Parameter Sensitivity Analysis in Geology-Engineering Integration Optimization for Shale Gas in Nanchuan Block
-
摘要: 地质工程一体化综合施策是页岩气降本增效、提高开发效益的重要途径，综合、定量是一体化优化决策研究的重要发展方向.但目前的定量优化更多针对单井进行，常以优化得到的单井最优的裂缝半长/水平井长作为井网部署的依据.在利用运筹学技术构建的单井和区块效益目标函数的基础上，以中国南方海相页岩气为例，对比分析了单井和区块优化结果对主要地质条件和工程参数变化的敏感性.结果表明，尽管随着压裂规模（裂缝半长）的增大，单井和区块的效益都呈现先增后减的趋势，但最优的裂缝半长明显不同.同时，随孔隙度、含气饱和度、压力系数、天然气价格、压裂成本、钻井成本的升高，优化所得的单井和区块的最优裂缝半长变化规律不同.这表明，单井优化的结果不能作为井网部署的依据，区块和单井得到的最优值并不一致，应该以区块地质工程整体一体化优化的结果来布井.这一认识对页岩气及其他非常规油气井网优化部署和效益开发有现实的指导意义.Abstract: Geology-engineering integration policy is an important way to reduce cost and increase efficiency of shale gas. Integrated and quantitative approach is an important development direction of integrated optimization decision research. However, the current quantitative optimization is more often performed for single well, and the optimal fracture half-length/horizontal well length obtained from the optimization of a single well is often used as the basis for well pattern deployment. In this paper, it compares the sensitivity of single-well and block optimization results to major geological conditions and engineering parameters based on the objective functions of single-well and block benefits constructed using operational research techniques. The results show that although the benefits of both single-well and block show a trend of increasing and then decreasing with increasing fracture size (fracture half-length), the optimal fracture half-lengths are significantly different. Meanwhile, with the increase of porosity, gas saturation, pressure coefficient, natural gas price, fracturing cost and drilling cost, the optimal fracture half length of single well and block varies. It indicates that the optimal values obtained for blocks and single well are not consistent, and wells should be laid out with the results of the overall geology-engineering integration optimization of the blocks. This finding has realistic implications for the optimal deployment and efficient development of shale gas and other unconventional oil and gas well networks.
-
图 1 南川地区五峰组底界构造及目标层段地质柱状图
Fig. 1. Geological histogram of bottom boundary structure and target interval of the Wufeng Formation in the Nanchuan area
图 4 工程参数对单井和区块的效益及成本的影响
Fig. 4. Influence of engineering parameters on benefit and cost of single well and whole block
图 6 经济参数和地质参数对单井和区块最优裂缝半长的影响
Fig. 6. Influence of economic and geological parameters on optimal fracture half-length of single well and whole block
图 7 工程参数对单井和区块最优裂缝半长的影响
压裂成本系数=实际压裂成本/理论公式的压裂成本，钻井成本系数=实际钻井成本/理论公式的钻井成本
Fig. 7. Influence of engineering parameters on fracture half-length of single well and whole block
表 1 页岩气数值模型基本参数（据Wang et al., 2019）
Table 1. Basic parameters of shale gas numerical model(from Wang et al., 2019)
参数 值 吸附气含量（m3/t） 3 兰氏压力（MPa） 4 兰氏体积（cm3/g） 2 初始地层压力（MPa） 32 基质渗透率（mD） 0.000 03 含气饱和度（%） 65 基质孔隙度（%） 4 水力压裂裂缝半长（m） 120 主裂缝导流能力（mD·m） 7 水力压裂裂缝开度（m） 0.001 簇间距（m） 20 水力压裂裂缝高度（m） 30 压裂段数 26 天然裂缝间距（m） 1 生产时间（a） 15 可采储量（108 m3） 19.32 
下载: 导出CSV
表 2 影响因素的范围
Table 2. Variation range of influencing factors
水平 孔隙度
(%)含气饱和度
(%)压力系数 裂缝半长
(m)簇间距
(m)裂缝导流能力
(mD·m)水平段长度
(m)水平1 1 10 1.0 50 10 1 1 000 水平2 2 40 1.2 100 15 10 3 000 水平3 3 60 1.4 150 20 20 5 000 水平4 4 90 1.6 200 25 30 8 000
下载: 导出CSV
表 3 工程参数对区块的效益和成本的影响
Table 3. Influence of engineering parameters on benefit and cost of the whole block
参数值(m) 井数 单井产量
(108m3)单井收入
(亿元)单井成本
(亿元)区块总成本
(亿元)区块总收入
(亿元)区块总效益
(亿元)裂缝半长 50 1 711 0.466 0.485 0.433 741 830 88.7 100 841 0.784 0.971 0.509 428 816 388 130 667 0.957 1.240 0.585 390 824 434 150 551 1.070 1.410 0.684 377 775 399 200 406 1.350 1.830 1.250 508 745 237 水平段长度 1 000 1 711 0.391 0.371 0.358 612 635 23 3 000 551 1.175 1.570 0.669 369 864 495 5 000 319 1.955 2.760 1.010 324 880 556 7 000 232 2.732 3.940 1.400 324 915 591 注：工区大小如图 3所示.
下载: 导出CSV
-
[1] Chen, X. J., 2003. The Application of the Analysis of Profit and Loss Equality in Oil Developmental Economy (Dissertation). Tianjin University, Tianjin (in Chinese with English abstract). [2] Clarkson, C. R., 2013. Production Data Analysis of Unconventional Gas Wells: Review of Theory and Best Practices. International Journal of Coal Geology, 109-110: 101-146. https://doi.org/10.1016/j.coal.2013.01.002 [3] Freund, Y., 1995. Boosting a Weak Learning Algorithm by Majority. Information and Computation, 121(2): 256-285. https://doi.org/10.1006/inco.1995.1136 [4] Golzari, A., Sefat, M. H., Jamshidi, S., 2015. Development of an Adaptive Surrogate Model for Production Optimization. Journal of Petroleum Science and Engineering, 133: 677-688. https://doi.org/10.1016/j.petrol.2015.07.012 [5] Guo, Y. D., Wang, W. H., Liu, H., et al., 2018. Research on the Production Influencing Factors of Shale Gas Multi-Stage Fractured Horizontal Well. Bulletin of Science and Technology, 34(4): 72-78, 83 (in Chinese with English abstract). [6] He, T. H., Li, W. H., Lu, S. F., et al., 2022. Mechanism and Geological Significance of Anomalous Negative δ13Ckerogen in the Lower Cambrian, NW Tarim Basin, China. Journal of Petroleum Science and Engineering, 208: 109384. https://doi.org/10.1016/j.petrol.2021.109384 [7] He, T. H., Lu, S. F., Li, W. H., et al., 2018. Effect of Salinity on Source Rock Formation and Its Control on the Oil Content in Shales in the Hetaoyuan Formation from the Biyang Depression, Nanxiang Basin, Central China. Energy & Fuels, 32(6): 6698-6707. https://doi.org/10.1021/acs.energyfuels.8b01075 [8] He, X. P., 2021. Sweet Spot Evaluation System and Enrichment and High Yield Influential Factors of Shale Gas in Nanchuan Area of Eastern Sichuan Basin. Natural Gas Industry, 41(1): 59-71 (in Chinese with English abstract). [9] Huang, H. Y., Fan, Y., Zeng, B., et al., 2020. Geology- Engineering Integration of Platform Well in Changning Block. Science Technology and Engineering, 20(1): 175-182 (in Chinese with English abstract). [10] Kulga, B., Artun, E., Ertekin, T., 2017. Development of a Data-Driven Forecasting Tool for Hydraulically Fractured, Horizontal Wells in Tight-Gas Sands. Computers & Geosciences, 103: 99-110. https://doi.org/10.1016/j.cageo.2017.03.009 [11] Li, D. H., Yao, H. S., He, X. P., et al., 2022. Geological Theory and Resource Potential of Atmospheric Pressure Shale Gas in Complex Structural Areas. Geological Publishing House, Beijing (in Chinese). [12] Li, Q. H., Chen, M., Wang, F. P., et al., 2012. Influences of Engineering Factors on Shale Gas Productivity: A Case Study from the Haynesville Shale Gas Reservoir in North America. Natural Gas Industry, 32(4): 54-59, 123 (in Chinese with English abstract). [13] Li, W. B., Li, J. Q., Lu, S. F., et al., 2022. Evaluation of Gas-in-Place Content and Gas-Adsorbed Ratio Using Carbon Isotope Fractionation Model: A Case Study from Longmaxi Shales in Sichuan Basin, China. International Journal of Coal Geology, 249: 103881. https://doi.org/10.1016/j.coal.2021.103881 [14] Liu, W. C., Zhang, Q. T., Zhu, W. Y., 2019. Numerical Simulation of Multi-Stage Fractured Horizontal Well in Low-Permeable Oil Reservoir with Threshold Pressure Gradient with Moving Boundary. Journal of Petroleum Science and Engineering, 178: 1112-1127. https://doi.org/10.1016/j.petrol.2019.04.033 [15] Liu, X., Zhang, L. N., Zhang, Y. Z., 2018. Influence of Fracturing Parameters on Development Effects of Shale Gas Wells in Southeast Sichuan Basin: A Case of Well LP-133HF. Reservoir Evaluation and Development, 8(5): 77-80 (in Chinese with English abstract). doi: 10.3969/j.issn.2095-1426.2018.05.013 [16] Nguyen-Le, V., Shin, H., 2019. Development of Reservoir Economic Indicator for Barnett Shale Gas Potential Evaluation Based on the Reservoir and Hydraulic Fracturing Parameters. Journal of Natural Gas Science and Engineering, 66: 159-167. https://doi.org/10.1016/j.jngse.2019.03.024 [17] Qiao, L., Wang, H. J., Lu, S. F., et al., 2022. Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application. ACS Omega, 7(10): 8294-8305. https://doi.org/10.1021/acsomega.1c05158 [18] Rammay, M. H., Awotunde, A. A., 2016. Stochastic Optimization of Hydraulic Fracture and Horizontal Well Parameters in Shale Gas Reservoirs. Journal of Natural Gas Science and Engineering, 36: 71-78. https://doi.org/10.1016/j.jngse.2016.10.002 [19] Wang, H. J., Qiao, L., Zhang, J., et al., 2022. An Effective Integration Optimization Algorithm for Regional Fracturing Design and Drilling Placement. Journal of Natural Gas Science and Engineering, 101: 104505. https://doi.org/10.1016/j.jngse.2022.104505 [20] Wang, H. J., Qiao, L., Lu, S. F., et al., 2021a. A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells. Frontiers in Earth Science, 9: 726537. https://doi.org/10.3389/feart.2021.726537 [21] Wang, S., Qin, C. X., Feng, Q. H., et al., 2021b. A Framework for Predicting the Production Performance of Unconventional Resources Using Deep Learning. Applied Energy, 295: 117016. https://doi.org/10.1016/j.apenergy.2021.117016 [22] Wang, S. H., Chen, Z., Chen, S. N., 2019. Applicability of Deep Neural Networks on Production Forecasting in Bakken Shale Reservoirs. Journal of Petroleum Science and Engineering, 179: 112-125. https://doi.org/10.1016/j.petrol.2019.04.016 [23] Xu, S. Q., Feng, Q. H., Wang, S., et al., 2018. Optimization of Multistage Fractured Horizontal Well in Tight Oil Based on Embedded Discrete Fracture Model. Computers & Chemical Engineering, 117: 291-308. https://doi.org/10.1016/j.compchemeng.2018.06.015 [24] Yang, C. D., Vyas, A., Datta-Gupta, A., et al., 2017. Rapid Multistage Hydraulic Fracture Design and Optimization in Unconventional Reservoirs Using a Novel Fast Marching Method. Journal of Petroleum Science and Engineering, 156: 91-101. https://doi.org/10.1016/j.petrol.2017.05.004 [25] Yao, J., Li, Z. H., Liu, L. J., et al., 2021. Optimization of Fracturing Parameters by Modified Variable-Length Particle-Swarm Optimization in Shale-Gas Reservoir. SPE Journal, 26(2): 1032-1049. https://doi.org/10.2118/205023-PA [26] Yong, R., Chang, C., Zhang, D. L., et al., 2020. Optimization of Shale-Gas Horizontal Well Spacing Based on Geology-Engineering-Economy Integration: A Case Study of Well Block Ning 209 in the National Shale Gas Development Demonstration Area. Natural Gas Industry, 40(7): 42-48 (in Chinese with English abstract). [27] Zhang, L., Li, Z. P., Lai, F. P., et al., 2019. Integrated Optimization Design for Horizontal Well Placement and Fracturing in Tight Oil Reservoirs. Journal of Petroleum Science and Engineering, 178: 82-96. https://doi.org/10.1016/j.petrol.2019.03.006 [28] 陈晓江, 2003. 盈亏分析在油田开发经济中的设计理论和方法研究(硕士学位论文). 天津: 天津大学. [29] 郭艳东, 王卫红, 刘华, 等, 2018. 页岩气多段压裂水平井产能影响因素研究. 科技通报, 34(4): 72-78, 83. https://www.cnki.com.cn/Article/CJFDTOTAL-KJTB201804015.htm [30] 何希鹏, 2021. 四川盆地东部页岩气甜点评价体系与富集高产影响因素. 天然气工业, 41(1): 59-71. https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG202101007.htm [31] 黄浩勇, 范宇, 曾波, 等, 2020. 长宁区块页岩气水平井组地质工程一体化. 科学技术与工程, 20(1): 175-182. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202001028.htm [32] 李东海, 姚红生, 何希鹏, 等, 2022. 复杂构造区常压页岩气地质理论与资源潜力. 北京: 地质出版社. [33] 李庆辉, 陈勉, Wang, F. P., 等, 2012. 工程因素对页岩气产量的影响——以北美Haynesville页岩气藏为例. 天然气工业, 32(4): 54-59, 123. https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG201204016.htm [34] 刘欣, 张莉娜, 张耀祖, 2018. 川东南页岩气井压裂参数对开发效果的影响——以LP-133HF井为例. 油气藏评价与开发, 8(5): 77-80. https://www.cnki.com.cn/Article/CJFDTOTAL-KTDQ201805013.htm [35] 雍锐, 常程, 张德良, 等, 2020. 地质工程—经济一体化页岩气水平井井距优化——以国家级页岩气开发示范区宁209井区为例. 天然气工业, 40(7): 42-48. 16 https://www.cnki.com.cn/Article/CJFDTOTAL-TRQG202007007.htm -
点击查看大图
图(7) / 表(3)
计量
- 文章访问数: 88
- HTML全文浏览量: 38
- PDF下载量: 33
- 被引次数: 0
