A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

Xie, Yunxin; Zhu, Chenyang; Hu, Ruoyu; Zhu, Zhengwei

doi:10.1007/s11004-020-09885-y

Cited by 29 publications

(6 citation statements)

References 22 publications

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“…Neural networks (NN), RF, extreme gradient boosting tree (XGBoost) algorithms, and one-versus-one support vector machines (OVO SVMs) are used to create machine learning (ML) [17]. 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].…”

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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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%

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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“…Yu et al (2021) compared GBDT, support vector machine, logistic regression, and decision tree models and found that the GBDT algorithm is superior to other algorithms. Xie et al (2021) proposed a coarse-tofine framework that combines outlier detection, multiclass classification with an extremely randomized treebased classifier is proposed to solve these issues. Comparisons are conducted with some baseline machine learning classifiers, namely random forest, gradient tree boosting, and xgboosting.…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent optimization algorithms can help solve this problem by automatically searching and optimizing the hyperparameter space to find the best combination of hyperparameters. In the field of lithology identification, some scholars have optimized the parameters of the machine learning model through intelligent optimization algorithm, thus significantly improving the prediction accuracy of lithology identification (Xie et al, 2023). Crow search algorithm is an optimization algorithm based on swarm intelligence.…”

Section: Introductionmentioning

confidence: 99%

A Real‐time Lithological Identification Method based on SMOTE‐Tomek and ICSA Optimization

DENG,

PAN,

et al. 2024

Acta Geologica Sinica (Eng)

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In petroleum engineering, real‐time lithology identification is very important for reservoir evaluation, drilling decisions and petroleum geological exploration. A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper. This method can effectively utilize downhole parameters collected in real‐time during drilling, to identify lithology in real‐time and provide a reference for optimization of drilling parameters. Given the imbalance of lithology samples, the synthetic minority over‐sampling technique (SMOTE) and Tomek link were used to balance the sample number of five lithologies. Meanwhile, this paper introduces Tent map, random opposition‐based learning and dynamic perceived probability to the original crow search algorithm (CSA), and establishes an improved crow search algorithm (ICSA). In this paper, ICSA is used to optimize the hyperparameter combination of random forest (RF), extremely random trees (ET), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) models. In addition, this study combines the recognition advantages of the four models. The accuracy of lithology identification by the weighted average probability model reaches 0.877. The study of this paper realizes high‐precision real‐time lithology identification method, which can provide lithology reference for the drilling process.

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“…Thermosensitive tactile recognition for mining robot refers to the development of technologies that enable mining robots to perceive and understand their environment using thermosensitive tactile sensors. [ 1 ] This technology has the potential to improve the safety, efficiency, and accuracy of mining operations by enabling robots to navigate and operate in harsh and hazardous environments with minimal human intervention. [ 2 ] Achieving high perceptual sensitivity to heat is heavily influenced by the interactions established between materials in contact.…”

Section: Introductionmentioning

confidence: 99%

Skin‐Inspired Thermosensitive Tactile Sensor Based on Thermally Conductive and Viscous Interface Composites for Rocks

et al. 2023

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Achieving high thermal conductivity and exceptional interfacial adhesion simultaneously in thermosensitive tactile recognition sensors poses a significant challenge. A copolymer, poly([[(butylamino)carbonyl]oxy]ethyl‐ester)‐co‐polydimethylsiloxane (referred to as PP), is synthesized and subsequently complexed with alumina particles coated with liquid metal (LMAl2O3) to prepare a composite material called PP/LMAl2O3 with high thermal conductivity and strong interfacial adhesion to address this challenge. The best thermal conductivity (4.43 W m−1 K−1), electrical insulation (10−6–10−7 S m−1), and adhesion properties derived from hydrogen bonding (1316 N m−2) are obtained by adjusting the volume fraction of PP and LMAl2O3 in PP/LMAl2O3. PP/LMAl2O3 with high thermal conductivity and high interface adhesion can efficiently transfer heat between thermal flux sensors and the objects being sensed, reliably detecting small thermal flux variations and ensuring accurate thermal flux measurements. In this study, PP/LMAl2O3 is used to make up thermosensitive tactile sensor. Surprisingly, PP/LMAl2O3 demonstrates high thermal signal sensitivity for tactile recognition applications, allowing the smart thermosensitive tactile sensor system to distinguish unknown rock materials even in the dark. Overall, PP/LMAl2O3 may function as a fundamental material in thermosensitive tactile sensors for lithology identification.

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A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

Cited by 29 publications

References 22 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

A Real‐time Lithological Identification Method based on SMOTE‐Tomek and ICSA Optimization

Skin‐Inspired Thermosensitive Tactile Sensor Based on Thermally Conductive and Viscous Interface Composites for Rocks

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