基于NOA-LSTM-FCNN方法的引导钻井井道轨迹预测
Yi Gao1,2, Na Wang3, Fei Li4,5
1Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi'an Shiyou University, Xi'an, 710065, China. gy@xsyu.edu.cn.
Scientific reports
|February 12, 2025
概括
一种新的NOA-LSTM-FCNN方法在复杂的地质学中提高了井口轨迹预测的准确性. 这种方法通过优化使用新型优化算法 (NOA) 的长短期记忆 (LSTM) 和完全连接的神经网络 (FCNN) 模型来增强转向钻探.
科学领域:
- 石油工程是石油工程中的一个.
- 地质工程是地质工程.
- 人工智能在地球科学中的应用
背景情况:
- 精确的钻井轨迹预测对于高效的钻井操作至关重要,特别是在复杂的地质构造中.
- 现有的方法往往难以应对在定向钻探过程中遇到的复杂地质条件.
研究的目的:
- 提出一种新的预测方法来指导钻井井口轨迹,克服复杂地质条件下的局限性.
- 通过使用先进的机器学习技术,提高钻井轨迹预测的准确性和适应性.
主要方法:
- 一个混合模型,将长短期内存 (LSTM) 结合起来,用于数据依赖性提取和完全连接的神经网络 (FCNN) 进行特征提取.
- 新型优化算法 (NOA) 的集成,用于LSTM-FCNN模型的超参数优化.
- 使用井偏角数据对传统机器学习 (LR,SVM,BP) 和深度学习 (CNN,LSTM,GRU) 方法进行验证.
主要成果:
- 与现有技术相比,拟议的NOA-LSTM-FCNN方法显示出优越的预测性能.
- 观察到R2评估指数的显著改善,表明预测准确度提高.
- 该方法在各种钻井轨迹数据集和复杂的地质条件中显示出强大的适应性.
结论:
- 在具有挑战性的地质环境中,NOA-LSTM-FCNN方法为井口轨迹预测提供了强大而准确的解决方案.
- 这种方法有效地提高了方向钻探能力,导致更高效,更精确的井位置.
- 该研究强调了将先进的人工智能技术集成到解决石油和天然气行业复杂问题的潜力.
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