Engineering, 2013, 5, 877-880
Published Online November 2013 (http://www.scirp.org/journal/eng)
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.
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
The oil and gas field companies mostly take the man-
agement concept of “Benchmarking” during the process
of gas field development . 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
. 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 .
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
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.
Figure 1. Hierarchical relationship of the evaluation indicators.
(1) Completion rate of gas production.
110 100%Cvv ,
where is the actual gas production, is the planned
gas output, unit: m3.
(2) Completion rate of inhibiter injection.
120 100%Cqq ,
where is the actual amount of filling, is the
planned amount of filling, unit: “tons”.
(3) Completion rate of measures.
where is the actual number of measures, is the
arranged number of measures, unit: “times”.
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.
(2) Time utilization rate of gas production.
where is the actual time of gas production, is the
calendar time of gas productio n, unit: day.
(3) Qualified rate of single well production allocation.
where is the number of qualified wells, is the
actual number of open wells.
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
(1) Non-normal shut frequency of single well.
where is the number of non-normal shut, is the
actual number of open wells, unit: times per one well.
(2) Qualified rate of dew point of trunk line.
where is the qualified number of monitored dew
points, is the total number of monitored dew points,
Open Access ENG
J. H. GOU ET AL. 879
(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.
(4) Consumption rate of me t han ol o f gas wells.
where is the actual consumptio n of methanol, is
the total annual b udget, unit: tons.
(5) Consumption rate of triethylene glycol of gas sta-
35 100%CVv ,
where is the consumption of triethylene glycol, unit:
kg; is the gas production, unit: m3.
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.
Step 1: Standardize the decision data matrix. The stan-
dardized decision data matrix is denoted by
and the transformation formula are given in the follow-
(a) When the jth indicator is the benefit type,
(b) When the jth indicator is the cost type,
(c) When the jth is the targe t type,
Step 2: Determine the weights of indicators. The
can be obtained by
ANP. Further more, we could calculate the weighted
decision matrix , where .
Step 3: Determine the positive ideal vector and the
negative ideal vector. Respectively, denoted by
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
The Euclidean distance between the i-th RMU and th e
negative ideal vector is denoted by
Step 5: Calculate the relative closeness to the positive
ideal vector. The relative closeness can be defined as
Step 6: Decide the ranking according to the value of
. The bigger the closeness shows the better the rank-
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
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.
0.05395; 0.03732; 0.01015;
Step 3: Production performance evaluation for GPUs.
Figure 2. The network structure of the evaluation indica-
Open Access ENG
J. H. GOU ET AL.
Open Access ENG
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
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
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.
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