ResNet和CWT融合:一种针对优化异质薄水库评估的新范式
Saima Akram1, Gulraiz Akhter1, Yonggang Ge2,3
1Department of Earth Sciences, Quaid-I-Azam University, Islamabad 45320, Pakistan.
ACS omega
|February 5, 2024
概括
这项研究结合了连续波波变换 (CWT) 和机器学习 (ML) 来描述具有挑战性的薄砂储层. 这种创新方法提高了水库描述,并更准确地确定了新的勘探目标.
科学领域:
- 地质物理学 地质物理学
- 石油地质科学 石油地质科学
- 机器学习应用 机器学习应用
背景情况:
- 水库的表征面临地质异质性,薄砂层和有限的地震/井记录数据的挑战.
- 传统方法在缺少数据的情况下扎,这阻碍了对水库属性的精确估计.
- 薄而异质的水库,如下戈鲁形成的水库,需要先进的特征化技术.
研究的目的:
- 在巴丁地区的下戈鲁岩层中描述薄而异质的潜在沙子.
- 克服低地震分辨率和缺少井日志数据的局限性,以改善水库描述.
- 开发和应用一个集成的CWT和ML方法,以提高石化物理和弹性属性估计.
主要方法:
- 集成连续波形变换 (CWT) 进行高分辨率数据增强.
- 机器学习 (ML) 的应用,特别是残余神经网络 (ResNet),用于数据近似和属性预测.
- 使用CWT和ML来近似缺失的S波 (DTS) 数据并改善弹性和石质物理性质.
主要成果:
- 成功地近似了缺失的S波 (DTS) 数据,并增强了弹性/石化物理特性.
- 使用CWT和ResNet实现了薄而异质的沙体的高分辨率表征.
- 确定了一个有潜力的低风险井位置的道化游乐场.
结论:
- 综合CWT-ML方法有效地解决了薄水库表征常规方法的局限性.
- 通过这种方法获得的增强的石油弹性特性可靠地划定了复杂的水库特征.
- 该研究提供了一种强大的方法来优化水库描述,并指导在具有挑战性的地质环境中进行勘探.
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