Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data

Hu, Yijie; Pan, Hao; Kong, Li; Liu, Jie; Huang, Zhen; Chen, Tao

doi:10.1088/1361-6501/ab4a45

Cited by 31 publications

(17 citation statements)

References 36 publications

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“…Compared with GBDT, LightGBM has the advantages of faster training efficiency, lower memory usage, higher accuracy, support for parallel and GPU computing and can meet the needs of large-scale data processing, which can overcome the traditional boosting algorithm's disadvantages in scalability and running speed. It is now widely used in classification, regression and sorting applications [44][45][46][47][48]. LightGBM adopts two methods, Gradient-based One-Side Sampling (GOSS) and Mutually Exclusive Feature Bundling (EFB), to improve the training and learning speed.…”

Section: Lightgbm Modelmentioning

confidence: 99%

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Guan

Zhang

et al. 2021

Remote Sensing

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In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS.

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Section: Lightgbm Modelmentioning

confidence: 99%

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Guan

Zhang

et al. 2021

Remote Sensing

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“…In this study, the importance type is “gain.” Different from the “split,” the “gain” measures the actual decrease in node impurity. The feature rankings of gain-based importance can be obtained after LightGBM fitting [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

“…LightGBM is an algorithm that has been successfully applied in the field of classification, ranking, and many other ML tasks [ 43 , 45 – 48 ]. It is an ensemble model based on a decision tree algorithm.…”

Section: Methodsmentioning

confidence: 99%

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model

Zhang

Liu

Qin

et al. 2020

Computational and Mathematical Methods in Medicine

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Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k -spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F -measure.

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“…The classification report for the RFC model (12 features) can be found in Table 7. By evaluating the precision information from Table 7, we noticed that the lowest value were computed for Dolomite (4) and Coal (10). A reason for such values could be the lack of representation of these lithofacies classes in the dataset.…”

Section: Lithofacies Prediction For the Norway Datamentioning

confidence: 98%

“…In [10], the authors proposed using embedded feature selection (EFS) and LightGBM to predict the permeability of a reservoir. Result of EFS was generated based on five features: DEPTH, AC, DEN, FMIT, and GR out of 22 features and was equal 0.9457 (R2).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

et al. 2021

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Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

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Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data

Cited by 31 publications

References 36 publications

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

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