机器学习增强了使用集成井记录和泥记录的形成压力预测
Jiwen Liang1, Ming Luo1, Wentuo Li1
1Hainan Branch of CNOOC (China) Co., Ltd, Haikou, 570100, Hainan, China.
Scientific reports
|July 3, 2025
概括
在石油工程中,准确预测高压形成压力至关重要. 使用井记录和泥记录数据的新综合模型,特别是IWM-GABP机器学习方法,显著提高了预测准确性,降低了钻井风险.
科学领域:
- 石油工程是石油工程中的一个.
- 地质科学地质科学是指地球科学.
- 机器学习应用 机器学习应用
背景情况:
- 准确预测异常高压形成压力是石油工程的一个关键挑战.
- 不准确的预测会导致安全事故,例如由于钻井安全密度窗口狭窄而导致泄漏和爆炸.
- 提高孔隙压力预测准确度对于安全高效的石油和天然气勘探至关重要.
研究的目的:
- 开发一个更准确的模型来预测形成孔隙压力.
- 为了研究各种井记录和泥记录参数和孔隙压力之间的相关性.
- 建立一个机器学习模型,整合井记录和泥记录数据,以提高预测准确度.
主要方法:
- 斯皮尔曼相关系数分析以确定孔隙压力和记录参数之间的关系.
- 开发一个集成的井记录和泥记录数据 (IWM) 机器学习模型.
- 传统模型与IWM模型的比较,特别是评估IWM-GABP (带有遗传算法的回传播神经网络) 和IWM-RBFN (辐射基函数网络) 模型.
主要成果:
- 孔隙压力与深度,比特重量和声传输时间等参数有不同的相关性.
- IWM-GABP模型实现了最高的预测准确度,超过96%.
- 与传统模型相比,IWM模型的预测准确度平均提高了8.32%,显著减少了预测错误.
结论:
- 集成的井记录和泥记录数据机器学习模型,特别是IWM-GABP,为形成压力预测提供了卓越的准确性.
- 建议使用IWM-GABP或基于IWM的反向传播神经网络模型,而不是IWM-RBFN模型来预测形成压力.
- 这项研究提供了一种强大的方法来提高形成压力预测的准确性,支持有效的现场油田开发.
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