页岩气井压裂液返排与生产阶段的压裂裂缝特征差异研究

刘文超; 乔成成; 汪萍; 黄文松; 刘曰武; 丁伟; 孙玉平

doi:10.6052/0459-1879-23-031

页岩气井压裂液返排与生产阶段的压裂裂缝特征差异研究

doi: 10.6052/0459-1879-23-031

刘文超^*, ,,
乔成成^*,
汪萍^†,
黄文松^†,
刘曰武^**,
丁伟^†,
孙玉平^†

*.
北京科技大学土木与资源工程学院, 北京 100083
†.
中国石油勘探开发研究院, 北京 100083
**.
中国科学院力学研究所, 北京 100190

基金项目: 中国石油科技创新基金资助项目(2021DQ02-0901)

详细信息

通讯作者:
刘文超, 副教授, 主要研究方向为非常规油气渗流力学 . E-mail: liuwenchao@ustb.edu.cn

中图分类号: TE33⁺2
计量
- 文章访问数: 471
- HTML全文浏览量: 126
- PDF下载量: 82
- 被引次数: 0
出版历程
- 收稿日期: 2023-02-02
- 录用日期: 2023-04-26
- 网络出版日期: 2023-04-27
- 刊出日期: 2023-06-18

STUDY ON FRACTURE CHARACTERISTICS DIFFERENCE BETWEEN FRACTURING FLUID FLOWBACK AND GAS PRODUCTION STAGES OF SHALE GAS WELLS

*.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
†.
PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
**.
Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China

摘要

摘要: 水平井分段压裂是实现页岩气经济开发的关键技术, 生产过程压裂裂缝闭合会对开采产生不利影响. 由于生产动态数据误差较大且震荡严重, 与渗流数学模型内边界条件不匹配, 目前很少有基于生产动态数据分析来定量评价压裂液返排与页岩气生产阶段压裂裂缝特征差异的方法. 为此, 文章提出一种基于反褶积的量化评估返排与生产阶段压裂裂缝特征差异的生产动态数据分析系统新方法. 首先, 给出返排和生产阶段的渗流模型及其Laplace解. 之后, 利用压力反褶积算法分别对两阶段的生产动态数据进行归一化处理. 并使反褶积计算的归一化参数调试与渗流模型计算的参数调试在特征曲线拟合过程中相互制约, 分别解释出两阶段的裂缝半长及裂缝导流能力. 最后, 引入导流能力模量, 对两阶段的压裂裂缝特征差异进行了量化评估. 利用此方法对现场10口井的分析结果表明: 本方法可以有效量化评估返排与生产阶段压裂裂缝特征差异; 相比于返排阶段, 生产阶段的裂缝导流能力下降了约两个数量级, 裂缝发生了明显闭合. 文章建立的分析方法对页岩气藏后期增产措施优化有重要参考价值.
- 页岩气 /
- 分段压裂水平井 /
- 动态数据分析 /
- 反褶积 /
- 特征曲线
Abstract: Horizontal well staged fracturing is the key technology to realize the economic development of shale gas. The closure of fracturing fractures in the production process will have adverse effects on the exploitation. Due to the large errors and serious oscillations of dynamic production data, it does not match the internal boundary conditions of the seepage flow mathematical model. Therefore, there are few quantitative methods to evaluate the difference of fracture characteristics between fracturing fluid flowback stage and shale gas production stage based on dynamic production data analysis currently. Based on this concern, a new method of production dynamic data analysis based on deconvolution is proposed to quantitatively evaluate the difference of fracture characteristics between flowback stage and production stage in this paper. Firstly, the seepage flow models and their Laplace solutions corresponding to flowback stage and production stage are given. Secondly, the pressure deconvolution algorithm is used to normalize the dynamic production data of the two stages. Then, the normalization parameter adjustment of deconvolution calculation and the parameter adjustment of theoretical seepage flow model calculation are mutually restricted in the process of typical curve fitting, and the fracture half-length and fracture conductivity of the two stages are interpreted respectively. Finally, the conductivity modulus is introduced to quantitatively evaluate the difference of fracture characteristics between flowback stage and production stage. The established method is used to analyze 10 wells in the field. The results show that this method can effectively quantify the difference of fracturing fracture characteristics between flowback stage and production stage; compared with the flowback stage, the fracture conductivity decreased by about two orders of magnitude in the production stage, and the fracture closed significantly. The analysis method established in this paper has important reference value for the optimization of stimulation measures in the later stage of shale gas reservoir.
- shale gas /
- multi-stage fractured horizontal well /
- dynamic data analysis /
- deconvolution /
- typical curve

HTML全文

图 1 分段压裂水平井三线性渗流物理模型

Figure 1. Trilinear seepage physical model of multi-stage fractured horizontal wells

下载: 全尺寸图片幻灯片

图 2 后期生产阶段动态数据分析流程图

Figure 2. Dynamic data analysis flow chart of later production stage

下载: 全尺寸图片幻灯片

图 3 前期返排阶段动态数据分析流程图

Figure 3. Dynamic data analysis flow chart of early flowback stage

下载: 全尺寸图片幻灯片

图 4 分段压裂水平井页岩气日产量及拟井底流压数据

Figure 4. Shale gas production rate and pseudo bottom hole flowing pressure in the multi-stage fractured horizontal well

下载: 全尺寸图片幻灯片

图 5 后期生产阶段单位流量下的拟压差数据拟合结果图

Figure 5. Fitting result diagram of pseudo pressure drop data per unit flow rate in later production stage

下载: 全尺寸图片幻灯片

图 6 归一化数据单位流量下的拟压力降和拟压力降导数双对数特征曲线拟合效果图

Figure 6. Fitting results diagram of log-log typical curves of normalized data and pseudo pressure drop and pseudo pressure drop derivative per unit flow rate

下载: 全尺寸图片幻灯片

图 7 页岩气藏流态识别图

Figure 7. Flow pattern identification diagram of shale gas reservoir

下载: 全尺寸图片幻灯片

图 8 现场页岩气日产量及模型计算日产量拟合效果图

Figure 8. Fitting effect drawing of production data of field shale gas and production data calculated by model

下载: 全尺寸图片幻灯片

图 9 分段压裂水平井压裂液日产量及井底流压数据

Figure 9. Shale gas fracturing fluid flow rate and bottom hole flowing pressure in the multi-stage fractured horizontal well

下载: 全尺寸图片幻灯片

图 10 前期返排阶段单位流量下的拟压差数据拟合结果图

Figure 10. Fitting result diagram of pseudo pressure drop data per unit flow rate in early flowback stage

下载: 全尺寸图片幻灯片

图 11 返排阶段的归一化数据的双对数特征曲线拟合效果图

Figure 11. Fitting results diagram of log-log typical curves of normalized data in flowback stage

下载: 全尺寸图片幻灯片

表 1 后期生产阶段特征曲线解释参数结果

Table 1. Interpretation results of typical curve in later production stage

Parameters	Value
main fracture half-length/m	37
main fracture conductivity/(mD·cm)	6
outer boundary distance/m	110
inter-fracture zone permeability/mD	0.1
matrix permeability/mD	0.0001
main fracture porosity	0.15
inter-fracture zone porosity	0.05
matrix porosity	0.03
main fracture composite compressibility /MPa⁻¹	0.005
inter-fracture zone composite compressibility/MPa⁻¹	0.0022
matrix composite compressibility /MPa⁻¹	0.006

下载: 导出CSV

表 2 前期返排阶段特征曲线解释参数结果

Table 2. Interpretation results of typical curve in early flowback stage

Parameters	Value
main fracture half-length/m	8.5
main fracture conductivity/(mD·cm)	390
main fracture porosity	0.15
main fracture composite compressibility /MPa⁻¹	0.0004

下载: 导出CSV

表 3 10口井的不同阶段裂缝特征汇总

Table 3. Fracture characteristics of 10 wells at different stages

Parameters	x_F/m	C_FD/(mD·cm)	*D_CFD/%		γ_k/MPa⁻¹
well 1 later	37	6		98.46	0.16866
well 1 early	8	390		98.46	0.16866
well 2 later	35	6.3		98.09	0.10581
well 2 early	8	330		98.09	0.10581
well 3 later	40	6		98.67	0.11776
well 3 early	15	450		98.67	0.11776
well 4 later	33	8.1		98.58	0.10742
well 4 early	16	570		98.58	0.10742
well 5 later	33	4.2		99.18	0.12292
well 5 early	10	510		99.18	0.12292
well 6 later	30	5.4		99.00	0.12802
well 6 early	10	540		99.00	0.12802
well 7 later	20	11.4		98.10	0.11276
well 7 early	8	600		98.10	0.11276
well 8 later	30	16.5		95.77	0.084981
well 8 early	10	390		95.77	0.084981
well 9 later	36	8.7		98.55	0.13613
well 9 early	20	600		98.55	0.13613
well 10 later	50	18		97.14	0.07831
well 10 early	21	630		97.14	0.07831
* D_CFD means the decreased degree of fracture conductivity.

下载: 导出CSV

参考文献(37)

[1]	李雷, 范莹莹. 基于绿色发展需要推进中国页岩气革命的策略思考. 中外能源, 2019, 24(1): 14-21 (Li Lei, Fan Yingying. Thoughts on promoting China’s shale gas revolution based on green development. Sino-Global Energy, 2019, 24(1): 14-21 (in Chinese) Li Lei, Fan Yingying. Thoughts on promoting China’s shale gas revolution based on green development. Sino-Global Energy, 2019, 24(01): 14-21 (in Chinese)
[2]	窦立荣, 李大伟, 温志新等. 全球油气资源评价历程及展望. 石油学报, 2022, 43(8): 1035-1048 (Dou Lirong, Wen Zhixin, Wang Zhanming, et al. History and outlook of global oil and gas resources evaluation. Acta Petrolei Sinica, 2022, 43(8): 1035-1048 (in Chinese) Dou Lirong, Wen Zhixin, Wang Zhanming, et al. History and outlook of global oil and gas resources evaluation. Acta Petrolei Sinica, 2022, 43(08): 1035-1048 (in Chinese)
[3]	邹才能, 赵群, 丛连铸等. 中国页岩气开发进展、潜力及前景. 天然气工业, 2021, 41(1): 1-14 (Zou Caineng, Zhao Qun, Cong Lianzhu, et al. Development progress, potential and prospect of shale gas in China. Natural Gas Industry, 2021, 41(1): 1-14 (in Chinese) Zou Caineng, Zhao Qun, Cong Lianzhu, et al. Development progress, potential and prospect of shale gas in China. Natural Gas Industry, 2021, 41(01): 1-14 (in Chinese)
[4]	Hu H, Zhu YQ, Li SY, et al. Effects of green energy development on population growth and employment: Evidence from shale gas exploitation in Chongqing, China. Petroleum Science, 2021, 18(5): 1578-1588 doi: 10.1016/j.petsci.2021.08.013
[5]	刘曰武, 高大鹏, 李奇等. 页岩气开采中的若干力学前沿问题. 力学进展, 2019, 49: 1-236 (Liu Yuewu, Gao Dapeng, Li Qi, et al. Mechanical frontiers in shale-gas development. Advances in Mechanics, 2019, 49: 1-236 (in Chinese) Liu Yuewu, Gao Dapeng, Li Qi, et al. Mechanical frontiers in shale-gas development. Advances in Mechanics, 2019, 49(00): 1-236 (in Chinese)
[6]	史璨, 林伯韬. 页岩储层压裂裂缝扩展规律及影响因素研究探讨. 石油科学通报, 2021, 6(1): 92-113 (Shi Can, Lin Botao. Principles and influencing factors for shale formations. Petroleum Science Bulletin, 2021, 6(1): 92-113 (in Chinese) Shi Can, Lin Botao. Principles and influencing factors for shale formations. Petroleum Science Bulletin, 2021, 6(01): 92-113 (in Chinese)
[7]	Qu ZQ, Wang JW, Guo TK, et al. Optimization on fracturing fluid flowback model after hydraulic fracturing in oil well. Journal of Petroleum Science and Engineering, 2021, 204: 108703 doi: 10.1016/j.petrol.2021.108703
[8]	Zhang FY, Meybodi HE. A type-curve method for two-phase flowback analysis in hydraulically fractured hydrocarbon reservoirs. Journal of Petroleum Science and Engineering, 2022, 209: 109912 doi: 10.1016/j.petrol.2021.109912
[9]	杜旭林, 程林松, 牛烺昱等. 考虑水力压裂缝和天然裂缝动态闭合的三维离散缝网数值模拟. 计算物理, 2022, 39(4): 453-464 (Du Xulin, Cheng Linsong, Niu Langyu, et al. Numerical simulation of 3D discrete fracture networks considering dynamic closure of hydraulic fractures and natural fractures. Chinese Journal of Computational Physics, 2022, 39(4): 453-464 (in Chinese) DuXulin, Cheng Linsong, Niu Langyu, et al. Numerical simulation of 3 D discrete fracture networks considering dynamic closure of hydraulic fractures and natural fractures. Chinese Journal of Computational Physics, 2022, 39(04): 453-464 (in Chinese)
[10]	Zhang FY, Meybodi HE. Flowback fracture closure of multi-fractured horizontal wells in shale gas reservoirs. Journal of Petroleum Science and Engineering, 2020, 186: 106711 doi: 10.1016/j.petrol.2019.106711
[11]	姜瑞忠, 何吉祥, 姜宇等. 页岩气藏压裂水平井Blasingame产量递减分析方法建立与应用. 石油学报, 2019, 40(12): 1503-1510 (Jiang Ruizhong, He Jixiang, Jiang Yu, et al. Establishment and application of Blasingame production decline analysis method for fractured horizontal well in shale gas reservoirs. Acta Petrolei Sinica, 2019, 40(12): 1503-1510 (in Chinese) doi: 10.1038/s41401-019-0280-2 Jiang Ruizhong, He Jixiang, Jiang Yu, et al. Establishment and application of Blasingame production decline analysis method for fractured horizontal well in shale gas reservoirs. Acta Petrolei Sinica, 2019, 40(12): 1503-1510 (in Chinese) doi: 10.1038/s41401-019-0280-2
[12]	Ren W, Lau HC. Analytical modeling and probabilistic evaluation of gas production from a hydraulically fractured shale reservoir using a quad-linear flow model. Journal of Petroleum Science and Engineering, 2020, 184: 106516 doi: 10.1016/j.petrol.2019.106516
[13]	Zhang FY, Meybodi HE. A semianalytical method for two-phase flowback rate-transient analysis in shale gas reservoirs. Society of Petroleum Engineers, 2020, 25(4): 1599-1622
[14]	Meng M, Chen Z, Liao X, et al. A well-testing method for parameter evaluation of multiple fractured horizontal wells with non-uniform fractures in shale oil reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 187-198 doi: 10.26804/ager.2020.02.07
[15]	Zhang FY, Meybodi HE. Multiphase flowback rate-transient analysis of shale gas reservoirs. International Journal of Coal Geology, 2020, 217: 103315
[16]	Luo L, Cheng S, Lee J. Time-normalized conductivity concept for analytical characterization of dynamic-conductivity hydraulic fractures through pressure-transient analysis in tight gas reservoirs. Journal of Natural Gas Science and Engineering, 2021, 92: 103997 doi: 10.1016/j.jngse.2021.103997
[17]	Cui Y, Jiang R, Wang Q, et al. Production performance analysis of multi-fractured horizontal well in shale gas reservoir considering space variable and stress-sensitive fractures. Journal of Petroleum Science and Engineering, 2021, 207: 109171 doi: 10.1016/j.petrol.2021.109171
[18]	陈志明, 王佳楠, 廖新维等. 海陆过渡相页岩气藏不稳定渗流数学模型. 力学学报, 2021, 53(8): 2257-2266 (Chen Zhiming, Wang Jianan, Liao Xinwei, et al. An unstable porous flow model of marine-continental transitional shale gas reservoir. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 2257-2266 (in Chinese) Chen Zhiming, Wang Jianan, Liao Xinwei, et al. An unstable porous flow model of marine-continental transitional shale gas reservoir. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 2257-2266 (in Chinese)
[19]	Tu Z, Hu X, Zhou F, et al. A new multi-fracture geometry inversion model based on hydraulic-fracture treatment pressure falloff data. Journal of Petroleum Science and Engineering, 2022, 215: 110724
[20]	Zhang FY, Meybodi, HE. Analysis of early-time production data from multi-fractured shale gas wells by considering multiple transport mechanisms through nanopores. Journal of Petroleum Science and Engineering, 2021, 197: 108092 doi: 10.1016/j.petrol.2020.108092
[21]	Clarkson CR, Yuan B, Zhang ZZ. A new straight-line analysis method for estimating fracture/reservoir properties using dynamic fluid-in-place calculations. Society of Petroleum Engineers Reservoir Evaluation & Engineering, 2020, 23(2): 606-626
[22]	Liang P, Aguilera R, Mattar L. A new method for production–data analysis and well testing by use of superposition rate. Society of Petroleum Engineers Reservoir Evaluation & Engineering, 2017, 21(1): 1-16
[23]	Von Schroeter T, Hollaender F, Gringarten AC. Deconvolution of well test data as a nonlinear total least squares problem. Society of Petroleum Engineers, 2004, 9(4): 375-390
[24]	Levitan MM. Deconvolution of multiwell test data. Society of Petroleum Engineers, 2007, 12(4): 420-428
[25]	Ilk D, Valko PP, Blasingame TA. Deconvolution of variable-rate reservoir-performance data using B-splines. Society of Petroleum Engineers Reservoir Evaluation & Engineering, 2006, 9(5): 582-595
[26]	Liu WC, Liu YW, Han GF, et al. An improved deconvolution algorithm using B-splines for well-test data analysis in petroleum engineering. Journal of Petroleum Science and Engineering, 2017, 149: 306-314 doi: 10.1016/j.petrol.2016.10.064
[27]	Khalaf MS, El-Banbi AH, El-Maraghi A, et al. Two-step deconvolution approach for wellbore storage removal. Journal of Petroleum Science and Engineering, 2020, 195: 107827 doi: 10.1016/j.petrol.2020.107827
[28]	Pan Y, Deng L, Lee WJ. A novel data-driven pressure/rate deconvolution algorithm to enhance production data analysis in unconventional reservoirs. Journal of Petroleum Science and Engineering, 2020, 192: 107332 doi: 10.1016/j.petrol.2020.107332
[29]	Brown M, Ozkan E, Raghavan R, et al. Practical solutions for pressure-transient responses of fractured horizontal wells in unconventional shale reservoirs. Society of Petroleum Engineers Reservoir Evaluation & Engineering, 2011, 14(6): 663-676
[30]	Du FS, Nojabaei B. Estimating diffusion coefficients of shale oil, gas, and condensate with nano-confinement effect. Journal of Petroleum Science and Engineering, 2020, 193: 107362 doi: 10.1016/j.petrol.2020.107362
[31]	Mosavat N, Hasanidarabadi B, Pourafshary P. Gaseous slip flow simulation in a micro/nano pore-throat structure using the lattice Boltzmann model. Journal of Petroleum Science and Engineering, 2019, 177: 93-103 doi: 10.1016/j.petrol.2019.02.029
[32]	Yu H, Zhu Y B, Jin X, et al. Multiscale simulations of shale gas transport in micro/nano-porous shale matrix considering pore structure influence. Journal of Natural Gas Science and Engineering, 2019, 64: 28-40 doi: 10.1016/j.jngse.2019.01.016
[33]	吴永辉, 程林松, 黄世军等. 考虑页岩气赋存及非线性流动机理的产能预测半解析方法. 中国科学: 技术科学, 2018, 48: 691-700 (Wu Yonghui, Cheng Linsong, Huang Shijun, et al. A semi-analytical method of production prediction for shale gas wells considering multi-nonlinearity of flow mechanisms. Sci. Sin. Tech., 2018, 48: 691-700 (in Chinese) doi: 10.1360/N092017-00138 Wu Y H, Cheng L S, Huang S J, et al. A semi-analytical method of production prediction for shale gas wells considering multi-nonlinearity of flow mechanisms. Sci Sin Tech, 2018, 48: 691–700 (in Chinese) doi: 10.1360/N092017-00138
[34]	Liu WC, Liu YW, Zhu WY, et al. A stability-improved efficient deconvolution algorithm based on B-splines by appending a nonlinear regularization. Journal of Petroleum Science and Engineering, 2018, 164: 400-416 doi: 10.1016/j.petrol.2018.01.083
[35]	郭金城. 反褶积数据处理方法在特低渗透储层试井解释中的应用. 钻采工艺, 2019, 42(1): 42-45, 4 (Guo Jincheng. Application of deconvolution data processing method in well test interpretation for extremely-low permeability reservoirs. Drilling &Production Technology, 2019, 42(1): 42-45, 4 (in Chinese) Guo Jincheng. Application of deconvolution data processing method in well test interpretation for extremely-low permeability reservoirs. Drilling & Production Technology, 2019, 42(01): 42-45 + 4 (in Chinese)
[36]	Vessaire C, Chancelier JP, De Lara M, et al. Multistage optimization of a petroleum production system with material balance model. Computers & Chemical Engineering, 2022, 167: 108005
[37]	刘文超, 刘曰武. 评价煤层吸附气解吸能力的生产数据系统分析新方法. 煤炭学报, 2017, 42(12): 3212-3220 (Liu Wenchao, Liu Yuewu. A new method of systematic analysis of production data for evaluating the desorption ability of adsorbed gas in coal beds. Journal of China Coal Society, 2017, 42(12): 3212-3220 (in Chinese) Liu Wenchao, Liu Yuewu. A new method of systematic analysis of production data for evaluating the desorption ability of adsorbed gas in coal beds. Journal of China Coal Society, 2017, 42(12): 3212-3220 (in Chinese))