海鸥优化算法在四川盆地渝西区块 
  <strong>H</strong>井区页岩气储层最优化测井解释中的应用

doi:10.11867/j.issn.1001-8166.2020.046

地球科学进展 ›› 2020, Vol. 35 ›› Issue (7): 761 -768. doi: 10.11867/j.issn.1001-8166.2020.046

新学科新技术新发现上一篇

海鸥优化算法在四川盆地渝西区块 H井区页岩气储层最优化测井解释中的应用

陈愿愿 ¹(

), 杨晓 ¹, 邓小江 ¹, 王小兰 ¹, 何奇 ¹, 程莉莉 ¹, 陈科贵 ²

^1.东方地球物理公司西南物探研究院，四川成都 610213
^2.西南石油大学，四川成都 610500

收稿日期:2020-03-02 修回日期:2020-05-13 出版日期:2020-08-20
基金资助:
国家自然科学基金项目“四川盆地油钾兼探的地球物理评价方法研究”(41372103);四川省科技计划项目“四川盆地深层钾盐勘探开发评价研究”(2019YJ0312)

Application of Seagull Optimization Algorithm Log Interpretation to Shale Gas Reservoir of Well H in Sichuan Basin Yuxi Block

Yuanyuan Chen ¹( ), Xiao Yang ¹, Xiaojiang Deng ¹, Xiaolan Wang ¹, Qi He ¹, Lili Cheng ¹, Kegui Chen ²

^1.Southwest Geophysical Research Institute of BGP CNPC, Chengdu 610213, China
^2.School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China

Received:2020-03-02 Revised:2020-05-13 Online:2020-08-20 Published:2020-08-20
About author:Chen Yuanyuan (1988-), female, Chengdu City， Sichuan Province， Engineer. Research areas include logging interpretation and seismic geology comprehensive research. E-mail: chenyy_wt@cnpc.com.cn
Supported by:
the National Natural Science Foundation of China “Research on the geophysical evaluation method of oil and potassium simultaneous exploration in the Sichuan Basin”(41372103);The Sichuan Science and Technology Plan Project “Research on the evaluation and development of deep potassium salt in the Sichuan Basin”(2019YJ0312)

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页岩气储层矿物类型多样，常规的测井解释方法难以建立储层参数体积模型。最优化测井可以有效地评价多矿物组成的复杂岩性油气藏，但该方法的关键是优化算法的选择。利用最新提出的海鸥优化算法对四川盆地渝西区块H井区页岩气储层进行了矿物、物性参数等最优化测井解释，并与遗传算法和遗传—复合形混合算法进行了对比。结果显示，海鸥优化算法的最优化测井解释与岩心分析资料吻合较高、误差小、计算速度较快，弥补了遗传算法过早收敛、容易陷入局部最优以及遗传—复合形混合算法需要二次优化、搜索速度较慢等不足。这为海鸥优化算法在其他地区页岩气储层评价中的应用提供了借鉴。

关键词: 页岩气, 最优化测井解释, 海鸥优化算法, 搜索能力, 岩心资料

Due to the diversity of mineral types in shale gas reservoirs, it is difficult to establish reservoir parameter volume model by conventional log interpretation methods. The optimization log interpretation method can evaluate complex lithology reservoirs effectively, and the key is optimization algorithm. With the newly proposed seagull optimization algorithm method, we calculate the mineral and physical parameters of shale gas reservoir in Well H of Yuxi block, Sichuan Basin, and compare with the genetic algorithm and the genetic algorithm-complex hybrid algorithm. It shows that calculation results of seagull optimization algorithm optimization log interpretation match well with core analysis data, and calculation error is small, calculation speed is fast. Seagull optimization algorithm also makes up for the shortcomings of premature convergence and easy to fall into local optimization of genetic algorithm, the need for secondary optimization and slow search speed of genetic-complex hybrid algorithm. It provides a reference for the application of seagull optimization algorithm in other shale gas reservoirs regions.

Key words: Shale gas, Optimization log interpretation, Seagull optimization algorithm, Search ability, Core data

中图分类号:

P618.13

图1 海鸥迁徙和攻击方式示意图

Fig.1 Migration and attacking behaviors of seagulls

图2 海鸥迁徙行为和攻击行为示意图

Fig.2 Movement and attacking behaviors of seagulls

图3 页岩气多矿物组分模型示意图

Fig.3 Multi-mineral composition model of shale gas

图4 SOA测井解释流程图

Fig.4 Log interpretation flowchart of SOA

表1 矿物骨架成分和孔隙流体的测井响应特征值

Table 1 Logging response characteristic values of matrix components and pore fluid

成分种类

自然伽马/API

声波时差/(μs/ft)

补偿中子/%

密度/

(g/cm ³)

体积光电吸收界面/(b/cm ³)

铀/×10 ^-6

钍/(mg/L)

钾/%

电阻率/(Ω·m)

石英

长石

220

55.50

69.00

-2.00

2.65

2.62

4.79

7.50

4.79

7.21

1.10

10.50

5.00×10 ⁵

4.30×10 ⁶

白云石

方解石

黄铁矿

黏土

干酪根

孔隙（流体）

-1

250

2 050

43.50

48.00

39.20

49.50

135.00

189.00

1.00

-1.00

-3.00

35.00

0.30

100.00

2.87

2.71

5.00

2.50

1.30

1.00

8.00

13.77

82.30

18.00

0.22

0.30

9.00

13.77

9.00

300.00

0.36

20.00

40.00

0.30

5.00

0.58

50.00

8.00×10 ¹⁰

7.20×10 ⁷

4.00×10 ^-4

50.00

8.00×10 ¹²

0.05

表1 矿物骨架成分和孔隙流体的测井响应特征值

Table 1 Logging response characteristic values of matrix components and pore fluid

密度/

(g/cm ³)

石英

长石

220

55.50

69.00

-2.00

2.65

2.62

4.79

7.50

4.79

7.21

1.10

10.50

5.00×10 ⁵

4.30×10 ⁶

白云石

方解石

黄铁矿

黏土

干酪根

孔隙（流体）

-1

250

2 050

43.50

48.00

39.20

49.50

135.00

189.00

1.00

-1.00

-3.00

35.00

0.30

100.00

2.87

2.71

5.00

2.50

1.30

1.00

8.00

13.77

82.30

18.00

0.22

0.30

9.00

13.77

9.00

300.00

0.36

20.00

40.00

0.30

5.00

0.58

50.00

8.00×10 ¹⁰

7.20×10 ⁷

4.00×10 ^-4

50.00

8.00×10 ¹²

0.05

图5 H井 SOA的测井解释成果图

Fig.5 Log interpretation results of SOA in Well H

表2 H井各优化算法运行时间对比

Table 2 Comparison of operating time of each optimization algorithm in Well H

算法名称

运行时间/s

SOA

GA-CM

153

468

689

表2 H井各优化算法运行时间对比

Table 2 Comparison of operating time of each optimization algorithm in Well H

算法名称

运行时间/s

SOA

GA-CM

153

468

689

表3 H井各算法的矿物相对误差统计表

Table 3 Relative errors minerals in algorithms of Well H

矿物误差	SOA	GA	GA-CM
孔隙度	0.87	1.50	1.85
有机碳	0.12	2.36	1.89
石英	4.86	15.54	5.36
斜长石	0.24	3.23	2.75
方解石	1.67	8.27	5.37
白云石	2.73	12.86	6.78

表3 H井各算法的矿物相对误差统计表

Table 3 Relative errors minerals in algorithms of Well H

矿物误差	SOA	GA	GA-CM
孔隙度	0.87	1.50	1.85
有机碳	0.12	2.36	1.89
石英	4.86	15.54	5.36
斜长石	0.24	3.23	2.75
方解石	1.67	8.27	5.37
白云石	2.73	12.86	6.78

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