Reservoir Characterization by a Combination of Fuzzy Logic and Genetic Algorithm

Ravandi, Ebrahim Ghotbi; Nezamabadi‐pour, Hossein; Monfared, Amir Ehsan Feili; Jaafarpour, A. M.

doi:10.1080/10916466.2011.601508

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…GAs have been proven useful in a variety of engineering design and optimization problems. For example, they have been used in problems involving reservoir and water quality management [10], extractants design [11], watershed problems [12], water networks [13,14], reservoir operation [15], reservoir characterization [16], machinery layout optimization [17], iron ore pressure optimization [18], separator pressure optimization in multistage production [19], scheduling types of problems [20] and many more. In this work, we study a pressure relief network design problem through the application of GAs.…”

Section: Figure 1 Comparison Between Classical Algorithms Vs Genetimentioning

confidence: 99%

Pipe size sensitivity in pressure relief networks using genetic algorithms

Alnouri

Kijevčanin

Stijepović

2020

Hem Ind

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This paper utilizes a stochastic optimization approach using genetic algorithms, for conducting rigorous pipe size sensitivity assessments onto the design of pressure relief networks. By sampling high performance candidates, only the finest options can survive. The pressure relief network system that was investigated in this work was previously reported in literature. The problem is constrained and involves minimizing a cost objective function that evaluates the overall network performance, in which the best pipe size combination should be selected for each segment within the network. The overall goal of this paper was to seek cost-effective designs for the pressure relief piping system by exploring different ranges of pipe diameters that are available for each segment in the network and comparing how the overall design of the system is affected, when the number of pipe size options to select from is varied.

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Section: Figure 1 Comparison Between Classical Algorithms Vs Genetimentioning

confidence: 99%

Pipe size sensitivity in pressure relief networks using genetic algorithms

Alnouri

Kijevčanin

Stijepović

2020

Hem Ind

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“…Also Ermida paid to strategic decisions of international oil companies (Ermida, 2014). Ravandi and his colleagues in a research study referred to combination of fuzzy logic and genetic algorithm in Reservoir characterization (Ravandi, Nezamabadi-Pour, Monfared, & Jaafarpour, 2014).…”

Section: Research Backgroundmentioning

confidence: 99%

Utilization of TOPSIS approach to formulate strategy in IRANOL Oil Company

Abolhasani¹,

Rad²

2019

Management and Business Research Quarterly

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Energy is a strategic commodity that its supply and security plays a critical role in international security and the global economy. Considering Iran's place among the countries active in production of oil and its related industries, identification of strategies related to this sector is of significant importance. At the present time, lubricants industry has become an imperative part of the development of machinery industry and its related industries. This industry has a unique quality and attractiveness when its total number of manufactured products and their markets are considered. Therefore, gaining competitive advantage is of significant importance to the players of this industry. The purpose of this research is to develop strategies for IRANOL Oil Company which is an active and important player in the lubricants industry in Iran. This study is an applied research which utilizes both descriptive and analytical approaches. By using field study method, the lubricants sector was analyzed. Then, by utilizing SWOT matrix, external and internal factors and strategies were identified and then defined through IE matrix. At last, the factors were prioritized based on TOPSIS method. Based on the results, conserva ve strategy, meaning the 11 strategies in the WO section, were selected for further evaluation and prioritization Which in order of 3 priority are:'strengthening the R&D department', 'designing and implementing comprehensive marketing strategies incorporating appropriate lateral services by developing sales engineering offices' and 'utilizing the company's full advertising power'.

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“…The proposed correlation is described in section "Verification of correlation". (Ravandi et al 2014) Some chromosomes in the population will mate through a process called crossover thus producing new chromosomes named offspring which its genes composition is the combination of their parent. In a generation, a few chromosomes will undergo mutations in their gene.…”

Section: Methodsmentioning

confidence: 99%

A new empirical correlation for estimating bubble point pressure using the genetic algorithm

Heidarian

Karimnezhad

Schaffie

et al. 2017

geol

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Abstract:In this paper, a new and more accurate correlation to predict bubble point pressure (P b ) for Middle East crudes by using the genetic alorithm (GA) is attempted. For this purpose, a total of 286 data sets of different crude oils from Middle East reservoirs were used as training data for constructing the correlation. The general form of the correlation was found by several regressive examinations. To improve the correlation, the genetic algorithm was applied. To validate the correlation, 143 data sets of different crudes from Middle East reservoirs which were different from the training data were used as test data for calculating mean absolute relative error (MARE) and correlation coefficient (R 2 ) between the predicted values from the proposed correlation and the experimental values. In addition, the MARE and R 2 were calculated for previous correlation in the test data. The results show that the proposed correlation is more accurate than all of the previous correlations exclusively for Middle East crudes.

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Reservoir Characterization by a Combination of Fuzzy Logic and Genetic Algorithm

Cited by 6 publications

References 14 publications

Pipe size sensitivity in pressure relief networks using genetic algorithms

Pipe size sensitivity in pressure relief networks using genetic algorithms

Utilization of TOPSIS approach to formulate strategy in IRANOL Oil Company

A new empirical correlation for estimating bubble point pressure using the genetic algorithm

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