For decision making, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since initial models constructed with limited data have high uncertainty, it is essential to integrate both static and dynamic data for reliable future predictions. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching, and multiple realizations of reservoir models should be computed. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline-based inversion and distance-based clustering. A distance between each reservoir model is defined as the norm of differences of generalized travel time (GTT) vectors. Then, reservoir models are grouped according to the distances and representative models are selected from each group. Inversions are performed on the representative models instead of using all models. We use generalized travel time inversion (GTTI) for the integration of dynamic data to overcome high nonlinearity and take advantage of computational efficiency. It is verified that the proposed method gathers models with both similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances reliably, while reducing the amount of calculations significantly by using the representative models.

References

1.
Cheng
,
Y.
,
Lee
,
W. J.
, and
McVay
,
D. A.
,
2008
, “
Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs
,”
ASME J. Energy Resour. Technol.
,
130
(
4
), p.
043201
.
2.
Jeong
,
D.
,
Jeong
,
K.
,
Baik
,
H.
, and
Choe
,
J.
,
2013
, “
Uncertainty Analyses of Basement Fracture Reservoir Performances Using Proxy Models With High-Quality History Matching
,”
Energy Explor. Exploit.
,
31
(
3
), pp.
395
410
.
3.
Jeong
,
D.
,
Jeong
,
K.
,
Baik
,
H.
, and
Choe
,
J.
,
2013
, “
Feasibility Study and Economic Analyses for the Marginal Field Development Using Proxy Models Under Uncertainty of Reservoir Characterization
,”
Energy Explor. Exploit.
,
31
(
6
), pp.
833
846
.
4.
Gu
,
Y.
, and
Oliver
,
D. S.
,
2005
, “
The Ensemble Kalman Filter for Continuous Updating of Reservoir Simulation Models
,”
ASME J. Energy Resour. Technol.
,
128
(
1
), pp.
79
87
.
5.
Arroyo-Negrete
,
E.
,
Devegowda
,
D.
,
Datta-Gupta
,
A.
, and
Choe
,
J.
,
2008
, “
Streamline Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating
,”
SPE Reservoir Eval. Eng.
,
11
(
6
), pp.
1046
1060
.
6.
Jung
,
S.
, and
Choe
,
J.
,
2010
, “
Stochastic Estimation of Oil Production by History Matching With Ensemble Kalman Filter
,”
Energy Sources, Part A
,
32
(
10
), pp.
952
961
.
7.
Jung
,
S.
, and
Choe
,
J.
,
2012
, “
Reservoir Characterization Using a Streamline-Assisted Ensemble Kalman Filter With Covariance Localization
,”
Energy Explor. Exploit.
,
30
(
4
), pp.
645
660
.
8.
Panwar
,
W.
,
Trivedi
,
J. J.
, and
Nejadi
,
S.
,
2015
, “
Importance of Distributed Temperature Sensor Data for Steam Assisted Gravity Drainage Reservoir Characterization and History Matching Within Ensemble Kalman Filter Framework
,”
ASME J. Energy Resour. Technol.
,
137
(
4
), p.
042902
.
9.
Wu
,
Z.
, and
Datta-Gupta
,
A.
,
2001
, “
Rapid History Matching Using a Generalized Travel Time Inversion Method
,”
SPE
Reservoir Simulation Symposium
, Houston, TX, Feb. 11–14, Paper No. SPE 66352.
10.
He
,
Z.
,
Yoon
,
S.
, and
Datta-Gupta
,
A.
,
2002
, “
Streamline-Based Production Data Integration With Gravity and Changing Field Conditions
,”
SPE J.
,
7
(
4
), pp.
423
436
.
11.
Bhark
,
E.
,
Rey
,
A.
,
Datta-Gupta
,
A.
, and
Jafarpour
,
B.
,
2012
, “
A Multiscale Workflow for History Matching Structured and Unstructured Grid Geometries
,”
SPE J.
,
17
(
3
), pp.
828
848
.
12.
Cheng
,
H.
,
Wen
,
X.
,
Milliken
,
W.
, and
Datta-Gupta
,
A.
,
2004
, “
Field Experiences With Assisted and Automatic History Matching Using Streamline Models
,”
SPE
Annual Technical Conference and Exhibition
, Houston, TX, Sept. 26–29, Paper No. SPE 89857.
13.
Mamghaderi
,
A.
,
Bastami
,
A.
, and
Pourafshary
,
P.
,
2012
, “
Optimization of Waterflooding in a Layered Reservoir Using a Combination of Capacitance-Resistive Model and Genetic Algorithm Method
,”
ASME J. Energy Resour. Technol.
,
135
(
1
), p.
013102
.
14.
Shi
,
J.
, and
Leung
,
J. Y.
,
2014
, “
Semi-Analytical Proxy for Vapex Process Modeling in Heterogeneous Reservoirs
,”
ASME J. Energy Resour. Technol.
,
136
(
3
), p.
032904
.
15.
Cheng
,
H.
,
Kharghoria
,
A.
,
He
,
Z.
, and
Datta-Gupta
,
A.
,
2005
, “
Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities
,”
SPE Reservoir Eval. Eng.
,
8
(
5
), pp.
426
436
.
16.
Kitanidis
,
P. K.
,
1995
, “
Quasilinear Geostatistical Theory for Inversing
,”
Water Resour. Res.
,
31
(
10
), pp.
2411
2419
.
17.
Lee
,
K.
,
Jeong
,
H.
,
Jung
,
S.
, and
Choe
,
J.
,
2013
, “
Characterization of Channelized Reservoir Using Ensemble Kalman Filter With Cluster Covariance
,”
Energy Explor. Exploit.
,
31
(
1
), pp.
17
29
.
18.
Lee
,
K.
,
Jeong
,
H.
,
Jung
,
S.
, and
Choe
,
J.
,
2013
, “
Improvement of Ensemble Smoother With Clustered Covariance for Channelized Reservoirs
,”
Energy Explor. Exploit.
,
31
(
5
), pp.
713
726
.
19.
Lee
,
H.
,
Jin
,
J.
,
Shin
,
H.
, and
Choe
,
J.
,
2015
, “
Efficient Prediction of SAGD Productions Using Static Factor Clustering
,”
ASME J. Energy Resour. Technol.
,
137
(
3
), p.
032907
.
20.
Schedit
,
C.
, and
Caers
,
J.
,
2009
, “
Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods—Application to a West Africa Deepwater Turbidite Reservoir
,”
SPE J.
,
14
(
4
), pp.
680
692
.
21.
Shirman
,
E.
,
Wojtanowicz
,
A. K.
, and
Kurban
,
H.
,
2014
, “
Enhancing Oil Recovery With Bottom Water Drainage Completion
,”
ASME J. Energy Resour. Technol.
,
136
(
4
), p.
042906
.
22.
Choe
,
J.
,
2007
,
Geostatistics
,
Sigma Press
,
Seoul, Korea
.
You do not currently have access to this content.