在新foundland和拉布拉多的声音日志的交叉井机器学习预测
Bahare Zare1, Mohammad Mojammel Huque2, Lesley A James2
1Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada.
Scientific reports
|January 15, 2026
概括
使用非声波日志预测压缩缓慢 (DTCO) 可以节省成本并改善现场规划. XGBoost模型实现了准确的盲目交叉井预测,证明了无泄漏特征工程的有效性.
科学领域:
- 地质物理学 地质物理学
- 石化物理学 石化物理学
- 机器学习在地球科学中的应用
背景情况:
- 压缩速度缓慢 (DTCO) 对于地震解释和水库表征至关重要.
- 获取声音日志是昂贵的,可能会导致数据缺口.
- 从非声波日志预测DTCO提供了一个具有成本效益的替代方案和数据缺口填补解决方案.
研究的目的:
- 为了评估盲目交叉井DTCO预测,使用严格的无泄漏,只有特征的策略.
- 为了比较随机森林 (RF),极端梯度增强 (XGBoost) 和BiLSTM模型的性能.
- 确定推动成功跨井DTCO预测的关键因素.
主要方法:
- 开发了一个无泄漏的预测管道,使用非声波日志的因果滞后窗口,不包括声波数据.
- 实现了确定性深度调节,相对深度特征,多尺度深度衍生和等级聚合特征选择.
- 采用了时间意识验证,并比较了RF,XGBoost和BiLSTM模型家族.
主要成果:
- 使用20个预测器和10个样本滞后的调整XGBoost实现了R2的[公式:见文本],MAE的[公式:见文本]和RMSE的[公式:见文本]用于交叉井预测 (井1到井2).
- 在反向方向 (井2到井1) 的表现较低,表明井间分布的转移.
- 射频显示出竞争性表现,而BiLSTM在这些数据集上表现不佳.
结论:
- 严格的泄漏控制,深度感知特征工程和原则性的特征选择对于准确的交叉井DTCO预测至关重要.
- 像XGBoost这样的基于树的合集为伪声波日志预测提供了强大的,数据效率高的基线.
- 这种方法支持通过可靠的DTCO预测降低收购成本和加强现场规划.
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