一种规模序列法中对油气藏规模变化系数（<i>k</i>）改进的取值技术及应用实例

陈双玲; 于京都; 郑民; 王小娟; 吴晓智; 王建

doi:10.19509/j.cnki.dzkq.tb20230371

一种规模序列法中对油气藏规模变化系数（k）改进的取值技术及应用实例

doi: 10.19509/j.cnki.dzkq.tb20230371

1.
中国石油西南油气田分公司勘探开发研究院，成都 610041
2.
中国石油勘探开发研究院，北京 100083
3.
中国石油股份有限公司油气与新能源分公司，北京 100010

基金项目: 中国石油西南油气田分公司科技专项“四川盆地中西部地区致密气勘探开发理论及关键技术研究”课题1：“四川盆地中西部地区致密气成藏条件及物探关键技术研究”（2022ZD01-01）；中国石油勘探开发研究院科技专项“四川盆地陆相致密气资源潜力评价与计算”（101022hx0001b25004）

详细信息

作者简介:
陈双玲：E-mail：chenshuangling@petrochina.com.cn

通讯作者:
E-mail：yujingdu@petrochina.com.cn

中图分类号: P618.13
计量
- 文章访问数: 216
- PDF下载量: 19
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-03
- 录用日期: 2023-08-17
- 修回日期: 2023-08-15
- 网络出版日期: 2023-12-17

An improved method for determining the coefficient of resource scale variation (k) in reservoir size sequential analysis and its application case

1.
Exploration and Development Research Institute, PetroChina Southwest Oil & Gasfield Company, Chengdu 610041, China
2.
Research Institute of Petroleum Exploration & Development, Beijing 100083, China
3.
PetroChina Oil & Gas and New Energy Company, Beijing 100010, China

More Information

Author Bio:
E-mail：chenshuangling@petrochina.com.cn

Corresponding author: E-mail：yujingdu@petrochina.com.cn

摘要

摘要:
规模序列法基于Pareto定律，有效获取油气藏规模变化系数（k）的取值长期以来是该方法应用中的关键及难点，并制约了该方法的应用成效。通过求取已发现油气资源规模比，利用已发现油气资源可能具有的规模序列号，构建了一种对k的优化取值技术，主要包括：以资源规模序列号与k为坐标轴建立坐标系，根据已发现油气资源规模比做数据交汇，当不同规模比下的交汇数据点近似位于垂直于k轴的直线上时，该直线与k轴的交点即为k的一个解；进一步提出了在获取k及规模序列解集后，对解集进行优选定解的原则，以满足油气资源评价需求。对已发表文献中经典数据的分析表明，通过应用该技术可有效获取油气藏规模变化系数（k）的取值；并进一步构建了对川中金秋气区盐亭区块侏罗系沙溪庙组6号砂组天然气资源的应用实例，表明预测与实际拟合结果较好，评价结果符合当前盆地天然气勘探认识。该技术对地质经验依赖程度低、不需要设定分析步长、无复杂的行列式-矩阵运算环节，有效降低了在k取值过程中的主观性和计算强度，并实现了程序化，提高了分析时效性，可为规模序列法的深入应用提供帮助。
- 规模序列 /
- 油气藏规模变化系数（k） /
- 分析算法 /
- 资源评价 /
- 天然气
Abstract: Objective
Sequential reservoir size analysis based on the Pareto principle encounters challenges in accurately determining the coefficient 'k', which quantifies the gradient of resource scale variation, thereby limiting the approach's effectiveness.
Methods
This study proposes an optimized methodology for calculating 'k' by analyzing the scale ratio of discovered resources and employing possible sequential numbers. This approach involves establishing a cross-plot with sequential numbers and k-axis values based on calculated ratios, locating combinations of data points from various ratios that form approximately straight vertical lines against the k-axis, and identifying the intersection points on the k-axis as solutions for k. Further optimization principles are suggested to enhance result selection to meet resource assessment requirements.
Results
Reanalysis of classic datasets from academic literature validated the methodology's capability in accurately determining the coefficient (k). A case study of tight gas reservoirs, specifically the 6th group of Jurassic Shaximiao formations in the Yanting Block of the Jinqiu gas-producing area in the central Sichuan Basin, demonstrated favorable linear fitting results between forecasted and actual data. The calculated resource scale is in strong alignment with established tight gas exploration outcomes in the Sichuan Basin.
Conclusion
The proposed methodology reduces reliance on geological experience, eliminates the need for complex determinant models or matrix manipulations, and minimizes subjectivity and computational complexity in parameter selection. Additionally, the algorithm is available as a coded computer program, enhancing its practical efficiency and applicability in sequential reservoir size methods.
- reservoir size sequence /
- coefficient of the resource scale variation (k) /
- analytic algorithm /
- resource assessment /
- natural gas

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图 1 潜在序列号与油气藏规模变化系数（k）交会图（c=1时）

Figure 1. Cross-plot between the potential number with the sequence and the coefficient of the resource scale variation (k) under the condition of c=1

下载: 全尺寸图片幻灯片

图 2 潜在序列号与油气藏规模变化系数（k）交会图（c=3时）

Figure 2. Cross-plot between the potential number with the sequence and k under the condition of c=3

下载: 全尺寸图片幻灯片

图 3 对规模序列法中油气藏资源规模变化系数（k）及已知油气藏序列号分析的Python程序流程设计图

Figure 3. Design of the Python workflow for analyzing k and the sequential number for the reservoir size sequential method

下载: 全尺寸图片幻灯片

图 4 川中金秋气区盐亭区块（应用实例）盆地位置示意图

Figure 4. Location of the Yanting Block (application case) of the Jinqiu gas-producing area located in the central part of Sichuan Basin

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图 5 应用实例潜在序列号与气藏规模变化系数（k）交会图（c=3时）

Figure 5. Cross-plot between the potential number with the sequence and k under the condition of c=3 (application case)

下载: 全尺寸图片幻灯片

图 6 已知气藏规模预测值和实际值交会图

Figure 6. Cross-plot between forecast and actual values at the scale of gas resources

下载: 全尺寸图片幻灯片

图 7 应用实例天然气资源规模与规模序列号交会图

a. 气藏规模与序列号交会图；b. 累计气藏规模与序列号交会图

Figure 7. Acquired sequence of gas resources for the application case

下载: 全尺寸图片幻灯片

表 1 针对某算例c=1条件下潜在序列号对油气藏规模变化系数（k）的映射关系

Table 1. Correlations of the potential number with the sequence and the coefficient of the resource scale variation (k) under the condition of c=1 for a certain study case

潜在的油气资源规模序列号（y_b，y_c，y_d）	油气藏规模变化系数k
潜在的油气资源规模序列号（y_b，y_c，y_d）	k_b	k_c	k_d
2	1.2765	2.1173	2.4509
3	0.8054	1.3358	1.5464
4	0.6382	1.0586	1.2255
5	0.5497	0.9119	1.0556
6	0.4938	0.8191	0.9482
7	0.4547	0.7542	0.8730
8	0.4255	0.7058	0.8170
9	0.4027	0.6679	0.7732
···	···	···	···
注：c为发现最大油气藏所对应的潜在规模序列号；y_b，y_c，y_d分别是规模为61.567万，34.375万，27.277万t的油藏的潜在序列号，y_b，y_c，y_d均为正整数，且1<y_b<y_c<y_d；k_b，k_c，k_d均为油气藏规模变化系数；下同

下载: 导出CSV

表 2 针对某算例c=3条件下潜在序列号对油气藏规模变化系数（k）的映射关系

Table 2. Correlations of the potential number with the sequence and the coefficient of the resource scale variation (k) under the condition of c=3 for a certain study case

潜在的油气资源规模序列号（y_b，y_c，y_d）	油气藏规模变化系数k
潜在的油气资源规模序列号（y_b，y_c，y_d）	k_b	k_c	k_d
4	3.0755	5.1014	5.9053
5	1.7321	2.8729	3.3257
6	1.2765	2.1173	2.4509
7	1.0442	1.7321	2.0050
8	0.9021	1.4963	1.7321
9	0.8054	1.3358	1.5464
···	···	···	···

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表 3 不同技术特点对比

Table 3. Contrast in traits among different means

特点	本次研究提出的方法	矩阵计算	直接赋值
结果客观性	基于已发现资源规模数据；计算相对客观		对地质经验依赖程度高；相对更主观
计算复杂程度	不需要进行矩阵计算，可利用常用办公软件实现图解	需要进行多次矩阵计算	利用经验直接确定
计算复杂程度	计算复杂程度低	计算复杂程度高	计算复杂程度低
分析步长设置	不需要人工设定	需要人工设定	不需要人工设定
精细程度	不需要迭代，无分析步长，精细程度高	需要迭代，分析步长之间的部分被略过	经验判定，精细化程度低
分析结果形式	对不同条件下对k取值的解集，相对更完整，利于评价与优选	解集中的单个解

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表 4 川中金秋气区盐亭区块侏罗系沙溪庙组6号砂组已发现资源规模

Table 4. Scale of discovered reservoirs in the 6th group of Jurrasic Shaximiao Formation in the Yanting Block of the Jinqiu gas-producing area located in the central part of Sichuan Basin

已发现气藏名称	JQ23	JQ517	JQ518	JQ507	JH51-ST1	ZQ2
天然气资源规模/ 10⁸m³	96.88	0.78	6.87	3.67	6.86	35.07

下载: 导出CSV

表 5 应用实例k、已知气藏规模序列号、最小经济气藏规模对应序列号求解结果

Table 5. Results acquired from the calculation analysis of the cofficient of the resource scale variation (k), sequential number of discovered reservoirs and sequential number corresponding to the minimum economic gas reservoir size for the application case

分析次数（即参数c的取值）	求解结果
分析次数（即参数c的取值）	已知气藏的规模序列号	气藏规模变化系数k	最小经济气藏规模条件下所对应规模序列号
1	[1, 3, 17, 17, 32, 174]	0.9345	280
2	[2, 5, 21, 21, 37, 149]	1.1201	220
3	[3, 6, 18, 18, 28, 79]	1.4721	107
4	[4, 8, 24, 24, 37, 105]	1.4735	142
5	[5, 9, 23, 23, 33, 81]	1.7328	104
…	…	…	…
20	[20, 31, 63, 63, 83, 161]	2.3129	194

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表 6 行列式-矩阵迭代算法结果与所提出方法中相应解对比情况

Table 6. Contrast between the results of determination-matrix iteration and the corresponding solution from the proposed method

已发现气藏规模序列号	天然气资源规模/10⁸m³
已发现气藏规模序列号	实际规模	行列式-矩阵迭代算法应用结果k=1.7321	所提出方法应用相应解k=1.7328
5	96.88	96.7400	96.8800
9	35.07	34.9500	34.9863
23	6.87	6.8800	6.8835
23	6.86	6.8800	6.8835
33	3.67	3.6800	3.6824
81	0.78	0.7800	0.7769
标准差		0.0759	0.0428

下载: 导出CSV

表 7 应用实例的气藏序列分析结果

Table 7. Results obtained from the analysis for the application case of the reservoir size sequential method

气藏规模序列号	气藏规模/ 10⁸m³	气藏规模变化系数k	最小经济气藏规模/10⁸m³
1	488.2074	1.4721	0.5
2	175.9779
3	96.8800
4	63.4325
5	45.6721
6	35.0700
7	27.8314
8	22.8647
9	19.2249
···	···
105	0.5167
106	0.5095
107	0.5025

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