Petroleum Reservoir Evaluation and Development ›› 2021, Vol. 11 ›› Issue (5): 730-735.doi: 10.13809/j.cnki.cn32-1825/te.2021.05.010
• Offshore Oil andGas Exploration and Development • Previous Articles Next Articles
LI Wei1(),TANG Fang1,HOU Boheng1,QIAN Yin2,CUI Chuanzhi2(
),LU Shuiqingshan2,WU Zhongwei2
Received:
2021-04-26
Online:
2021-10-26
Published:
2021-10-12
Contact:
CUI Chuanzhi
E-mail:liwei1@cnooc.com.cn;ccz2008@126.com
CLC Number:
LI Wei,TANG Fang,HOU Boheng,QIAN Yin,CUI Chuanzhi,LU Shuiqingshan,WU Zhongwei. A method for oil recovery prediction of sandstone reservoirs in the eastern South China Sea based on neural network[J].Petroleum Reservoir Evaluation and Development, 2021, 11(5): 730-735.
Table 1
Training samples of neural network regression"
油藏 名称 | 含油面积 (km2) | 油层厚度 (m) | 孔隙度 (%) | 含油饱和度 (%) | 地层压力 (MPa) | 流度 (10-3 μm2/mPa·s) | 井网密度 (口/km2) | 渗透率 (10-3 μm2) | 地质储量 (104 m3) | 采收率 (%) |
---|---|---|---|---|---|---|---|---|---|---|
N1 | 7.2 | 3.5 | 15.8 | 61.0 | 28.6 | 822.1 | 0.98 | 271.3 | 164 | 76.5 |
N2 | 7.5 | 6.4 | 16.2 | 63.4 | 29.0 | 737.9 | 0.79 | 317.3 | 368 | 67.8 |
N3 | 8.9 | 2.0 | 16.5 | 61.1 | 29.0 | 656.5 | 0.13 | 282.3 | 134 | 64.5 |
N4 | 7.8 | 2.9 | 22.2 | 63.6 | 18.5 | 196.2 | 0.14 | 1 436.4 | 305 | 47.9 |
N5 | 5.5 | 5.2 | 15.4 | 61.6 | 29.2 | 534.2 | 1.09 | 192.3 | 193 | 73.4 |
N6 | 9.8 | 5.6 | 21.6 | 60.6 | 14.3 | 138.5 | 1.96 | 637.0 | 681 | 61.9 |
N7 | 6.4 | 2.8 | 19.9 | 46.5 | 16.0 | 143.5 | 3.21 | 660.0 | 149 | 62.2 |
N8 | 3.7 | 3.4 | 20.5 | 53.2 | 16.4 | 123.7 | 2.70 | 569.0 | 127 | 51.9 |
N9 | 10.9 | 5.6 | 21.3 | 75.8 | 20.1 | 345.4 | 2.11 | 1 088.0 | 912 | 67.0 |
| | | | | | | | | | |
N50 | 2.6 | 3.3 | 23.0 | 63.2 | 20.2 | 651.0 | 0.50 | 2 929.7 | 129 | 44.4 |
Table 3
Model test sample data"
油藏 名称 | 含油面积 (km2) | 油层厚度 (m) | 孔隙度 (%) | 含油饱和度 (%) | 地层压力 (MPa) | 流度 (10-3 μm2/mPa·s) | 井网密度 (口/km2) | 渗透率 (10-3 μm2) | 地质储量 (104 m3) | 采收率 (%) |
---|---|---|---|---|---|---|---|---|---|---|
H1 | 4.9 | 3.4 | 18.3 | 53.1 | 17.1 | 75.5 | 2.66 | 2 393.7 | 155 | 46.97 |
H2 | 5.2 | 4.4 | 24.2 | 75.6 | 18.2 | 369.9 | 4.42 | 3 587.7 | 402 | 53.87 |
H3 | 8.2 | 4.1 | 23.7 | 73.3 | 20.2 | 435.5 | 3.02 | 2 526.1 | 559 | 72.30 |
H4 | 9.1 | 4.3 | 25.0 | 71.8 | 22.3 | 534.2 | 3.13 | 3 098.3 | 668 | 69.72 |
H5 | 10.4 | 10.5 | 21.6 | 66.1 | 24.2 | 439.9 | 5.98 | 1 659.8 | 1 485 | 69.85 |
H6 | 2.4 | 2.0 | 21.9 | 58.8 | 19.6 | 284.3 | 1.60 | 1 648.7 | 60 | 64.56 |
Table 4
Comparison of results predicted by different models"
油藏 名称 | 实际采收率 (%) | 神经网络预测值(%) | 相对误差 (%) | 支持向量机回归预测值(%) | 相对误差(%) | 线性回归预测值(%) | 相对误差(%) |
---|---|---|---|---|---|---|---|
H1 | 46.97 | 47.23 | 0.55 | 51.77 | 10.22 | 49.60 | 5.60 |
H2 | 53.87 | 58.43 | 8.46 | 56.97 | 5.75 | 60.72 | 12.72 |
H3 | 72.30 | 63.71 | 11.88 | 56.10 | 22.41 | 60.00 | 17.01 |
H4 | 69.72 | 71.46 | 2.50 | 49.99 | 28.30 | 64.38 | 7.66 |
H5 | 69.85 | 73.55 | 5.30 | 64.42 | 7.77 | 77.84 | 11.44 |
H6 | 64.56 | 59.33 | 8.10 | 58.23 | 9.80 | 55.22 | 14.47 |
Table 6
Results of variance analysis"
分析项 | 偏差平方和 | 自由度 | F值 | F临界值 | 显著性 | 分析项 | 偏差平方和 | 自由度 | F值 | F临界值 | 显著性 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
含油面积 | 124.00 | 3.00 | 2.03 | 9.28 | 不显著 | 地层压力 | 61.00 | 3.00 | 1.00 | 9.28 | 不显著 | |
油层厚度 | 3 324.00 | 3.00 | 54.60 | 9.28 | 显著 | 流度 | 852.00 | 3.00 | 14.00 | 9.28 | 显著 | |
孔隙度 | 174.00 | 3.00 | 2.86 | 9.28 | 不显著 | 井网密度 | 1 489.00 | 3.00 | 24.47 | 9.28 | 显著 | |
含油饱和度 | 131.00 | 3.00 | 2.15 | 9.28 | 不显著 | 渗透率 | 631.00 | 3.00 | 10.36 | 9.28 | 显著 |
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