Improvement Of Particle Swarm Optimization And Its Application In Petroleum Engineering

Many problems in petroleum engineering can be abstracted to optimization problems.The traditional optimization methods are powerless in dealing with these complexoptimization problems. Intelligent optimization methods have become the effective methodsto solve complex optimization problems. Particle Swarm Optimization (PSO) is an intelligentoptimization method which is concerned and used widely.PSO has simple calculation, less control parameters, easy realization and strongrobustness. PSO is very suitable for solving complex optimization problems. It has theshortcomings of being easy to fall into local optimum and low convergence precision.Therefore its performance is studied and improved in this paper as it is used to solve theproblems of unconstrained single objective optimization, constrained single objectiveoptimization and constrained multi-objective optimization. The improved algorithms areapplied to several typical petroleum engineering optimization problems and satisfactoryresults have been achieved.1. Dynamic Quantum-behaved Particle Swarm Optimization Based on Chaos (CDQPSO)is proposed in this paper. According to population evolution factor, a particle swarm will bedivided dynamically into two subgroups. When the evolution of the population slows down,chaotic mutation will be used to update the particles in the subgroup which is composed ofparticles having worse fitness values, and a small perturbation will be given to the globaloptimal particle to keep population diversity and improve the global searching ability. Thetest results of typical complex high dimension functions indicate that CDQPSO is not easy tofall into local extremum and its convergence speed is high. Its optimization effect is betterthan that of CO and QPSO. It shows good global optimization performance. Combining withpenalty function, better effect is achieved as it is applied to operation optimization of oilfieldwater injection system.2. Currently penalty function is most commonly used to handle the constraints. It isdifficult to determine appropriate penalty factor. It needs to be adjusted through manyexperiments. In this paper, Quantum-behaved Particle Swarm Optimization with DoubleFitness (DFQPSO) is proposed for constrained optimization. Double fitness values aredefined for every particle by separating objective function and the constraints. Whether theparticle is better or not will be decided by its two fitness values. An adaptive strategy is usedto keep a proper proportion of infeasible particles. Numerical experimental results show that DFQPSO is better on precision and convergence than QPSO using a penalty function and afew other algorithms. The effect is good when it is applied to layout optimization design ofoilfield water injection pipe network.3. Multi-objective Particle Swarm Optimization Based on Spatial Partition Tree(SPTMOPSO) is proposed in this paper. The target space, corresponding to archive set, isdivided into many cell-grids. Nonempty cell-grids are indexed by spatial partition tree. As aresult, the time complexity of the algorithm is cut down. The particle, whose density ratio ofcrowding distance is the largest, has priority to be selected as the global extremum. Globalextremum selection is more accurate. Pareto optimal set has better diversity. Numericalexperimental results show that SPTMOPSO is effective. The effect is good when it is appliedto oil blending optimization.4. The effects are good when DFQPSO is applied respectively to dynamic divisionprediction of development indexes and pipeline insulation optimization. The results aresatisfactory when SPTMOPSO is applied respectively to injection allocation schemeoptimization and pipeline insulation optimization.