2022
DOI: 10.3389/feart.2022.918384
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XGBoost Formation Thickness Identification Based on Logging Data

Abstract: Based on research on the response mechanism of formation and reservoir response to logging curves, 12 logging curves were selected in combination with formation depth characteristics, and 4 algorithms were used to identify the formation and reservoir: logistic regression (LR), support vector machine (SVM), random forest (RF), and XGBoost. In the study block, 57 wells out of 60 wells were selected for training and learning, and the remaining three wells were used as prediction samples. The recognition of format… Show more

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Cited by 6 publications
(2 citation statements)
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“…The Yichang slope zone is located south of the Huangling anticline, the middle of the Yangtze Craton, and is bordered by the Qinling-Dabie orogenic belt to the north and the Xuefeng orogenic belt to the south [24][25][26] (Figure 1). During the Sinian (Ediacaran) and Cambrian periods, the tectonic movement and sedimentary environment changed drastically in southern China.…”
Section: Geological Settingmentioning
confidence: 99%
“…The Yichang slope zone is located south of the Huangling anticline, the middle of the Yangtze Craton, and is bordered by the Qinling-Dabie orogenic belt to the north and the Xuefeng orogenic belt to the south [24][25][26] (Figure 1). During the Sinian (Ediacaran) and Cambrian periods, the tectonic movement and sedimentary environment changed drastically in southern China.…”
Section: Geological Settingmentioning
confidence: 99%
“…13 It extracts knowledge from data and uses computers to obtain intrinsic information from the given data and provides a reference and basis for solving basic scientific and engineering problems. ML has a variety of different algorithms and is widely used in food safety, 14 geography, 15 medicine, 16 finance 17 and other fields. Furthermore, due to the ability of neural networks to efficiently approximate arbitrary functions, artificial neural networks and adaptive collocation strategies are used to solve boundary value problems for second order partial differential equations, so as to increase the robustness of the neural network approximation and result in significant computational savings.…”
Section: Introductionmentioning
confidence: 99%