Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

Kazemi, Hajar; Yazdjerdi, Kouros; Asadi, Abdolmajid; Mozafari, Mohammad Reza

doi:10.1556/24.2020.00004

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…21,[23][24][25][26][27] 2.1.1 | K-means clustering K-means is a clustering algorithm that introduced in 1967 by MacQueen. 28 The procedure to cluster the data is based on defining a certain number of clusters (K). The algorithm will define k centroids for each cluster randomly from the data set to use them as a head for each cluster to assign a data point to each one of cluster heads.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

et al. 2021

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Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k-means, fuzzy c-means, and hierarchical clustering algorithms). The

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Section: Clustering Algorithmsmentioning

confidence: 99%

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

et al. 2021

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Semi-analytical approach to the determination of fracture permeability

et al. 2023

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Proppant is one of the major elements of a hydraulic fracture process that can influence the productivity of an oil or gas well. The permeability of a propped fracture may be determined through laboratory measurements, utilizing experimental relationships, or using analytical methods. All methods have their advantages and disadvantages, and this study attempts to offers a solution that combines the various methods to achieve a practical solution. The standard measurement method for fracture conductivity and permeability is a time-consuming and delicate procedure and can be performed only at the cost of a significant degree of uncertainty. Investigating a method that can predict fracture permeability in significantly less time and with an acceptable level of accuracy would be useful for the industry. Proppant can be viewed as an unconsolidated, heterogeneous (in size distribution) although well-sorted granular packing, and the different relationships that can predict the permeability of such systems may be utilized. Such interdependencies account for the frictional pressure drop caused by the proppant particles and often neglect the pressure drop caused by the surface of the apparatus. To extend the applicability of the various models, the combination of a theoretical model that accounts for the pressure drop caused by the fracture wall with those models would have clear practical advantages. For this reason, a new equation had to be developed that establishes the relationship between the fracture permeability and the so-called modified particle friction factor. Since the frictional pressure drop correlations usually contain porosity as a parameter, determining its value under reservoir conditions is essential and a new procedure had to be developed. Since the different relationships were developed under different conditions to those under which the proppant-packs are usually used, providing a solution to the issues of their wide range of application is the primary objective of this study.

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Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

Cited by 2 publications

References 51 publications

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

Semi-analytical approach to the determination of fracture permeability

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