STUDY ON FRACTURE CHARACTERISTICS DIFFERENCE BETWEEN FRACTURING FLUID FLOWBACK AND GAS PRODUCTION STAGES OF SHALE GAS WELLS
-
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.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.
-
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 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 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 xF/m CFD/(mD·cm) *DCFD/% γk/MPa−1 well 1 later 37 6 98.46 0.16866 well 1 early 8 390 well 2 later 35 6.3 98.09 0.10581 well 2 early 8 330 well 3 later 40 6 98.67 0.11776 well 3 early 15 450 well 4 later 33 8.1 98.58 0.10742 well 4 early 16 570 well 5 later 33 4.2 99.18 0.12292 well 5 early 10 510 well 6 later 30 5.4 99.00 0.12802 well 6 early 10 540 well 7 later 20 11.4 98.10 0.11276 well 7 early 8 600 well 8 later 30 16.5 95.77 0.084981 well 8 early 10 390 well 9 later 36 8.7 98.55 0.13613 well 9 early 20 600 well 10 later 50 18 97.14 0.07831 well 10 early 21 630 * DCFD means the decreased degree of fracture conductivity. -
[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/s41401-019-0280-2Jiang 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/N092017-00138Wu 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)) -