在油田产量预测中应用集成学习模型的研究
MingCheng Ni1, XianKang Xin1,2,3, GaoMing Yu1,2,3
1School of Petroleum Engineering, Yangtze University, Wuhan, Hubei 430100, China.
ACS omega
|October 30, 2023
概括
本研究介绍了一种集成学习模型,用于稳定的石油产量预测. 这种新的方法通过分析内部曲线特征,准确地预测长期生产趋势,优于传统模型.
科学领域:
- 石油工程是石油工程中的一个.
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 准确的石油产量预测对于有效的油田管理至关重要.
- 现有的多功能模型由于不同的场景条件而缺乏普遍适用性.
- 需要强大的预测方法,专注于内在生产数据.
研究的目的:
- 为石油生产提出一种新的时间序列预测方法.
- 开发一个综合学习模型,将线性和非线性结合起来.
- 通过专注于生产曲线动态来提高预测准确性和稳定性.
主要方法:
- 单一频谱分析 (SSA) 用于数据处理.
- 自动回归集成移动平均线 (ARIMA) 和Prophet模型的集成.
- 波形神经网络 (WNN) 用于预测的应用.
- 使用J油田 (2011-2021) 的历史数据进行验证.
主要成果:
- 综合模型在后期预测中表现稳定.
- 与单个模型和主流方法相比,综合模型显示波动减少.
- 预测准确性在早期阶段是可比的,但在后期阶段是优越的.
结论:
- 拟议的综合学习模型提供了稳定而准确的石油产量预测.
- 这种方法有效地捕捉了内部生产曲线的特征,从而改善了预测.
- 该模型为现有的预测技术提供了可靠的替代方案,特别是用于长期分析.
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