深入洞察:使用时空卷积网络和科尔莫戈罗夫-阿诺德网络进行油井产量预测的高效混合模型
Yandong Hu1, Xiankang Xin2,3,4, Gaoming Yu1,5,6
1School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
Scientific reports
|March 11, 2025
概括
本研究引入了一种新的TCN-KAN模型,用于准确预测油井产量. 它通过捕捉复杂的数据动态和运营变化来提高预测准确度,以更好地管理资源.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 全球能源需求需要优化石油和天然气生产以实现可持续性.
- 准确的油井预测对于资源管理和运营决策至关重要.
- 传统模型和现有的混合网络在处理复杂的生产数据和操作变化方面存在局限性.
研究的目的:
- 开发一种新的混合模型,即时间卷积网络-科尔莫戈罗夫-阿诺德网络 (TCN-KAN),用于增强油井产量预测.
- 通过解决数据中的非线性和长期依赖性,提高生产预测的准确性和稳定性.
- 整合领域专业知识和先进的算法,以提供更敏感和实用的预测解决方案.
主要方法:
- 开发了一种混合TCN-KAN模型,集成时间卷积网络和科尔莫戈罗夫-阿诺德网络.
- 使用水库工程专业知识和斯皮尔曼相关性分析来减少维度的特征选择.
- 使用修改后的鱼优化算法 (WOA) 进行超参数调整,以提高模型的稳定性.
主要成果:
- TCN-KAN模型在捕捉非线性相互作用和长期时间依赖性方面表现出卓越的性能.
- 与其他测试模型相比,实现了最高的预测准确性.
- 在沃尔沃油田数据 (2008-2016年) 上验证,显示出显著的操作敏感性和实际价值.
结论:
- 在油井生产预测准确性和可靠性方面,TCN-KAN模型提供了显著的进步.
- 综合方法有效地处理复杂的生产数据和运营动态.
- 为优化油田管理和资源分配提供可操作的见解.
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