Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy

Abstract Layout optimization of large-scale oil–gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells.

[1]  Pengsong Guo,et al.  Modified particle swarm optimization for BMDS interceptor resource planning , 2015, Applied Intelligence.

[2]  Swagatam Das,et al.  An improved particle swarm optimizer with difference mean based perturbation , 2014, Neurocomputing.

[3]  A. Hodgkinson,et al.  Elevation of the Concentration of Plasma Oxalic Acid in Renal Failure , 1966, Nature.

[4]  Fuad E. Alsaadi,et al.  A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay , 2016, Cognitive Computation.

[5]  Yang Liu,et al.  The role of surface and subsurface integration in the development of a high-pressure and low-production gas field , 2015, Environmental Earth Sciences.

[6]  Beatriz Souza Leite Pires de Lima,et al.  Optimal design of submarine pipeline routes by genetic algorithm with different constraint handling techniques , 2014, Adv. Eng. Softw..

[7]  Mohammad Mehdi Ebadzadeh,et al.  History-Driven Particle Swarm Optimization in dynamic and uncertain environments , 2016, Neurocomputing.

[8]  Yuan Xi-Gang,et al.  An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints , 2007 .

[9]  Ling Wang,et al.  An effective hybrid PSOSA strategy for optimization and its application to parameter estimation , 2006, Appl. Math. Comput..

[10]  Fuad E. Alsaadi,et al.  A switching delayed PSO optimized extreme learning machine for short-term load forecasting , 2017, Neurocomputing.

[11]  Emilio F. Campana,et al.  Dynamic system analysis and initial particles position in Particle Swarm Optimization , 2006 .

[12]  Andrea Ramírez,et al.  The influence of risk mitigation measures on the risks, costs and routing of CO2 pipelines , 2014 .

[13]  Yong Feng,et al.  Chaotic Inertia Weight in Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[14]  O. J. Dunn Multiple Comparisons among Means , 1961 .

[15]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[16]  Yang Liu,et al.  A self-adaptive harmony PSO search algorithm and its performance analysis , 2015, Expert Syst. Appl..

[17]  James P. Brill,et al.  A Study of Two-Phase Flow in Inclined Pipes , 1973 .

[18]  Chen-Chien James Hsu,et al.  Hybrid particle swarm optimization incorporating fuzzy reasoning and weighted particle , 2015, Neurocomputing.

[19]  Zheng Li,et al.  Expert Systems With Applications , 2022 .

[20]  Xiang Li,et al.  A hybrid particle swarm with a time-adaptive topology for constrained optimization , 2014, Swarm and Evolutionary Computation.

[21]  Samiran Chattopadhyay,et al.  An efficient GA-PSO approach for solving mixed-integer nonlinear programming problem in reliability optimization , 2014, Swarm Evol. Comput..

[22]  Yang Liu,et al.  Optimal Parameters Design of Oilfield Surface Pipeline Systems Using Fuzzy Models , 1999, Inf. Sci..

[23]  Mousa Shamsi,et al.  A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm , 2016, PloS one.

[24]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[25]  Javad Mahmoudimehr,et al.  Optimal design of a natural gas transmission network layout , 2013 .

[26]  Halit Üster,et al.  Optimization for Design and Operation of Natural Gas Transmission Networks , 2014 .

[27]  José Neves,et al.  Watch thy neighbor or how the swarm can learn from its environment , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[28]  Jeff Orchard,et al.  Particle swarm optimization using dynamic tournament topology , 2016, Appl. Soft Comput..

[29]  M. Friedman A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[30]  Ioannis A. Papazoglou,et al.  Integrated framework for the design of pipeline systems using stochastic optimisation and GIS tools , 2012 .

[31]  Ying Tan,et al.  Fireworks Algorithm for Optimization , 2010, ICSI.

[32]  Menglan Duan,et al.  A mathematical model for subsea wells partition in the layout of cluster manifolds , 2012 .

[33]  Ivan Stojmenovic,et al.  A Fast Iterative Algorithm for Generating Set Partitions , 1989, Comput. J..

[34]  V. Arnold Mathematical Methods of Classical Mechanics , 1974 .

[35]  Jun Zeng,et al.  Optimization of natural gas transport pipeline network layout: a new methodology based on dominance degree model , 2018 .

[36]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[37]  Tibérius O. Bonates,et al.  Integrated optimization model for location and sizing of offshore platforms and location of oil wells , 2016 .

[38]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[39]  Zidong Wang,et al.  A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease , 2018, Neurocomputing.

[40]  Julián M. Ortiz,et al.  A comparison between ACO and Dijkstra algorithms for optimal ore concentrate pipeline routing , 2017 .

[41]  Nélida B. Brignole,et al.  Simulated Annealing Optimization for Hydrocarbon Pipeline Networks , 2013 .

[42]  Hang Dong,et al.  Optimization model establishment and optimization software development of gas field gathering and transmission pipeline network system , 2016, J. Intell. Fuzzy Syst..

[43]  Haoran Zhang,et al.  A novel particle swarm optimization based on prey-predator relationship , 2018, Appl. Soft Comput..

[44]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[45]  Christodoulos A. Floudas,et al.  A review of recent advances in global optimization , 2009, J. Glob. Optim..

[46]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..

[47]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[48]  Steven Li,et al.  Improved global-best-guided particle swarm optimization with learning operation for global optimization problems , 2017, Appl. Soft Comput..

[49]  Mark S. Daskin,et al.  Strategic facility location: A review , 1998, Eur. J. Oper. Res..

[50]  Kenneth Steiglitz,et al.  Optimal Design of Offshore Natural-Gas Pipeline Systems , 1970, Oper. Res..

[51]  Bijay Ketan Panigrahi,et al.  Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection , 2015 .

[52]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[53]  Yang Liu,et al.  PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization , 2018, Comput. Intell. Neurosci..