Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network

Larestani, Aydin; Mousavi, Seyed Pezhman; Hadavimoghaddam, Fahimeh; Hemmati-Sarapardeh, Abdolhossein

doi:10.1016/j.petrol.2021.109315

Cited by 37 publications

(14 citation statements)

References 30 publications

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“…All trees are connected in a series and each tree tries to reduce the error of the previous tree as much as possible. Gradient algorithms are often slow to learn from data because of this sequential connection, but GBT outperforms classical ML approaches (22).…”

Section: Data Preprocessing and Development Of Predictive Modelsmentioning

confidence: 99%

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Akbulut¹

2022

Erciyes Med J

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Objective:The primary aim of this study was to use metagenomic next-generation sequencing (mNGS) data to identify coronavirus 2019 (COVID-19)-related biomarker genes and to construct a machine learning model that could successfully differentiate patients with COVID-19 from healthy controls. Materials and Methods:The mNGS dataset used in the study demonstrated expression of 15,979 genes in the upper airway in 234 patients who were COVID-19 negative and COVID-19 positive. The Boruta method was used to select qualitative biomarker genes associated with COVID-19. Random forest (RF), gradient boosting tree (GBT), and multi-layer perceptron (MLP) models were used to predict COVID-19 based on the selected biomarker genes. Results:The MLP (0.936) model outperformed the GBT (0.851), and RF (0.809) models in predicting COVID-19. The three most important biomarker candidate genes associated with COVID-19 were IFI27, TPTI, and FAM83A. Conclusion:The proposed model (MLP) was able to predict COVID-19 successfully. The results showed that the generated model and selected biomarker candidate genes can be used as diagnostic models for clinical testing or potential therapeutic targets and vaccine design.

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Section: Data Preprocessing and Development Of Predictive Modelsmentioning

confidence: 99%

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Akbulut¹

2022

Erciyes Med J

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“…6) Wireless sensors network [42], [43]. 7) Other significant technologies [44], [45], [46], [47], [48], [49]. Although smart/intelligent oil fields have obtained fruitful advances, it is worth noting that they are studied in CPS.…”

Section: Smart/intelligent Oil Fieldsmentioning

confidence: 99%

Metaverses-Based Parallel Oil Fields in CPSS: A Framework and Methodology

Wang

Cheng

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Aiming to provide a novel paradigm of oil fields, metaverses-based parallel oil fields are proposed in this article. Compared with the existing smart/intelligent oil fields in cyber-physical systems (CPS), parallel oil fields can take human factors into full consideration and expand the operation space to cyber-physical-social systems (CPSS), which can be regarded as the abstract and scientific explanation of metaverses. In the proposed parallel oil fields, there are three kinds of workers (human workers, digital workers, and robotic workers) coordinating to construct a more reliable and intelligent oil field. Furthermore, the framework and methodology of parallel oil fields are illustrated by parallel systems and the artificial systems, computational experiments, and parallel executions (ACP) approach. Based on the proposed framework, parallel oil fields are capable of generating a more trustworthy artificial system and guaranteeing the realization of the 6S (safety, security, sustainability, sensitivity, service, and smartness) goal. Finally, based on dynamometer cards, fault diagnosis of sucker rod pumping systems (SRPS) is investigated in parallel oil fields.

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“…In this work, a system based on a cloud for software fault prediction in real time is given [ 16 ]. For the first time, models estimating formation damage accurately in terms of permeability of damage during a water flooding operation were developed as relatively innovative intelligent models [ 17 ]. Perceptron of multiple layers and linear stepwise regression have been proposed for grading agarwood oil.…”

Section: Related Workmentioning

confidence: 99%

A Web-Based Model to Predict a Neurological Disorder Using ANN

et al. 2022

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Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person’s ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person’s ability to control emotions or to be social can result in demotivation which can severely affect the brain’s ability to perform optimally. One of the main causes of reliance and disability among older people worldwide is dementia. Often it is misunderstood which results in people not accepting it causing a delay in treatment. In this research, the data imputation process, and an artificial neural network (ANN), will be established to predict the impact of dementia. based on the considered dataset. The scaled conjugate gradient algorithm (SCG) is employed as a training algorithm. Cross-entropy error rates are so minimal, showing an accuracy of 95%, 85.7% and 89.3% for training, validation, and test. The area under receiver operating characteristic (ROC) curve (AUC) is generated for all phases. A Web-based interface is built to get the values and make predictions.

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Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network

Cited by 37 publications

References 30 publications

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Metaverses-Based Parallel Oil Fields in CPSS: A Framework and Methodology

A Web-Based Model to Predict a Neurological Disorder Using ANN

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