使用机器学习的数据驱动方法来预测深层煤床甲储的现场气体含量:来自井记录数据的见解
Qian Zhang1,2,3, Shuheng Tang1,2,3, Songhang Zhang1,2,3
1School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, PR China.
ACS omega
|February 3, 2025
概括
准确预测深层煤床甲 (CBM) 在现场气体含量对于资源回收至关重要. 一个结合灰色关系分析,遗传算法和反向传播神经网络的新型模型显著提高了深度CBM的预测准确性.
科学领域:
- 地球科学 地球科学 地球科学
- 地质地质地质地质地质地
- 石油工程是石油工程中的一个.
背景情况:
- 现场气体含量对于煤床甲 (CBM) 开采和回收至关重要.
- 深度CBM资源提供了巨大的潜力,但由于复杂的地质,在获得现场气体含量数据方面面临挑战.
研究的目的:
- 开发一种高效准确的方法,利用井记录数据预测深层CBM的现场气体含量.
- 集成多个算法来克服传统预测模型的局限性.
主要方法:
- 通过将灰色关系分析 (GRA) 和遗传算法 (GA) 与反向传播神经网络 (BPNN) 集成,开发了一种新的预测模型.
- 使用GRA来确定BPNN的最佳输入参数.
- 使用GA优化BPNN的初始权重和值.
主要成果:
- 与独立的BPNN模型相比,组合的GRA-GA-BPNN模型显示出更高的性能.
- 与BPNN模型相比,平均平方误差 (MSE) 降低了77.60%.
- 验证了模型的可靠性,在宁武盆地的深深CBM井中,平均相对误差仅为4.62%.
结论:
- 该GRA-GA-BPNN模型有效地提高预测准确性和稳定性深 CBM 在现场气体含量.
- 该模型表现出高稳定性和强大的概括能力,减少对实验测量的依赖.
- 这种方法对于深入的CBM资源评估和开发具有重要的实际应用价值.
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