石油与天然气地质 ›› 2023, Vol. 44 ›› Issue (1): 226-237.doi: 10.11743/ogg20230119
• 方法技术 • 上一篇
收稿日期:
2022-06-01
修回日期:
2022-11-03
出版日期:
2023-02-01
发布日期:
2023-01-13
通讯作者:
段太忠
E-mail:liuyf.syky@sinopec.com;duantz.syky@sinopec.com
第一作者简介:
刘彦锋(1986—),男,博士、副研究员,油气田开发地质。E-mail: 基金项目:
Yanfeng LIU(), Taizhong DUAN(), Yuan HUANG, Wenbiao ZHANG, Meng LI
Received:
2022-06-01
Revised:
2022-11-03
Online:
2023-02-01
Published:
2023-01-13
Contact:
Taizhong DUAN
E-mail:liuyf.syky@sinopec.com;duantz.syky@sinopec.com
摘要:
油气藏勘探开发逐步向深层化、复杂化方向发展,观测数据不足、分辨率低等资料难题突显,传统的地质建模方法无法适应技术需求。以深度学习为代表的智能化地质建模方法可以充分整合多尺度、多维度的数据信息以及专家认识,是地质建模技术发展的重要方向。在综合分析地层沉积模拟和深度学习地质建模技术优缺点的基础上,探索形成了沉积过程模拟驱动的深度学习地质建模方法。首先,基于综合地质分析开展沉积正演模拟,分析参数不确定性,通过参数扰动形成大规模地质模型作为训练样本库;其次,利用条件化生成对抗网络学习样本库中蕴含的地质模式和规律,其中生成网络以井-震等条件数据作为输入、地质模型作为输出;最后,利用训练后生成网络在实际条件数据上的应用,得到目标区块的地质模型。通过在四川盆地普光气藏主力区块典型地质剖面的测试应用,该方法的可行性得到了验证,并分析了训练样本库大小对模拟结果的影响。沉积模拟和深度学习相结合,弥补了训练样本不足的缺陷,间接实现了知识驱动的深度学习地质建模,具有重要的推广意义。
中图分类号:
表1
碳酸盐岩沉积模拟主要参数类型及分析方法"
参数类型 | 具体参数 | 分析方法 |
---|---|---|
可容空间类 | 构造沉降曲线 | 地层回剥法,地震资料解释 |
海平面曲线 | Haq曲线,岩石的水深指示曲线 | |
初始地形 | 标志层厚度,沉积微相对水深指示意义 | |
沉积物供给类 | 沉积物供给速率,最大产率 | 地层厚度,井上地层厚度序列 |
沉积物供给集中度,透光带厚度 | 物源方向初始地层厚度变化程度,高能相带厚度 | |
沉积物供给成分比例,产率下降系数 | 观测数据中岩性比例,岩性变化频率 | |
沉积物搬运剥蚀类 | 势能扩散系数 | 沿沉积物搬运方向的地层厚度变化 |
扩散系数变化因子 | 岩性纵向变化程度 | |
动能对流系数 | 沿沉积物搬运方向的岩性变化程度 | |
流体动能分布类 | 波浪能 | 水体流速相对大小 |
风能 | 高能和低能相带分布的突变程度 | |
地形消浪能 | 沉积相对水体能量的指示意义 |
表2
沉积反演模拟参数区间"
序号 | 参数 | 最小值 | 最大值 | 序号 | 参数 | 最小值 | 最大值 |
---|---|---|---|---|---|---|---|
1 | AmpSeaL1 | 40.000 | 120.000 | 22 | KEng2 | 1.000 | 3.000 |
2 | PerdSeaL1 | 500.000 | 1500.000 | 23 | KEng3 | 0.500 | 1.500 |
3 | AmpSeaL2 | 20.000 | 60.000 | 24 | bEng1 | 0.500 | 1.500 |
4 | PerdSeaL2 | 80.000 | 150.000 | 25 | bEng2 | 0.040 | 0.120 |
5 | PerdSeaL3 | 20.000 | 60.000 | 26 | bEng3 | 10.000 | 30.000 |
6 | LinEuElev3 | -55.000 | -10.000 | 27 | pPr1 | 0.350 | 1.000 |
7 | SubsidenceScale1 | 0.500 | 1.500 | 28 | KV1 | 5.000 | 15.000 |
8 | PDX2 | 40.000 | 120.000 | 29 | KV2 | 17.000 | 52.000 |
9 | PDX1 | 800.000 | 2200.000 | 30 | KV3 | 12.000 | 35.000 |
10 | KDX1 | 5.000 | 15.000 | 31 | KW1 | 0.007 | 0.022 |
11 | KDX2 | 25.000 | 750.000 | 32 | KW2 | 0.012 | 0.040 |
12 | Apord1 | 2.500 | 7.500 | 33 | KW3 | 0.007 | 0.022 |
13 | Apord2 | 15.000 | 45.000 | 34 | kR1 | 0.025 | 0.000 |
14 | Apord3 | 7.500 | 22.000 | 35 | kR2 | 0.020 | 0.060 |
15 | Kpord3 | 0.010 | 0.030 | 36 | kR3 | 0.007 | 0.020 |
16 | Kpord1 | 0.010 | 0.030 | 37 | kH1 | 0.030 | 0.090 |
17 | Kpord2 | 0.020 | 0.070 | 38 | kH2 | 0.100 | 0.300 |
18 | Wpord1 | 30.000 | 90.000 | 39 | kH3 | 0.030 | 0.090 |
19 | Wpord2 | 15.000 | 45.000 | 40 | WaveBase1 | 20.000 | 60.000 |
20 | Wpord3 | 35.000 | 100.000 | 41 | KL1 | 0.002 | 0.007 |
21 | KEng1 | 0.020 | 0.060 |
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