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Updated: May 9, 2025

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XGBoost算法通过模拟化遗传算法进行优化,用于碳酸盐水库的透性预测建模

Changbing Huang1, Xinyu Zhu2, Mingyu Lu3

  • 1School of Environment and Resources, Southwest University of Science and Technology, Mianyang, 621010, China. 1832873542@qq.com.

Scientific reports
|April 28, 2025
PubMed
概括

这项研究引入了一个新的透性预测模型,用于复杂的碳酸盐储库,使用混合XGBoost和模拟化遗传算法 (SA-GA). 该模型准确地预测了井日志的透性,改善了在具有挑战性的形成中进行石油和天然气勘探.

关键词:
碳酸盐岩石是一种碳酸盐岩石.遗传算法 遗传算法 遗传算法透性 透性的模拟的回火模拟在XGBoost中使用.

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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