基于灰色关联方法的深层煤层气井压后产能影响地质工程因素评价

doi:10.13809/j.cnki.cn32-1825/te.2023.04.004

摘要/Abstract

摘要：

煤储层具有低孔、低渗和低压特征，实现煤层气工业开采主要依靠水力压裂等技术。沁水盆地柿庄区块目前日产量小于500 m³的气井占到50 %以上，气井改造后增产效果不理想，气井产能主控影响因素不清楚，直接影响气井整体产能的提升。基于煤层气井压裂及产能数据，利用灰色关联方法刻画了煤层气地质工程因素对压裂产能的影响程度，分析了气井压裂后产能主控影响因素。根据皮尔逊相关性分析方法，建立产能主控因素与气井产量的关联数学模型来预测气井产能，结合气井生产数据验证了预测模型可靠性。采用卡方自动交互检测决策树方法，建立了已压裂井地质和工程因素对气井产能影响关系分类决策树，高含气量条件下工程因素对气井产能提升影响较小，随着含气量降低，不同工程因素对气井产能的影响程度逐渐增大，有助于优化排量、砂量及总液量等主要设计参数，丰富了煤层气压裂后产能评价方法。

关键词: 煤层气, 灰色关联方法, 压裂效果, K-均值聚类算法, 多参数模型

Abstract:

Coal-bed methane reservoirs are characterized by low porosity, low permeability and low pressure, making their industrial exploitation primarily reliant on techniques like hydraulic fracturing. Currently, more than 50 percent of the gas wells in the Shizhuang block in the Qinshui Basin currently produce less than 500 m³/d of coal bed methane. However, the increase in production after gas well retrofitting has not been ideal and the main factors affecting gas well productivity remain unclear, directly impacting the overall improvement. To address this, the degree of influence of geological and engineering factors on fracturing productivity in coal-bed gas wells is described using the gray correlation method, and the main factors controlling gas well productivity after fracturing are analyzed. A correlation mathematical model between the main control factors and gas well production is established using the Pearson correlation analysis method to predict gas well productivity. The reliability of the prediction model is verified through gas well data validation. Furthermore, a classification decision tree is established using the chi-square automatic interactive detection decision tree method, in conjunction with gas well productivity data, to understand the impact of geological and engineering factors on gas well productivity in fractured wells. Under high gas content conditions, engineering factors have a relatively small impact on the productivity improvement of gas wells. However, as the gas content decreases, the impact of different engineering factors on the gas well productivity gradually increases, which helps optimize the main design parameters such as displacement, sand volume, and total liquid volume, enriching the evaluation methods for post fracturing productivity of coal seam pressure.

Key words: coal-bed methane, grey correlation method, fracturing effect, K-means clustering algorithm, optimization of fracturing parameters

中图分类号:

TE328

孔祥伟,谢昕,王存武,时贤. 基于灰色关联方法的深层煤层气井压后产能影响地质工程因素评价[J]. 油气藏评价与开发, 2023, 13(4): 433-440.

KONG Xiangwei,XIE Xin,WANG Cunwu,SHI Xian. Evaluation of geological engineering factors for productivity of deep CBM well after fracturing based on grey correlation method[J]. Petroleum Reservoir Evaluation and Development, 2023, 13(4): 433-440.

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煤层	埋深/ m	储厚/ m	含气量/ （m³/t）	煤岩密度/ （t/m³）	渗透率/ 10^-3μm²	孔隙度/ %	储层压力/ MPa	顶板岩性	底板岩性	显微裂隙密度/ （条/cm）
3号	651	6.21	13.69	1.58	0.21~0.60	4.53	3.21	粉砂质泥岩	粉砂质泥岩	6.2
15号	742	4.93	13.38	1.60	0.14~0.46	5.17	4.26	灰岩	粉砂质泥岩	7.1

影响次序	压裂参数	相关度
1	含气量	0.102 4
2	储层厚度	0.101 5
3	渗透率	0.100 2
4	可压指数	0.088 9

影响次序	压裂参数	相关度
1	最大施工排量	0.104 7
2	支撑剂总量	0.101 1
3	平均砂比	0.081 0
4	施工总液量	0.080 4
5	最后瞬时停泵压力	0.066 7
6	携砂液量	0.061 4
7	施工泵压	0.058 2
8	前置液量	0.053 6

模型参数	平均日产量
模型参数	显著性	显著性等级
储层厚度	0.144	3
含气量	0.151	2
渗透率	0.139	5
可压指数	0.137	6
施工排量	0.154	1
支撑剂总量	0.142	4
施工总液量	0.133	7