Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Lu, Shengyan; Li, Moujie; Luo, Na; Wang, He; He, Xiaohong; Gan, Changjian; Deng, Rui

doi:10.1155/2022/1640096

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…A coarse-to-fine architecture that incorporates outlier detection, multi-class classification, and a tree-based classifier suggested to identify the lithology using two actuals well logging data sets [19]. A hybrid framework consisting of artificial neural networks and hidden Markov models (ANN-HMM) was suggested for the classification of the lithological sequence [11]. They thoroughly evaluated the effectiveness of the suggested classifier using a combination of extreme gradient boosting (XGBoost) and Bayesian optimization (BO) [18].…”

Section: Page|78mentioning

confidence: 99%

“…In contrast to the ANN, SVM, AdaBoost, and RF classifiers, the performance of the gradient boosting decision tree (GBDT) classifier was demonstrated and confirmed [22]. A Gray Wolf Optimization Algorithm (GWO-SVM)-based automatic identification system for lithology logging has been presented [11]. So far it becomes vital to assess the machine learning model's propensity to forecast the kind of lithology under various circumstances.…”

Section: Page|78mentioning

confidence: 99%

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Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

hamada,

M. Al-khudafi,

A. Al-Sharifi

et al. 2024

Journal of Petroleum and Mining Engineering

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This study aims to assess the effectiveness of several decision tree machine techniques for identifying formation lithology of complex carbonate reservoir rocks in Gamal oil field. A total of 20966 log data points from four wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep lateral log, shallow lateral log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. Several typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logistic model trees, Hoeffding Tree were assessed using well logging data for formation lithology prediction. The obtained results show that the random forest model, out of the proposed decision tree models, performed best at lithology identification, with precession, recall, and F-score values of 0.913, 0.914, and 0.913, respectively. Random trees came next. with average precision, recall, and F1-score of 0.837, 0.84, and 0.837, respectively, the J48 model came in third place. The Hoeffding Tree classification model, however, showed the worst performance. We conclude that boosting strategies enhance the performance of tree-based models. Evaluation of prediction capability of models is also carried out using different datasets.

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Section: Page|78mentioning

confidence: 99%

Section: Page|78mentioning

confidence: 99%

Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

hamada,

M. Al-khudafi,

A. Al-Sharifi

et al. 2024

Journal of Petroleum and Mining Engineering

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“…Once they send a warning signal, the population needs to escape immediately and move to another safe place for food [52]. The basic parameter setting in this algorithm is as follow: (iii) GWO By analyzing and summarizing the population mechanism of wolves, Mirjalili S proposed the grey wolf optimizer algorithm in 2013 [53][54][55]. Similar to other natureinspired heuristic algorithms, the GWO also starts with a set of randomly generated locations.…”

Section: Svm Optimized By Multiple Heuristic Algorithmsmentioning

confidence: 99%

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Zhong

2023

Sustainability

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Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.

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“…Machine learning technology has been widely applied in lithology classification, with its excellent feature mining and data fitting ability. Examples include extreme learning machines [44,45], logistic regression [46,47], back-propagation neural networks [48,49], support vector machines [50][51][52], and multi-layer perceptrons [53,54]. The commonly used lithologic classification models can be divided into three categories, the space vector type, neural network type, and linear type.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Cited by 6 publications

References 16 publications

Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

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