This paper presents an efficient production optimization scheme for an oil reservoir undergoing water injection by optimizing the production rate for each well. In this approach, an adaptive version of simulated annealing (ASA) is used in two steps. The optimization variables updating in the first stage is associated with a coarse grid model. In the second step, the fine grid model is used to provide more details in final solution search. The proposed method is formulated as a constrained optimization problem defining a desired objective function and a set of existing field/facility constraints. The use of polytope in the ASA ensures the best solution in each iteration. The objective function is based on net present value (NPV). The initial oil production rates for each well come from capacity and property of each well. The coarse grid block model is generated based on average horizon permeability. The proposed optimization workflow was implemented for a field sector model. The results showed that the improved rates optimize the total oil production. The optimization of oil production rates and total water injection rate leads to increase in the total oil production from 315.616 MSm3 (our initial guess) to 440.184 MSm3, and the recovery factor is increased to 26.37%; however, the initial rates are much higher than the optimized rates. Beside this, the recovery factor of optimized production schedule with optimized total injection rate is 3.26% larger than the initial production schedule with optimized total water injection rate.

References

References
1.
Zhang
,
L.
,
Zhang
,
K.
,
Chen
,
Y.
,
Li
,
M.
,
Yao
,
J.
,
Li
,
L.
, and
Lee
,
J.
,
2015
, “
Smart Well Pattern Optimization Using Gradient Algorithm
,”
ASME J. Energy Resour. Technol.
,
138
(
1
), p.
012901
.
2.
Asheim
,
H.
,
1998
, “
Maximization of Water Sweep Efficiency by Controlling Production and Injection Rates
,”
SPE
European Petroleum Conference
, London, Paper No. SPE 18365.
3.
Sudaryanto
,
B.
, and
Yortsos
,
Y. C.
,
2000
, “
Optimization of Fluid Front Dynamics in Porous Media Using Rate Control
,”
Phys. Fluids
,
12
(
7
), pp.
1656
1670
.
4.
Brouwer
,
D.
, and
Jansen
,
J.
,
2004
, “
Dynamic Optimization of Water Flooding With Smart Wells Using Optimal Control Theory
,”
SPE J.
,
9
(
4
), pp.
391
402
.
5.
Sarma
,
P.
,
Aziz
,
K.
, and
Durlofsky
,
L.
,
2005
, “
Implementation of Adjoint Solution for Optimal Control of Smart Wells
,”
SPE
Reservoir Simulation Symposium
, Houston, TX, Paper No. SPE 92864.
6.
Jansen
,
J.
,
Bosgra
,
O.
, and
Van den Hof
,
P.
,
2008
, “
Model-Based Control of Multiphase Flow in Subsurface Oil Reservoirs
,”
J. Process Control
,
18
(
9
), pp.
864
855
.
7.
Bailey
,
W.
,
Couët
,
B.
, and
Wilkinson
,
D.
,
2005
, “
Framework for Field Optimization to Maximize Asset Value
,”
SPE Reservoir Eval. Eng.
,
8
(
1
), pp.
7
21
.
8.
Yasari
,
E.
,
Pishvaie
,
M. R.
,
Khorasheh
,
F.
,
Salahshoor
,
K.
, and
Kharrat
,
R.
,
2013
, “
Application of Multi-Criterion Robust Optimization in Water-Flooding of Oil Reservoir
,”
J. Pet. Sci. Eng.
,
109
, pp.
1
11
.
9.
Chen
,
C.
,
Li
,
G.
, and
Reynolds
,
A.
,
2012
, “
Robust Constrained Optimization of Short- and Long-Term Net Present Value for Closed-Loop Reservoir Management
,”
SPE J.
,
17
(
3
), pp.
849
864
.
10.
Siraj
,
M.
,
Van den Hof
,
P.
, and
Jansen
,
J.
,
2015
, “
Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization
,”
SPE
Reservoir Simulation Symposium
, Paper No. SPE-173285-MS.
11.
Van Essen
,
G.
,
Zandvliet
,
M.
,
Van den Hof
,
P. M. J.
,
Bosgra
,
O.
, and
Jansen
,
J. D.
,
2009
, “
Robust Waterflooding Optimization of Multiple Geological Scenarios
,”
SPE J.
,
14
(
1
), pp.
202
210
.
12.
Ikewun
,
P.
, and
Ahmadi
,
M.
,
2012
, “
Production Optimization and Forecasting of Shale Gas Wells Using Simulation Models and Decline Curve Analysis
,”
SPE
Western Regional Meeting
, Bakersfield, CA, Paper No. SPE 153914.
13.
Pan
,
Y.
, and
Horne
,
R.
,
1998
, “
Improved Methods for Multivariate Optimization of Field Development Scheduling and Well Placement Design
,”
SPE
Annual Technical Conference and Exhibition
, New Orleans, LA, Sept. 27–30, Paper No. SPE 49055.
14.
Pan
,
Y.
,
1995
, “
Application of Least Squares and Kriging in Multivariate Optimizations of Field Development Scheduling and Well Placement
,”
Ph.D. thesis
, Stanford University, Stanford, CA.
15.
Güyagüler
,
B.
,
Horne
,
L.
, and
Rosenzweig
,
J.
,
2002
, “
Optimization of Well Placement in a Gulf of Mexico Waterflooding Project
,”
SPE Reservoir Eval. Eng.
,
5
(
3
), pp.
229
236
.
16.
Artus
,
V.
,
Durlofsky
,
L.
,
Onwunalu
,
J.
, and
Aziz
,
K.
,
2006
, “
Optimization of Nonconventional Wells Under Uncertainty Using Statistical Proxies
,”
Comput. Geosci.
,
10
(
4
), pp.
389
404
.
17.
Stoisits
,
R.
,
Crawford
,
K.
,
MacAllister
,
D.
,
Lawal
,
A.
, and
Ogbe
,
D.
,
1999
, “
Production Optimization at the Kuparuk River Field Utilizing Neural Networks and Genetic Algorithms
,”
Mid-Continent Operations Symposium
, Oklahoma City, OK, Mar. 28–31, Paper No. SPE 52177.
18.
Yeten
,
B.
,
2003
, “
Optimum Deployment of Nonconventional Wells
,” Ph.D. thesis, Stanford University, Stanford, CA.
19.
Zangl
,
G.
,
Graf
,
T.
, and
Al-Kinani
,
A.
,
2006
, “
Proxy Modeling in Production Optimization
,”
SPE
Europec/EAGE Annual Conference and Exhibition
, Vienna, Austria, June 12–15, Paper No. SPE100131.
20.
Onwunalu
,
J.
,
Litvak
,
M.
,
Durlofsky
,
L.
, and
Aziz
,
K.
,
2008
, “
Application of Statistical Proxies to Speed Up Field Development Optimization Procedures
,”
International Petroleum Exhibition and Conference
, Abu Dhabi, UAE, Nov. 3–6, Paper No. SPE-117323-MS.
21.
Guyaguler
,
B.
, and
Horne
,
R.
,
1999
, “
Optimization of Well Placement
,”
ASME J. Energy Resour. Technol.
,
122
(
2
), pp.
64
70
.
22.
Siavashi
,
M.
,
Tehrani
,
M. R.
, and
Nakhaee
,
A.
,
2016
, “
Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function
,”
ASME J. Energy Resour. Technol.
,
138
(
5
), p.
052903
.
23.
Mamghaderi
,
A.
,
Bastami
,
A.
, and
Pourafshary
,
P.
,
2012
, “
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
.
24.
Yeten
,
B.
,
Castellini
,
A.
,
Guyaguler
,
B.
, and
Chen
,
W.
,
2005
, “
A Comparison Study on Experimental Design and Response Surface Methodologies
,”
SPE
Reservoir Simulation Symposium
, The Woodlands, TX, Jan. 31–Feb. 2, Paper No. SPE 93347.
25.
Amorim
,
T.
, and
Mocyzdlower
,
B.
,
2007
, “
Validating the Use of Experimental Design Techniques in Exploratory Evaluations
,”
SPE
Latin American and Caribbean Petroleum Engineering Conference
, Buenos Aires, Argentina, Apr. 15–18, Paper No. SPE 107441.
26.
Selley
,
R.
,
1998
,
Elements of Petroleum Geology
,
2nd ed.
,
Academic Press
,
San Diego, CA
.
27.
Carlson
,
M.
,
2003
,
Practical Reservoir Simulation: Using, Assessing, and Developing Results
,
PennWell Books
,
Tulsa, OK
.
28.
Tureyen
,
O.
, and
Caers
,
J.
,
2003
, “
A Two-Level Optimization Method for Geostatistical Integration of Production Data on Non-Uniform Grids
,”
SPE
Annual Technical Conference and Exhibition
, Denver, CO, Oct. 5–8, Paper No. SPE 84368.
29.
Chen
,
Y.
, and
Durlofky
,
L.
,
2007
, “
An Ensemble Level Upscaling Approach for Efficient Estimation of Fine-Scale Production Statistics Using Coarse-Scale Simulations
,”
SPE
Reservoir Simulation Symposium
, Houston, TX, Feb. 26–28, Paper No. SPE 106086.
30.
Bhark
,
E.
,
Rey
,
A.
, and
Jafarpour
,
B.
,
2011
, “
Multiscale Parameterization and History Matching in Structured and Unstructured Grid Geometries
,”
SPE
Reservoir Simulation Symposium
, The Woodlands, TX, Feb. 21–23, Paper No. SPE 141764.
31.
Camponogara
,
E.
, and
Nakashima
,
P. H. R.
,
2006
, “
Optimal Allocation of Lift-Gas Rates Under Multiple Facility Constraints: A Mixed Integer Linear Programming Approach
,”
ASME J. Energy Resour. Technol.
,
128
(
4
), pp.
280
289
.
32.
Ekkawong
,
P.
,
Kritsadativud
,
P.
,
Lerlertpakdee
,
P.
, and
Amornprabharwat
,
A.
,
2015
, “
Innovative and Automated Workflow for Fast Production Optimization and Forecast in Gulf of Thailand Gas Fields Using Linear Programing Optimization
,”
SPE
Digital Energy Conference and Exhibition
, The Woodlands, TX, Paper No. SPE 173450.
33.
Kritsadativud
,
P.
,
Jafarpour
,
B.
, and
Ekkawong
,
P.
,
2015
, “
Fast Production Optimization With Decline Curve Analysis Under Facility Constraints: A Field Case Study
,”
SPE
Western Regional Meeting
, Garden Grove, CA, Paper No. SPE-174039-MS.
34.
Güyagüler
,
B.
, and
Horne
,
R.
,
2001
, “
Uncertainty Assessment of Well Placement Optimization
,”
SPE
Annual Technical Conference and Exhibition
, New Orleans, LA, Sept. 30–Oct. 3, Paper No. SPE 71625.
35.
Bardu
,
O.
,
2003
, “
Well Placement Optimization Using the Quality Map Approach
,”
Ph.D. thesis
, Stanford University, Stanford, CA.
36.
Bittencourt
,
A. C.
, and
Horne
,
R. N.
,
1997
, “
Reservoir Development and Design Optimization
,”
SPE
Annual Technical Conference and Exhibition
, San Antonio, TX, Oct. 5–8, Paper No. SPE 38895.
37.
Salam
,
D.
,
Gunardi
,
I.
, and
Yasutra
,
A.
,
2015
, “
Production Optimization Strategy Using Hybrid Genetic Algorithm
,”
Abu Dhabi International Petroleum Exhibition and Conference
, Abu Dhabi, UAE, Nov. 9–12, Paper No. SPE-177442-MS.
38.
Beckner
,
B.
, and
Song
,
X.
,
1995
, “
Field Development Planning Using Simulated Annealing—Optimal Economic Well Scheduling and Placement
,”
SPE
Annual Technology Conference and Exhibition
, Dallas, TX, Oct. 22–25, Paper No. SPE30650.
39.
Yang
,
D.
,
Zhang
,
Q.
, and
Gu
,
Y.
,
2003
, “
Integrated Optimization and Control of the Production-Injection Operation Systems for Hydrocarbon Reservoirs
,”
J. Pet. Sci. Eng.
,
37
(
1–2
), pp.
69
81
.
40.
Fallat
,
M.
, and
Stan
,
E.
,
1998
, “
Simplex Simulated Annealing: A Hybrid Approach to Geoacoustic Inversion
,”
J. Acoust. Soc. Am.
,
103
(
5
), pp.
2934
2934
.
41.
Hedar
,
A.
, and
Fukushima
,
M.
,
2002
, “
Hybrid Simulated Annealing and Direct Search Method for Nonlinear Unconstrained Global Optimization
,”
Optim. Methods Software
,
17
(
5
), pp.
891
912
.
42.
Van Essen
,
G.
,
Van den Hof
,
P.
, and
Jansen
,
J.
,
2009
, “
Hierarchical Long-Term and Short-Term Production Optimization
,”
SPE
Annual Technical Conference and Exhibition
, New Orleans, LA, Oct. 4–7, Paper No. SPE-124332-MS.
43.
van Laarhoven
,
P.
, and
Aarts
,
E.
,
1987
,
Simulated Annealing: Theory and Applications
,
Springer Science+Business Media
,
Eindhoven, The Netherlands
.
44.
Ingber
,
L.
,
1996
, “
Adaptive Simulated Annealing (ASA): Lessons Learned
,”
Control Cybern.
,
25
(
1
), pp.
33
54
.
You do not currently have access to this content.