A novel tool for designing well placements by combination of modified genetic algorithm and artificial neural network

Ariadji, Tutuka; Haryadi, Febi; Rau, Irfan Taufik; Aziz, Prasandi Abdul; Dasilfa, Rinaldy

doi:10.1016/j.petrol.2014.05.018

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…A detailed introduction to and explanation of selection, crossover, and mutation are given by Ariadji et al. (2014). The MPGA‐based elitism selection procedure required 100 iteration steps.…”

Section: Methodsmentioning

confidence: 99%

“…We used the genetic operator of elitism selection, one-point crossover, and point-mutation mechanism. A detailed introduction to and explanation of selection, crossover, and mutation are given by Ariadji et al (2014). The MPGA-based elitism selection procedure required 100 iteration steps.…”

Section: Establishing a New Populationmentioning

confidence: 99%

“…Hence, genetic algorithms may have a unique advantage in searching the optimal boundaries, such as sequence boundaries (SBs), maximum flooding surface (MFS), and first flooding surface (FFS). In particular, multipopulation genetic algorithms (MPGA) can provide an optimal layering scheme ('elite population') through simultaneous searching at multiple depth ranges ('multipopulation'), recording the optimal SBs ('elite individuals') of each population, and making the results more convinced (Ariadji et al, 2014;Dobróka & Szabó, 2012;Goldberg, 1989;Li et al, 2009;Maschio et al, 2008Maschio et al, , 2015Mohammadi et al, 2019;Xue et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

et al. 2021

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The division and correlation of sequence stratigraphy are one of the essential steps in petroleum exploration. To comprehensively utilize the sequence stratigraphic information implied in multiple wireline well logs and improve the accuracy and reliability of sequence stratigraphic division, based on the edge detection, optimal partition, and natural evolution theories, and realizes the information fusion (formed Highlights1. MPGA is a powerful tool for intelligent sequencestratigraphy division.Map of Central Block of the Junggar Basin. (b) Thickness contour of Lower Cretaceous Qingshuihe Formation (K 1 q). (c) SSE-NNW-trending profile through Central Block showing stratigraphic framework of Junggar Basin (K 1 TG: Lower Cretaceous Tugulu Group; J 2 x:

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Section: Methodsmentioning

confidence: 99%

Section: Establishing a New Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

et al. 2021

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“…This method is one of the most popular methods in the well placement optimization. The idea of a genetic algorithm is first introduced by Holland in 1975 (Ariadji et al 2014).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Investigating the effect of heterogeneity on infill wells

Bagheri

Masihi

2015

J Petrol Explor Prod Technol

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In recent years, improving oil recovery (IOR) has become an important subject for the petroleum industry. One IOR method is infill drilling, which improves hydrocarbon recovery from virgin zones of the reservoir. Determining the appropriate location for the infill wells is very challenging and greatly depends on different factors such as the reservoir heterogeneity. This study aims to investigate the effect of reservoir heterogeneity on the location of infill well. In order to characterize the effect of heterogeneity on infill well locations, some geostatistical methods, e.g., sequential gaussian simulation, have been applied to generate various heterogeneity models. In particular, different correlation ranges (R) were used to observe the effect of heterogeneity. Results revealed that an increase in correlation ranges will lead to (1) a higher field oil production total, and (2) a faster expansion of the drainage radius which consequently reduced the need for infill wells. The results of this study will help engineers to appropriately design infill drilling schemes.

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“…The determination of optimum well placement is one of the key issues in the development of oil and gas fields, whether it is the development of green fields or the management of brown fields (Ariadji et al, 2014). The problem in finding optimum numbers and locations of wells where the maximization of net present value (NPV) or the cumulative oil production (COP) is sought while minimizing costs and accommodating operating limits and other constraints is recognized as a nonlinear optimization problem with integer parameters (Cullick et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Optimize Well Placement Based on Genetic Algorithm and Productivity Potential Maps

Chang²,

Liu³

et al. 2022

Front. Energy Res.

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Determining the optimal well location is a challenging task because the effects of geological and engineering variables on reservoir performance are often highly nonlinear and multimodal. The computational requirements for this problem based on automatic optimization are extensive, as many functional evaluations are required, each of which requires a complete reservoir simulation. Therefore, reducing the optimization time and improving the optimization effect is the key to promote the wide application of automatic optimization technology. In this study, we present a technique that combines the genetic algorithm (GA) with the helper method, productivity potential maps (PPMs) (GA + PPMs), to improve the effect of well placement optimization. The PPMs are generated by three typical methods: analysis method, numerical simulation method, and fuzzy system method. Numerical tests are carried out on three well placement methods in the PUNQ-S3 oilfield, namely, the original well placement and well placement proposed by GA and GA + PPMs plans. The result shows that generating the PPMs by an analytical method is the best choice. The cumulative oil production (COP) generated by GA + PPMs increased by 20.95% and 8.09%, respectively, compared with the original well scheme and GA well scheme, which demonstrates that the initial well location determined by reservoir engineers based on the PPMs has a significant impact on GA performance. Overall, the combination of GA and productivity potential maps is promising for this challenging task.

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A novel tool for designing well placements by combination of modified genetic algorithm and artificial neural network

Cited by 14 publications

References 6 publications

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

Investigating the effect of heterogeneity on infill wells

Optimize Well Placement Based on Genetic Algorithm and Productivity Potential Maps

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