Optimization of vertical well placement by using a hybrid particle swarm optimization

Dong, Xiaojian; Wu, Zhijian; Dong, Chao; Chen, Zhangxin; Wang, Hui

doi:10.1007/s11859-011-0742-y

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…Minimizing the volume of hydrocarbon in place at terminal time T However, the objective for most E&P companies is to maximize the economic value of the asset. The commonly used economic criterion for this purpose is the NPV [1,3,5,8,13,20,46,47,57,65,72,73]. Therefore, we designate NPV as the objective function in all problems considered in this work.…”

Section: Objective Function Formulation and Optimizationmentioning

confidence: 99%

“…To this end, we consider six population size and iteration combinations: (5, 10), (10,10), (20,10), (10,20), (10,40), and (10, 80), respectively. Note that in three of the six population size and iteration combinations, the number of iteration is held constant (while the population size is varied), and the population size is held constant (while the maximum number of iteration is varied) in the remaining three combinations.…”

Section: Case 1: Placement Of a Single Producermentioning

confidence: 99%

“…However, like DE, the application of PSO in reservoir engineering is exiguous. We note that [20] and [47] are the only known applications of PSO in well placement optimization problem domain.…”

Section: Introductionmentioning

confidence: 99%

“…The use of neural networks (NN) was applied in [11], while GA was employed in [1,6,23,26,40,46,48]. A combination of NN and GA was applied in [72]; PSO was applied in [20,47], and covariance matrix adaptation evolution strategy was applied in [8]. Apparently, there appears to be no instance or citation of DE in this problem domain.…”

Section: Introductionmentioning

confidence: 99%

“…In the same vein, PSO has been used in diverse application areasdynamic economic dispatch problems [12], quadratic assignment problems [24], pole shape optimization [10], and neural networks training [9,21,35]. Despite the popularity and application of both algorithms in the aforementioned areas of science and engineering, they are almost nonexistent in the well placement optimization problem domain, but for [20] and [47]. In [47] the standard PSO was applied to various well optimization problems, and it was shown that on the average, PSO outperforms GA in maximizing the net present value (NPV) of the reservoir models.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid differential evolution and particle swarm optimization for optimal well placement

2012

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There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems

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