A sample paper for presentation at ANZIIS 2001


Copyright © the author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. 

DOI: 10.14800/IOGR.454  

Received September 10, 2019; revised November 12, 2019; accepted December 4, 2019. 

*Corresponding author: hel_ca@petrochina.com                                                                                                                                                                             1                                                                                                                                                                 

 A New Method For Estimating The Peak 
Gas Rate  

 

Lei He*, Yilin He, Liangrong You, Feng Li, and Fang Zhang, Research Institute of Exploration 
and Development, Changqing Oilfield Company, PetroChina, Beijing, China 

 

 

Abstract 

Every winter from November of the first year to March of the next year, gas consumption would roar. As 

a result, all gas fields will increase gas rate to meet the needs. Traditionally, peak rate and rational rate are 

usually determined by methods such as pressure drop rate, Rate Transient Analysis (RTA), and numerical 

simulation. These approaches have been verified in the production, but they show flaws like heavy 

workload and poor time effectiveness. Theoretical research indicates that a plot of the increment of gas rate 

and the pressure drop rate should be linear, when pseudo-steady state is reached. In consequence, based on 

numerical simulation, correlations of the increment of gas rate and pressure drop rate were developed 

respectively for a series of reservoir pressure and well types. Field applications show that this method can 

not only process a huge batch of data in one time, but quickly estimate the rational and peak gas rate in real 

time. 

Introduction 

Changqing oilfield company provides fuel for more than 13 provinces and so it enjoys the reputation of the 

natural gas base in China. As a major block in Changqing, it is vital to predict the rate during the peak gas 

supply period. However, after ten years of production since 1997, ever-decreasing pressure has caused great 

difficulties in maintaining plateau and meeting the needs of consumption in winter. Conventional gas rate 

predicting methods include pressure drop rate (Luo et al. 2008), rate transient analysis (Blasingame et al. 

1991; Oghena 2012) and numerical simulation (Li et al. 2004). But they show flaws like heavy workload 

and poor time effectiveness. Therefore, this study aimed at establishing a quick and accurate way to predict 

peak gas rate. 

Peak Gas Rate Evaluation 

Flow Performance in Pseudo Steady State. Theoretical research indicates that if a surrounded well 

produced at a constant flow rate, every cell pressure will decline with a constant rate when the effect of the 

outer boundary has been felt (Figure 1). And this period of time is called the pseudo steady state (PSS) 

which develops with a linear relationship of gas rate and reservoir pressure. Field applications show that 

type I wells in Jingbian gas field need 0.5 year to reach the SSS, type II wells 1 year and type II wells 2.5 

years. With ten years of producing history, most wells in the study area has reached the SSS and so the 

relationship of gas rate and reservoir pressure can be widely established. 

 

mailto:hel_ca@petrochina.com


2 

 

Figure 1—Pressure distribution under pseudo steady state. 

 

Correlation of Casing Pressure and Reservoir Pressure. Reservoir pressure is hard to get, but the casing 

pressure can be monitored with real time. As a result, this article developed a quick way to estimate 

reservoir pressure by casing pressure. 

    First as shown in Eq. 1, applying Cullender-Smith method (Wang et al. 2011), bottom-hole-pressure can 

be obtained 

∫
𝑝

𝑍𝑇

(
𝑝

𝑍𝑇
)
2
+
1.324×10−18𝑓𝑞𝑠𝑐

2

𝑑5

𝑝𝑤𝑓

𝑝𝑡𝑓

𝑑𝑝 = ∫ 0.03415𝛾𝑔𝑑ℎ
ℎ

0
...……………….……………………..…………….(1) 

Then with the bottom-hole-pressure, reservoir pressure could be abstained by solving the gas well inflow 

Eq. 2 (Wang et al. 2014) 

𝑃𝑅
2 − 𝑃𝑤𝑓

2 = 𝐴𝑞 + 𝐵𝑞2.…….…………………………………………………………………………..(2) 

Based on the above methods, reservoir pressures were calculated as shown in Figure 2, which indicates 

the reservoir pressure and casing pressure yield a linear relationship. Compared with real reservoir pressure, 

the average errors of this method is only about 3% (Figure 3). 

 

 

Figure 2—Relationship of Casing Pressure Reservoir Pressure. 

 

15

20

25

30

35

40

0 5 10 15 20 25 30

P
e（

M
P

a
）

Pwh（MPa）

Pe linear



3 

 

Figure 3—Errors of the Calculating Method. 

 

Graphical Correlation of Pressure Drop Rate and Increment of Gas Rate. Base on the theoretical 

research and numerical simulation analysis, correlations of pressure drop rate as a function of increment of 

gas rate and reservoir pressure were established for three types of wells, respectively (Figures 4 through 

6). 

 

 

Figure 4—Correlation of pressure drop rate and increment of gas rate of type I well. 

 

 

Figure 5—Correlation of pressure drop rate and increment of gas rate of type II well. 

0 

1 

2 

3 

4 

5 

6 

0 5 10 15 20 25 30

E
r
r
o

r
s（

%
）

Pwh（MPa）

0.010

0.015

0.020

0.025

0.030

0.035

1 1.2 1.4 1.6 1.8 2

P
re

ss
u
re

 D
ro

p
 R

at
e 

(M
p
a/

d
)

Magnification of Gas Rate

27 Mpa 24 Mpa
21 Mpa 18 Mpa
15 Mpa 12 Mpa

0.005

0.010

0.015

0.020

0.025

0.030

1 1.2 1.4 1.6 1.8 2

P
re

ss
u

re
 D

ro
p

 R
at

e 
(M

p
a/

d
)

Magnification of Gas Rate

27 Mpa 24 Mpa
21 Mpa 18 Mpa
15 Mpa 12 Mpa



4 

 

Figure 6—Correlation of pressure drop rate and increment of gas rate of type III well. 

 

    After determining the reservoir pressure, these graphic correlations can be used for peak gas rate 

evaluation quickly and massively. But Fig. 6 shows that when reservoir pressure is less than 21 MPa, even 

tiny rate increase will cause huge casing pressure drop, then these wells are not qualified for peak gas 

production. 

Field Application 

In order to verify the graphic correlations, 22 wells in Jingbian gas field were put into calculations and 

results show that errors of this method is only about 10%. 

    Well X-1 was selected for peak gas production on Oct 12th, 2016. As shown in Figure 7, with the 

reservoir pressure of 17.2 MPa, when gas rate increase 1.3 times, from 12×104m3/d to 15.3×104m3/d, 

casing pressure drop rate increases to 0.0188 MPa/d. And the graphic correlation (Figure 8) indicates the 

rate increase corresponds to a pressure drop rate of 0.0179 MPa/d, which yield errors of 4.8%. 

Well X-2 was selected for peak gas production on Oct 6th, 2016. As shown in Figure 9, with the reservoir 

pressure of 15.1 MPa, when gas rate increase 1.3 times, from 2.2×104m3/d to 2.8×104m3/d, casing 

pressure drop rate increase to 0.0113 MPa/d. The graphic correlation (Figure 10) indicates the rate increase 

corresponds to a pressure drop rate of 0.0120 MPa/d, which yield errors of 5.8%. 

 

 

Figure 7—Well X-1 production history (I). 

 

 

0.000

0.003

0.006

0.009

0.012

0.015

1 1.2 1.4 1.6 1.8

P
re

ss
u

re
 D

ro
p

 R
at

e 
(M

p
a/

d
)

Magnification of Gas Rate

30 Mpa 27 Mpa

24 Mpa 21 Mpa

0

4

8

12

16

20

2015/12/1 2016/3/1 2016/6/1 2016/9/1 2016/12/1

g
a

s 
ra

te
(1

0
4
m

3
)

0

3

6

9

12

15

18

p
re

ss
u

re
（

M
P

a）

gas rate tubing pressure casing pressure

0.0188MPa/d

Magnification of ×1.3



5 

 

Figure 8—Well X-1 gas rate evaluation chart (I). 

 

 

Figure 9—Well X-2 production history (II). 

 

 

Figure 10—Well X-2 gas rate evaluation chart (II). 

Conclusions 

Theoretical research shows that when pseudo steady state is reached, gas rate and pressure drop rate yield 

a linear relationship. Base on the theoretical research and numerical simulation analysis, correlations of 

pressure drop rate as a function of increment of gas rate and reservoir pressure were established for three 

0.010

0.015

0.020

0.025

0.030

0.035

1 1.2 1.4 1.6 1.8 2

P
re

ss
u

re
 D

ro
p

 R
at

e 
(M

p
a/

d
)

Magnification of Gas Rate

27 Mpa 24 Mpa
21 Mpa 18 Mpa
15 Mpa 12 Mpa

0.0179 MPa/d
Errors of 4.8%

0

1

2

3

4

2015/1/1 2015/6/1 2015/11/1 2016/4/1 2016/9/1

g
a

s 
ra

te
(1

0
4
m

3
)

0

5

10

15

20

25

p
re

ss
u

re
(M

p
a

)

gas rate casing pressure tubing pressure

0.0113MPa/d

Magnification of ×1.25

0.005

0.010

0.015

0.020

0.025

0.030

1 1.2 1.4 1.6 1.8 2

P
re

ss
u

re
 D

ro
p
 R

at
e 

(M
p
a/

d
)

Magnification of Gas Rate

27 Mpa 24 Mpa
21 Mpa 18 Mpa
15 Mpa 12 Mpa

0.012 MPa/d 

Errors of 5.8% 



6 

types of wells, respectively.  These graphic correlations not only can process a huge batch of data in one 

time, but quickly estimate the peak gas rate in real time. Field applications show that the errors of this 

method are less than 10%. 

Conflicts of Interest 

The author(s) declare that they have no conflicting interests. 

References 

Blasingame, T. A., Mccray, T. L., and Lee, W. J. 1991. Decline Curve Analysis for Variable Pressure Drop/Variable 

Flow Rate Systems. Paper SPE-21513-MS presented at SPE Gas Technology Symposium, Houston, USA, 22-24 

January.  

Li, Y., Chen, J., Zhang, L., et al. 2004. New Model of Numeral Simulation for Development of Gas Reservoirs with 

Low Permeability. Natural Gas Industry 24(8):65-68. 

Luo, W., Tang, H., Wang, X., et al. 2008. Application of Pressure-Drop Method to Sebei-1 Gas Field. Oil & Gas 

Geology 29(1):84-87. 

Oghena, A. 2012. Pressure Drawdown Decline Curve Analysis. Paper SPE-150906-MS presented at North Africa 

Technical Conference and Exhibition, Cairo, Egypt, 20-22 February.  

Wang, L., Peng, C., Ni, X., et al. 2011. Optimal for Calculating Method About Bottom-Hole Pressure of Gas Well. 

Well Testing 20(4):25-26. 

Wang, C., Li, Z., and Lai, F. 2014. A Novel Binomial Deliverability Equation for Fractured Gas Well Considering 

Non-Darcy Effects. Journal of Natural Gas Science and Engineering 20(9):27-37. 

 
He Lei is a senior engineer engaged in Research Institute of Exploration and Development, Changqing 
oilfield company, PetroChina. He mainly focuses on dynamic monitoring of gas filed and numerical 
simulation. 
 

Yilin He is a senior engineer engaged in Research Institute of Exploration and Development, Changqing 
oilfield company, PetroChina. Yilin He specializes in production analysis and numerical simulation of 
unconventional gas reservoirs. 

 

Liangrong You is a senior engineer of Research Institute of Exploration and Development, Changqing 
oilfield company, PetroChina. Liangrong You specializes in dynamic monitoring of unconventional gas 
reservoirs. 

 

Feng Li is a senior engineer engaged in Research Institute of Exploration and Development, Changqing 
oilfield company, PetroChina. Feng Li mainly focuses on dynamic monitoring of gas filed and numerical 
simulation. 

 

Fang Zhang is a senior engineer engaged in Research Institute of Exploration and Development, 
Changqing oilfield company, PetroChina. Fang Zhang mainly focuses on dynamic monitoring of gas filed 
and production analysis of tight gas reservoirs.