Generalized field-development optimization with well-control zonation

Awotunde, Abeeb A.

doi:10.1007/s10596-016-9559-2

Cited by 22 publications

(1 citation statement)

References 40 publications

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“…Another important aspect of automatic well placement approaches is the optimization algorithm. To solve the optimal well placement problem, most researchers have utilized heuristic global optimization methods such as genetic algorithm (GA) (Montes et al, 2001;Yeten et al, 2003;Ozdogan and Horne, 2006;Emerick et al, 2009;Salmachi et al, 2013), differential evolution (DE) (Afshari et al, 2015;Awotunde, 2016), simulated annealing (Sa) (Beckner and Song, 1995), particle swarm optimization (PSO) (Onwunalu and Durlofsky, 2010;Bouzarkouna et al, 2013;Afshari et al, 2014;Forouzanfar et al, 2016), the covariance matrix adaptation-evolution strategy (CMA-ES) algorithm (Bouzarkouna et al, 2012;Jesmani et al, 2016a;Forouzanfar et al, 2016) and some hybrid algorithms (Nwankwor et al, 2013). Although these methods have the ability to avoid local solutions, their convergence to the global solution is heuristic in natural and typically require a very large number of reservoir simulation runs in an optimization loop.…”

Section: Introductionmentioning

confidence: 99%

Infill well placement optimization for secondary development of waterflooding oilfields with SPSA algorithm

Fang²,

Huang³

et al. 2022

Front. Energy Res.

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A significant amount of bypassed oil resources often remain in a mature waterflooding reservoir because of non-uniform sweep caused by natural complexities of a subsurface reservoir and improper management of the reservoir. Infill drilling is one of the most attractive options for increasing oil recovery in consequence of its operational simplicity, low risk and promising results. Determining optimal infill well placements in heterogeneous mature reservoirs is a critical and challenging task that has a significant impact on the recovery performance and economic revenue of subsurface remaining oil resources. An integrated framework is constructed to attain best-obtained optimal location and completion of infill wells in multi-layer mature oil reservoirs. The placement of an infill vertical well is parameterized in terms of two sets of variables that define the location and completion respectively. A variant of SPSA algorithm is used to solve the defined optimization problem. The performance of the proposed algorithm is first tested for the joint optimization of well location and completion of an injection well using a synthetic model. The results show that the algorithm with average SPSA gradients outperforms the single SPSA gradient method both in solution and convergence rate. Besides, there are two plateaus on the performance curve of all algorithms: on the first plateau, each algorithm is approaching to its optimal well location with relatively little change on the completion parameters, while on the second plateau, each algorithm obtains the corresponding optimal completions. A complex heterogeneous reservoir model is then constructed by using the data of a mature oil reservoir in Shengli Oilfield in China to design an optimal 10 years’ infill drilling program. Four vertical production wells are placed in the oil-rich regions and both simultaneous and sequential algorithms are tried to obtain their optimal locations and completions. The performances of simultaneous joint optimization and sequential joint optimization are compared and as a result it is recommended to use sequential joint optimization as the optimization algorithm in the integrated framework.

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