A novel estimation method for capillary pressure curves based on routine core analysis data using artificial neural networks optimized by Cuckoo algorithm – A case study

Jamshidian, Majid; Zadeh, Mostafa Mansouri; Hadian, Mohsen; Moghadasi, Ramin; Mohammadzadeh, Omid

doi:10.1016/j.fuel.2018.01.099

Cited by 14 publications

(9 citation statements)

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“…Furthermore, an ANN approach with multilayer perceptron (MLP) structure and feed-forward propagation was applied in Jamshidian et al ( 2018 ) to estimate the capillary pressure curves for a target reservoir. The ANN method was optimized by adopting the cuckoo optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-022-06917-z.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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“…The cuckoo optimization algorithm has been introduced in Rajabioun. 25 It has been implemented and modified for various engineering optimization problems, such as proportional–integral–derivative (PID) controller, 25 machining processes, 26–28 replacement of obsolete components with crisp data, 10 optimal warranty period, 29 energy dispatch, 30 cloud computing, 31 fluids distribution, 32 and system reliability. 33 In this article, the COA is adapted for the replacement of obsolete components.…”

Section: Approach Descriptionmentioning

confidence: 99%

Optimal replacement strategy of obsolete industrial components under fuzzy data

Mellal

Williams

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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Competitiveness and rapid technological advances lead to the obsolescence of household items and industrial components. The challenge to people and industrial firms is to be equipped with the latest items, but at the lowest cost. During the last decade, several works have investigated various replacement strategies in order to optimally replace the obsolete industrial components. This article introduces the first work of the literature addressing a replacement strategy in case of fuzzy data on the components. The approach is based on fuzzy logic and the cuckoo optimization algorithm. Two case studies are illustrated for highlighting the applicability of the approach proposed.

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“…In recent years, companies have begun using machine learning techniques to combat several challenges and issues in data processing and handling in various oil and gas activities such as reducing risk factors and cost of maintenance [3]. In current literature, machine learning has been used to predict the experimental capillary pressure data points obtained from centrifugal and mercury injection tests with varying results [4][5][6]. For the case that did use centrifugal data, correlations were not applied to the experimental results to obtain a complete capillary pressure curve.…”

Section: Introductionmentioning

confidence: 99%

Prediction of centrifuge capillary pressure using machine learning techniques

et al. 2023

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In current literature in the petroleum industry, machine learning has been used to predict capillary pressure only on the centrifugal data points and not the complete capillary pressure curves generated from existing correlations after analysis. This paper will present novel information that will benefit the petroleum industry as it shows machine learning techniques can be used to obtain the complete capillary pressure curve which is the end goal in undertaking an SCAL centrifuge experiment. This research involves testing core samples using a centrifuge set up to produce capillary pressure data points. Then, using a commercial SCAL interpretation software, the collected data is utilized to generate complete capillary pressure curves based on developed literature correlations. RCAL data for the core samples is also obtained to be used with the machine learning techniques. The machine learning models are then applied to the collected data to predict the capillary pressure curves. Optimization of the different machine learning techniques is done to improve the predictions. The results show the machine learning techniques perform very well on the validation set after being trained on the training set. The machine learning models also provide reasonable prediction of the complete capillary pressure curves on the testing data set. Changing of the machine learning technique parameters also shows the effect on the overall precision and the improvements that can be made. Further research can be done to see the effectiveness of using machine learning techniques to predict other SCAL properties such as relative permeability. This can then greatly reduce the time needed to obtain these extremely important properties for reservoir characterization.

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A novel estimation method for capillary pressure curves based on routine core analysis data using artificial neural networks optimized by Cuckoo algorithm – A case study

Cited by 14 publications

References 50 publications

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Optimal replacement strategy of obsolete industrial components under fuzzy data

Prediction of centrifuge capillary pressure using machine learning techniques

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