Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.

References

References
1.
Butler
,
R.
,
McNab
,
G.
, and
Lo
,
H.
,
1981
, “
Theoretical Studies on the Gravity Drainage of Heavy Oil During in‐Situ Steam Heating
,”
Can. J. Chem. Eng.
,
59
(
4
), pp.
455
460
.
2.
Birrel
,
G. E.
, and
Putnam
,
P. E.
,
2000
, “
A Study of the Influence of Reservoir Architecture on SAGD Steam Chamber Development at the Underground Test Facility, Northeaster Alberta, Canada, Using a Graphical Analysis Of Temperature Profiles
,”
Petroleum Society's Canadian International Petroleum Conference
, Calgary, AB, Canada, Paper No. PETSOC-2000-104.
3.
Zhang, W.
,
Youn, S.
, and
Doan, Q. T.
, 2007, “
Understanding Reservoir Architectures and Steam-Chamber Growth at Christina Lake, Alberta, by Using 4D Seismic and Crosswell Seismic Imaging
,”
SPE Reservoir Eval. Eng.
,
10
(5), pp. 446–452.
4.
Yang
,
G.
, and
Butler
,
R.
,
1992
, “
Effects of Reservoir Heterogeneities on Heavy Oil Recovery by Steam-Assisted Gravity Drainage
,”
J. Can. Pet. Technol.
,
31
(
8
), pp. 37–43.
5.
Chen
,
Q.
,
Gerritsen
,
M. G.
, and
Kovscek
,
A. R.
,
2008
, “
Effects of Reservoir Heterogeneities on the Steam-Assisted Gravity-Drainage Process
,”
SPE Reservoir Eval. Eng.
,
11
(
5
), pp.
921
932
.
6.
Amirian
,
E.
,
Leung
,
J. Y.
,
Zanon
,
S.
, and
Dzurman
,
P.
,
2015
, “
Integrated Cluster Analysis and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Performance Prediction in Heterogeneous Reservoirs
,”
Expert Syst. Appl.
,
42
(
2
), pp.
723
740
.
7.
Wang
,
C.
, and
Leung
,
J.
,
2015
, “
Characterizing the Effects of Lean Zones and Shale Distribution in Steam-Assisted-Gravity-Drainage Recovery Performance
,”
SPE Reservoir Eval. Eng.
,
18
(
3
), pp.
329
345
.
8.
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
.
9.
Elkatatny
,
S.
,
2018
, “
Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data
,”
ASME J. Energy Resour. Technol.
,
140
(
7
), p.
072905
.
10.
Le Van
,
S.
, and
Chon
,
B. H.
,
2018
, “
Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks
,”
ASME J. Energy Resour. Technol.
,
140
(
3
), p.
032906
.
11.
Moussa
,
T.
,
Elkatatny
,
S.
,
Mahmoud
,
M.
, and
Abdulraheem
,
A.
,
2018
, “
Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches
,”
ASME J. Energy Resour. Technol.
,
140
(
7
), p.
072903
.
12.
Haykin
,
S.
,
2008
,
Neural Networks and Learning Machines
,
3rd ed.
,
M. J.
Horton
,
S.
Disanno
, eds.,
Prentice Hall
, Upper Saddle River, NJ.
13.
Ma
,
Z.
,
Leung
,
J. Y.
,
Zanon
,
S.
, and
Dzurman
,
P.
,
2015
, “
Practical Implementation of Knowledge-Based Approaches for Steam-Assisted Gravity Drainage Production Analysis
,”
Expert Syst. Appl.
,
42
(
21
), pp.
7326
7343
.
14.
Fedutenko
,
E.
,
Yang
,
C.
,
Card
,
C.
, and
Nghiem
,
L. X.
,
2014
, “
Time-Dependent Neural Network Based Proxy Modeling of SAGD Process
,”
SPE Heavy Oil Conference-Canada
, Calgary, AB, Canada, June 10–12,
SPE
Paper No. SPE-170085-MS.
15.
Ma
,
Z.
,
Leung
,
J. Y.
, and
Zanon
,
S.
,
2017
, “
Practical Data Mining and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Production Analysis
,”
ASME J. Energy Resour. Technol.
,
139
(
3
), p.
032909
.
16.
IHS Energy
,
2015
, “
AccuMap Software
,” 321 Inverness Drive South Englewood, CO, accessed Apr. 23, 2018, http://www.ihsenergy.com
17.
TOP Analysis
,
2015
, “
TOP Analysis Software
,” TOP Analysis, Calgary, AB, Canada, accessed Nov. 17, 2015, http://www.topanalysis.com
18.
Regulator, A.E., 2012, “
Suncor Firebag 2012 ERCB Performance Presentation
,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2012AthabascaSuncorFirebagSAGD8870.pdf
19.
Regulator, A. E.
, 2013, “
Suncor Firebag 2013 ERCB Performance Presentation
,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2013AthabascaSuncorFirebagSAGD8870.pdf
20.
Regulator, A. E.
, 2014, “
Suncor Firebag 2014 AER Performance Presentation
,” Alberta Energy Regulator, Calgary, AB, Canada, accessed Mar. 15, 2016, https://www.aer.ca/documents/oilsands/insitu-presentations/2014AthabascaSuncorFirebagSAGD8870.pdf
21.
Li
,
P.
,
2006
, “
Numerical Simulation of the SAGD Process Coupled With Geomechanical Behavior
,” Ph.D. thesis, University of Alberta, Edmonton, AB, Canada.
22.
CMG
,
2015
,
STARS: Users' Guide, Advanced Processes & Thermal Reservoir Simulator (Version 2015)
,
Computer Modeling Group
,
Calgary, AB, Canada
.
23.
Cox
,
T. F.
, and
Cox
,
M. A.
,
2001
,
Multidimensional Scaling
,
2nd ed.
,
Chapman and Hall
,
London, UK
.
24.
Zheng
,
J.
,
Leung
,
J. Y.
,
Sawatzky
,
R. P.
, and
Alvarez
,
J. M.
,
2018
, “
A Cluster-Based Approach for Visualizing and Quantifying the Uncertainty in the Impacts of Uncertain Shale Barrier Configurations on SAGD Production
,”
SPE Canada Heavy Oil Technical Conference
, Calgary, AB, Canada, SPE Paper No. SPE-189753-MS.
25.
Nielsen
,
M. A.
,
2015
,
Neural Network and Deep Learning
,
Determination Press
.
26.
Deutsch
,
C. V.
, and
Journel
,
A. G.
,
1998
,
GSLIB: Geostatistical Software Library and User's Guide
,
Oxford University Press
,
New York
.
27.
Liu
,
J.
,
Jaiswal
,
A.
,
Yao
,
K.
, and
Raghavenda
,
C. S.
,
2015
, “
Autoencoder-Derived Features as Inputs to Classification Algorithms for Predicting Well Failures
,”
SPE Western Regional Meeting
, Garden Grove, CA,
SPE
Paper No. SPE-174015-MS.
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