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Research Papers: Petroleum Engineering

Well Placement Optimization With Cat Swarm Optimization Algorithm Under Oilfield Development Constraints

[+] Author and Article Information
Chen Hongwei, Zhang Xianmin, Wang Sen

School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China

Feng Qihong

School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fengqihong.upc@gmail.com

Zhou Wensheng, Liu Fan

China National Offshore Oil Corporation
Research Institute,
Beijing 100027, China

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 13, 2018; final manuscript received June 21, 2018; published online July 23, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 141(1), 012902 (Jul 23, 2018) (11 pages) Paper No: JERT-18-1127; doi: 10.1115/1.4040754 History: Received February 13, 2018; Revised June 21, 2018

Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.

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Figures

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Fig. 1

Sketch of optimization variables for well placement

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Fig. 2

Flow chart of constrained well placement optimization with CSO algorithm

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Fig. 3

The log-permeability and porosity distributions of case I: (a) log-permeability distribution and (b) porosity distribution

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Fig. 6

Cumulative oil and water production under the initial and optimized well placements for case I

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Fig. 5

Oil saturation distributions under the different well placements: (a) initial well placements and (b) optimized well placements

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Fig. 4

Average convergence curves of CSO algorithm with different constraint handling methods

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Fig. 9

The average convergence curves of CSO, GA and DE algorithms in case III

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Fig. 8

Permeability and initial oil saturation distributions for case III: (a) permeability distribution and (b) porosity distribution

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Fig. 7

The average convergence curves of CSO, GA and DE algorithms in case II

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Fig. 10

Three-dimensional configurations of the optimal well locations for case III

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Fig. 11

Remaining oil saturation under the initial and optimized well locations for case III: (a) initial well locations and (b) optimized well locations

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Fig. 12

Cumulative oil, water, and gas productions under the initial and optimized well placements for case III

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