Abstract

It is complex and obviously different for the production characteristics of CO2 water-alternating-gas (WAG) flooding in tight reservoir and influenced by quite a few factors. Therefore, the prediction of oil production is a key matter of efficient development of CO2 WAG to be solved in tight reservoirs. In order to cope with this issue, in this paper, the production characteristics of CO2 WAG flooding are analyzed and classified in tight oil reservoir of block A as an example. On this basis, properties of reservoir, fracture factors, and operational factors are taken into account and the sensitivity of the influencing factors is carried out. Subsequently, the gray relation analysis is used to confirm the primary influencing factors. Finally, the evaluated model is established to predict oil production rapidly. The results illustrate that the wells of CO2 WAG flooding in tight reservoirs can be divided into four types of fluid production characteristics. The production is affected by permeability, reservoir thickness, amount of sand entering the ground, amount of liquid entering the ground, gas/water ratio, the injection rate, injection pressure, permeability variation coefficient, water sensitive index, acid sensitive index, and expulsion pressure. And the primary influencing factors are the amount of sand entering the ground, reservoir thickness, and amount of liquid entering the ground. The oil production can be predicted quickly based on the relation between production and comprehensive evaluation factor of production. The average relative error between the predicted results and the actual production is 8%, which proves the reliability and accuracy of this method.

References

1.
Jia
,
C.
,
Zheng
,
M.
, and
Zhang
,
Y.
,
2012
, “
Unconventional Petroleum Resources and Exploration and Development Prospects in China
,”
Pet. Explor. Dev.
,
39
(
2
), pp.
129
136
.
2.
Jiao
,
F.
,
2019
, “
Re-recognition of ‘Unconventional’ in Unconventional Oil and Gas
,”
Pet. Explor. Dev.
,
46
(
5
), pp.
847
855
.
3.
Wang
,
N.
,
Zhao
,
Q.
,
Zang
,
H.
,
Guo
,
W.
, and
Liu
,
D.
,
2017
, “
Prospect and Technology of Unconventional Gas Exploration and Development in China
,”
IOP Conference
,
China
, Vol. 59.
4.
Wu
,
S.
,
Zhu
,
R.
,
Yan
,
Z.
,
Mao
,
Z.
,
Cui
,
J.
, and
Zhang
,
X.
,
2019
, “
Distribution and Characteristics of Lacustrine Tight Oil Reservoirs in China
,”
J. Asian Earth Sci.
,
178
, pp.
20
36
.
5.
Zou
,
C.
,
Yang
,
Z.
,
He
,
D.
,
Wei
,
Y.
,
Li
,
J.
,
Jia
,
A.
,
Chen
,
J.
, et al
,
2018
, “
Theory, Technology and Prospects of Conventional and Unconventional Natural Gas
,”
Pet. Explor. Dev.
,
45
(
4
), pp.
604
618
.
6.
Manrique
,
E.
,
Thomas
,
C.
,
Ravikiran
,
R.
,
Izadi
,
M.
,
Lantz
,
M.
,
Romero
,
J.
, and
Alvarad
,
V.
,
2010
, “
EOR: Current Status and Opportunities
,”
SPE Improved Oil Recovery Symposium
,
Tulsa, OK
.
7.
Christensen
,
J. R.
,
Stenby
,
E. H.
, and
Skauge
,
A.
,
2001
, “
Review of WAG Field Experience
,”
SPE Reservoir Eval. Eng.
,
4
(
2
), pp.
97
106
.
8.
Hoffman
,
B. T.
, and
Shoaib
,
S.
,
2014
, “
CO2 Flooding to Increase Recovery for Unconventional Liquids-Rich Reservoirs
,”
ASME J. Energy Resour. Technol.
,
136
(
2
), p.
022801
.
9.
Ren
,
X.
,
Li
,
A.
,
Memon
,
A.
,
Fu
,
S.
,
Wang
,
G.
, and
He
,
B.
,
2019
, “
Experimental Simulation on Imbibition of the Residual Fracturing Fluid in Tight Sandstone Reservoirs
,”
ASME J. Energy Resour. Technol.
,
141
(
8
), p.
082906
.
10.
Cao
,
C.
,
Song
,
Z.
,
Su
,
S.
,
Tang
,
Z.
,
Xie
,
Z.
,
Chang
,
X.
, and
Shen
,
P.
,
2021
, “
Water-Based Nanofluid-Alternating-CO2 Injection for Enhancing Heavy Oil Recovery: Considering Oil-Nanofluid Emulsification
,”
J. Pet. Sci. Eng.
,
205
(
2
), p.
108934
.
11.
Gong
,
Y.
, and
Gu
,
Y.
,
2015
, “
Miscible CO2 Simultaneous Water-and-Gas (CO2-SWAG) Injection in the Bakken Formation
,”
Energy Fuels
,
29
(
9
), pp.
5655
5665
.
12.
Han
,
L.
, and
Gu
,
Y.
,
2014
, “
Optimization of Miscible CO2 Water-Alternating-Gas Injection in the Bakken Formation
,”
Energy Fuels
,
28
(
11
), pp.
6811
6819
.
13.
Meng
,
F.
,
Su
,
Y.
,
Wang
,
W.
,
Lei
,
Q.
, and
He
,
D.
,
2020
, “
Semi-Analytical Evaluation for Water-Alternating-CO2 Injectivity in Tight Oil Reservoirs
,”
Int. J. Oil Gas Coal Technol.
,
24
(
1
), pp.
62
84
.
14.
Raziperchikolaee
,
S.
,
Pasumarti
,
A.
, and
Mishra
,
S.
,
2020
, “
The Effect of Natural Fractures on CO2 Storage Performance and Oil Recovery From CO2 and WAG Injection in an Appalachian Basin Reservoir
,”
Greenhouse Gases: Sci. Technol.
,
10
(
5
), pp.
1098
1114
.
15.
Wang
,
L.
,
Wang
,
X.-D.
,
Ding
,
X.-M.
,
Zhang
,
L.
, and
Li
,
C.
,
2012
, “
Rate Decline Curves Analysis of a Vertical Fractured Well With Fracture Face Damage
,”
ASME J. Energy Resour. Technol.
,
134
(
3
), p.
032203
.
16.
Xu
,
X.
,
Saeedi
,
A.
, and
Liu
,
K.
,
2017
, “
Experimental Study on a Novel Foaming Formula for CO2 Foam Flooding
,”
ASME J. Energy Resour. Technol.
,
139
(
2
), p.
022902
.
17.
Yang
,
J.
,
Wang
,
X.
,
Yang
,
Y.
,
Peng
,
X.
, and
Zeng
,
F.
,
2019
, “
An Empirical Model to Estimate Sweep Efficiency of a Surfactant-Alternating-Gas Foam Process in Heterogeneous Reservoirs
,”
ASME J. Energy Resour. Technol.
,
141
(
12
), p.
122902
.
18.
Moritis
,
G.
,
2008
, “
More US EOR Projects Start But EOR Production Continues Decline
,”
Oil Gas J.
,
106
(
15
), pp.
41
45
.
19.
Wang
,
Z.
,
Yang
,
S.
,
Lei
,
H.
,
Yang
,
M.
,
Li
,
L.
, and
Yang
,
S.
,
2017
, “
Oil Recovery Performance and Permeability Reduction Mechanisms in Miscible CO2 Water-Alternative-Gas (WAG) Injection After Continuous CO2 Injection: An Experimental Investigation and Modeling Approach
,”
J. Pet. Sci. Eng.
,
150
, pp.
376
385
.
20.
Zhang
,
J.
,
Zhang
,
H. X.
,
Ma
,
L. Y.
,
Liu
,
Y.
, and
Zhang
,
L.
,
2020
, “
Performance Evaluation and Mechanism With Different CO2 Flooding Modes in Tight Oil Reservoir With Fractures
,”
J. Pet. Sci. Eng.
,
188
, p.
106950
.
21.
Walker
,
J. W.
, and
Turner
,
J. L.
,
1968
, “
Performance of Seeligson Zone 20b-07 Enriched-Gas-Drive Project
,”
J. Pet. Technol.
,
20
(
4
), pp.
369
373
.
22.
Motealleh
,
M.
,
Kharrat
,
R.
, and
Hashemi
,
A.
,
2013
, “
An Experimental Investigation of Water-Alternating-CO2 Core Flooding in a Carbonate Oil Reservoir in Different Initial Core Conditions
,”
Energy Sources
,
35
(
13–16
), pp.
1187
1196
.
23.
Adebayo
,
A. R.
,
Kamal
,
M. S.
, and
Barri
,
A. A.
,
2017
, “
An Experimental Study of Gas Sequestration Efficiency Using Water Alternating Gas and Surfactant Alternating Gas Methods
,”
J. Nat. Gas Sci. Eng.
,
42
, pp.
23
30
.
24.
Yang
,
D.
,
Song
,
C.
,
Zhang
,
J.
,
Zhang
,
G.
,
Ji
,
Y.
, and
Gao
,
J.
,
2015
, “
Performance Evaluation of Injectivity for Water-Alternating-CO2 Processes in Tight Oil Formations
,”
Fuel
,
139
, pp.
292
300
.
25.
Zheng
,
S.
, and
Yang
,
D.
,
2013
, “
Pressure Maintenance and Improving Oil Recovery in Terms of Immiscible Water-Alternating-CO2 Processes in Thin Heavy Oil Reservoirs
,”
SPE Reservoir Eval. Eng.
,
16
(
1
), pp.
60
71
.
26.
Song
,
Z.
,
Li
,
Z.
,
Wei
,
M.
,
Lai
,
F.
, and
Bai
,
B.
,
2014
, “
Sensitivity Analysis of Water-Alternating-CO2 Flooding for Enhanced Oil Recovery in High Water Cut Oil Reservoirs
,”
Comput. Fluids
,
99
, pp.
93
103
.
27.
Hu
,
G.
,
Li
,
P.
,
Yi
,
L.
,
Zhao
,
Z.
,
Tian
,
X.
, and
Liang
,
X.
,
2020
, “
Simulation of Immiscible Water-Alternating-CO2 Flooding in the Liuhua Oilfield Offshore Guangdong, China
,”
Energies
,
13
(
9
), p.
2130
.
28.
Mamghaderi
,
A.
,
Bastami
,
A.
, and
Pourafshary
,
P.
,
2013
, “
Optimization of Waterflooding Performance in a Layered Reservoir Using a Combination of Capacitance-Resistive Model and Genetic Algorithm Method
,”
ASME J. Energy Resour. Technol.
,
135
(
1
), p.
013102
.
29.
Juanes
,
R.
, and
Julian Blunt
,
M.
,
2007
, “
Impact of Viscous Fingering on the Prediction of Optimum WAG Ratio
,”
SPE J.
,
12
(
4
), pp.
486
495
.
30.
Liao
,
C.
,
Liao
,
X.
,
Mu
,
L.
,
Wu
,
X.
,
Chen
,
J.
,
Ding
,
H.
, and
Xu
,
F.
,
2017
, “
Improving Water-Alternating-CO2 Flooding of Heterogeneous, Low Permeability Oil Reservoirs Using Ensemble Optimisation Algorithm
,”
Int. J. Global Warm.
,
12
(
2
), p.
242
.
31.
Adesina
,
F. A. S.
,
Churchill
,
A.
, and
Olugbenga
,
F.
,
2011
, “
Modeling Productivity Index for Long Horizontal Well
,”
ASME J. Energy Resour. Technol.
,
133
(
3
), p.
033101
.
32.
Van
,
S. L.
, and
Chon
,
B. H.
,
2017
, “
Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance
,”
Energies
,
10
(
7
), p.
842
.
33.
Chen
,
S.
,
Li
,
H.
,
Yang
,
D.
, and
Tontiwachwuthikul
,
P.
,
2010
, “
Optimal Parametric Design for Water-Alternating-Gas (WAG) Process in a CO2-Miscible Flooding Reservoir
,”
J. Can. Pet. Technol.
,
49
(
10
), pp.
75
82
.
34.
Deng
,
J.
,
1990
,
Grey System Theory Tutorials
,
Huazhong University of Science and Technology Publisher
,
Wuhan
.
35.
Xia
,
J.
,
1989
, “
Research and Application of Grey System Theory to Hydrology
,”
J. Grey Syst.
,
1
(
1
), pp.
43
52
.
36.
Fan
,
J.
, and
Li
,
X.
,
2015
, “
Prediction of the Productivity of Steam Flooding Production Wells Using Gray Relation Analysis and Support Vector Machine
,”
J. Comput. Methods Sci. Eng.
,
15
(
3
), pp.
499
506
.
37.
Yang
,
Z.
,
Li
,
Z. J.
,
Liang
,
H.
, and
Zhang
,
R.
,
2018
, “
Preferred Seepage Channel Identification Based on Multifactorial Gray Correlation Analysis
,”
Chem. Technol. Fuels Oils
,
54
(
5
), pp.
625
631
.
38.
Cui
,
L.
,
Chen
,
P.
,
Wang
,
L.
,
Li
,
J.
, and
Ling
,
H.
,
2021
, “
Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete
,”
Adv. Civ. Eng.
,
2021
, p.
14
.
39.
Miswan
,
N. H.
,
Chan
,
C. S.
, and
Ng
,
C. G.
,
2021
, “
Hospital Readmission Prediction Based on Improved Feature Selection Using Grey Relational Analysis and LASSO
,”
Grey Syst. Theory Appl.
40.
Geng
,
X. Y.
,
He
,
C.
, and
Wan
,
Y. J.
,
2020
, “
Productivity Evaluation and Prediction of Horizontal Shale Gas Wells Based on Grey Correlation Method
,”
Math. Pract. Theory
,
50
(
19
), pp.
102
108
.
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