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Research Papers: Petroleum Engineering

Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function

[+] Author and Article Information
Majid Siavashi

Applied Multi-Phase Fluid Dynamics Laboratory,
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: msiavashi@iust.ac.ir

Mohammad Rasoul Tehrani

Institute of Petroleum Engineering,
University of Tehran,
P.O. Box 14395-515,
Tehran, Iran
e-mail: rasoul_tehrani@alumni.ut.ac.ir

Ali Nakhaee

Institute of Petroleum Engineering,
University of Tehran,
P.O. Box 14395-515,
Tehran, Iran
e-mail: anakhaee@ut.ac.ir

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 22, 2015; final manuscript received January 5, 2016; published online February 23, 2016. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 138(5), 052903 (Feb 23, 2016) (9 pages) Paper No: JERT-15-1357; doi: 10.1115/1.4032547 History: Received September 22, 2015; Revised January 05, 2016

One of the main reservoir development plans is to find optimal locations for drilling new wells in order to optimize cumulative oil recovery. Reservoir simulation is a necessary tool to study different configurations of well locations to investigate the future of the reservoir and determine the optimal places for well drilling. Conventional well-known numerical methods require modern hardware for the simulation and optimization of large reservoirs. Simulation of such heterogeneous reservoirs with complex geological structures with the streamline-based simulation method is more efficient than the common simulation techniques. Also, this method by calculation of a new parameter called “time-of-flight” (TOF) offers a very useful tool to engineers. In the present study, TOF and distribution of streamlines are used to define a novel function which can be used as the objective function in an optimization problem to determine the optimal locations of injectors and producers in waterflooding projects. This new function which is called “well location assessment based on TOF” (WATOF) has this advantage that can be computed without full time simulation, in contrast with the cumulative oil production (COP) function. WATOF is employed for optimal well placement using the particle swarm optimization (PSO) approach, and its results are compared with those of the same problem with COP function, which leads to satisfactory outcomes. Then, WATOF function is used in a hybrid approach to initialize PSO algorithm to maximize COP in order to find optimal locations of water injectors and oil producers. This method is tested and validated in different 2D problems, and finally, the 3D heterogeneous SPE-10 reservoir model is considered to search locations of wells. By using the new objective function and employing the hybrid method with the streamline simulator, optimal well placement projects can be improved remarkably.

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References

Figures

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Fig. 1

A sample objective function surface for a single well placement problem [45]

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Fig. 2

Distribution of streamlines in the reservoir for optimal well arrangement of 13 wells

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Fig. 3

Convergence history of WATOF-based PSO

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Fig. 4

Convergence history of COP-based PSO

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Fig. 5

Convergence history of COP-based, WATOF-based, and the hybrid approaches

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Fig. 6

Initial position of wells and its corresponding distribution of oil saturation after 2000 days

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Fig. 7

Optimal position of wells and its corresponding distribution of oil saturation after 2000 days

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