[2] Y. C. Li, “The Technology of Petroleum Production,”
2nd Edition, Petroleum Industry Press, Beijing, 2009.
[3] P. T. Geoffrey, “Oil and Gas: A Practical Handbook,”
Globe Law and Business, London, 2009.
[4] N. Ezekwe, “Petroleum Reservoir Engineering Practice,”
Prentice Hall, Upper Saddle River, 2010.
[5] C. L. Li, “Fundamentals of Reservoir Engineering (Sec-
ond edition)”, Petroleum Industry Press, Beijing, 2011.
[6] K. Joshi and R. Kant, “Decision Making in Effective
Supply Chain Collaboration: A Fuzzy AHP Approach,”
International Journal of Decision Sciences, Risk and
Management, Vol. 4, No. 3-4, 2012, pp. 197-261.
[7] H.-W. Yang and K.-F. Chang, “Combining Means-End
Chain and Fuzzy ANP to Explore Customers’ Decision
Process in Selecting Bundles,” International Journal of
Information Management, Vol. 32, No. 1, 2012, pp. 381-
395.
[8] M. L. Tseng, Y. H. Lin, A. S. F. Chiu and J. C. H. Liao,
“Using FANP Approach on Selection of Competitive
Priorities Based on Cleaner Production Implementation:
A Case Study in PCB Manufacturer, Taiwan,” Clean
Technologies and Environmental Policy, Vol. 10, No. 1,
2008, pp. 17-29.
http://dx.doi.org/10.1007/s10098-007-0109-4
[9] H.-Y. Kang, A. H. I. Lee and C.-Y. Yang, “A Fuzzy ANP
Model for Supplier Selection as Applied to IC Packag-
ing,” Journal of Intelligent Manufacturing, Vol. 23, No. 1,
2012, pp. 1477-1488.
[10] F. Kong and H. Y. Liu, “Applying Fuzzy Analytic Hier-
archy Process to Evaluate Success Factors of E-Com-
merce,” International Journal of Information and Systems
Sciences, Vol. 1, No. 3-4, 2005, pp. 406-412.
[11] H. C. Sun, G. Y. Xu and P. Tian, “Design Alternatives
Evaluation of Emergency Bridge by Applying Analytic
Network Process,” Systems Engineering-Theory & Prac-
tice, Vol. 27, No. 3, 2007, pp. 63-70.
http://dx.doi.org/10.1016/S1874-8651(08)60025-3
[12] T. L. Saaty and L. G. Vargas, “Decision Making with the
Analytic Network Process,” Springer, 2010.