使用井记录数据和机器学习来预测密集的水库岩石的孔径
Yawen He1,2, Hongjun Zhang3, Zhiyu Wu4,5
1Department of Geology, Northwest University, Xi'an, 710069, China.
Scientific reports
|April 16, 2025
概括
精确的孔径量化在紧密的水库对于石油和天然气勘探至关重要. 一种新的粒子优化-梯度提升决策树 (PSO-GBDT) 模型使用井记录数据显著提高了预测准确性.
科学领域:
- 石油地质科学 石油地质科学
- 机器学习应用 机器学习应用
- 储水库工程 储水库工程
背景情况:
- 精确的孔径量化对于优化石油和天然气勘探和生产在狭窄的水库中至关重要.
- 传统的孔隙性预测方法往往是昂贵的,低效的,缺乏精度.
- 传统技术的挑战限制了有效的勘探和开发活动.
研究的目的:
- 开发和验证一种先进的机器学习模型,用于在紧密的水库中精确量化孔径.
- 在成本,效率和准确性方面克服传统预测模型的局限性.
- 通过改进的孔隙性预测,增强石油和天然气勘探和生产战略.
主要方法:
- 机器学习算法的应用:渐变增强决策树 (GBDT),随机森林,XGBoost和多层感知器.
- 使用全面的井记录数据:声时间 (AC),口径 (CAL),补偿中子 (CNL),密度 (DEN),自然马 (GR),电阻 (RT) 和自发电位 (SP).
- 使用粒子优化 (PSO) 算法优化机器学习模型以提高预测性能.
主要成果:
- 粒子集群优化-渐变增强决策树 (PSO-GBDT) 模型在与其他评估模型相比,表现优越.
- 该PSO-GBDT模型的确定系数 (R2) 超过0.99,表明高预测准确度.
- 对独立井数据的验证证实了PSO-GBDT模型的稳定性和高精度.
结论:
- PSO-GBDT模型提供了一个非常准确和高效的解决方案,用于在紧密的水库中预测孔隙性.
- 这种先进的方法具有很大的潜力,可以提高石油和天然气勘探和生产的成功率和经济可行性.
- 这些发现突显了机器学习在水库表征和资源管理方面的变革性影响.
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