页岩油水平井产量影响因素分析及压裂参数优化决策

doi:10.13809/j.cnki.cn32-1825/te.2024.05.012

摘要/Abstract

摘要：

济阳坳陷页岩在沙三下亚段和沙四上亚段等主要产层获得重大突破，但开发时间短，存在单井产量差异较大，产量主控因素尚不明确的问题，深入分析页岩油水平井高产主控因素、优化确定合理压裂工艺参数仍是目前研究的重点。为明确各因素对水平井产量的影响，基于矿场实际数据开展因素关联性分析和规律挖掘。利用灰色关联分析方法及主成分分析方法定量计算页岩油水平井生产90 d、180 d和270 d的平均日产油量与压裂液用量、加砂量等影响因素之间的相关性，并在此基础上建立页岩油产能预测模型，结合SHAP算法对压裂参数进行优化分析。结果表明：压裂液用量、加砂量和破裂事件数是影响产量的主要工程参数，灰质含量、总有机碳含量和页岩孔隙性是影响产量的主要地质参数；随着生产时间的延长，地质因素对产量的影响逐渐增强，工程因素对产量的影响逐渐减弱；压裂参数优化分析确定了40~45 m压裂段长，2 700 m³单段压裂液用量，180 m³单段加砂量为最佳压裂施工参数，为页岩油水平井的开发决策和压裂设计提供了新的技术思路。

关键词: 水平井产量, 影响因素分析, 灰色关联分析, SHAP算法, 页岩油

Abstract:

Significant productivity breakthroughs have been achieved in key production layers of the shale in Jiyang Depression, notably the lower sub-member of the third member and the upper sub-member of the fourth member of Shahejie Formation. Despite these achievements, the development of these layers is relatively recent, and they exhibit considerable variation in individual well production. The primary factors influencing production remain unclear. Currently, a major focus of research is the comprehensive analysis of the main control factors for high production and the selection of reasonable fracturing parameters for shale oil horizontal wells. To better understand the impact of various factors on horizontal well production, factor correlation and pattern analysis are conducted using field data. Techniques such as gray correlation analysis and principal component analysis are employed to quantify the relationships between the average daily oil production over 90, 180, and 270 days and factors like the volume of fracturing fluid used and sand addition. Subsequently, a shale oil productivity prediction model is constructed, and fracturing parameters are optimized using SHAP（SHapley Additive exPlanations）. The research findings suggest that the volume of fracturing fluid, the amount of sand added, and the number of fracture events are the main engineering parameters affecting production. In contrast, geological parameters such as gray matter content, Total Organic Carbon（TOC）, and porosity significantly influence production as well. Over time, the impact of geological factors on production increases, while the influence of engineering factors diminishes during the later stages of production. Optimization analysis of fracturing parameters determined that a stage length of 40~45 meters, a fracturing fluid volume of 2 700 m³, and a sand addition volume of 180 m³ per stage are the optimal settings. These findings offer new insights for development determination and fracturing design in shale oil horizontal wells.

Key words: horizontal well production, influencing factors analysis, grey correlation analysis, SHAP（SHapley Additive exPlanations）, shale oil

中图分类号:

TE33

刘巍,曹小朋,胡慧芳等. 页岩油水平井产量影响因素分析及压裂参数优化决策[J]. 油气藏评价与开发, 2024, 14(5): 764-770.

LIU Wei,CAO Xiaopeng,HU Huifang, et al. Production influencing factors analysis and fracturing parameters optimization of shale oil horizontal wells[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(5): 764-770.

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参考文献 25

[1]	宋明水. 济阳坳陷页岩油勘探实践与现状[J]. 油气地质与采收率, 2019, 26(1): 1-12.
	SONG Mingshui. Practice and current status of shale oil exploration in Jiyang Depression[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(1): 1-12.
[2]	刘惠民. 济阳坳陷页岩油勘探实践与前景展望[J]. 中国石油勘探, 2022, 27(1): 73-87. doi: 10.3969/j.issn.1672-7703.2022.01.007
	LIU Huimin. Exploration practice and prospect of shale oil in Jiyang Depression[J]. China Petroleum Exploration, 2022, 27(1): 73-87. doi: 10.3969/j.issn.1672-7703.2022.01.007
[3]	孙焕泉. 济阳坳陷页岩油勘探实践与认识[J]. 中国石油勘探, 2017, 22(4): 1-14. doi: 10.3969/j.issn.1672-7703.2017.04.001
	SUN Huanquan. Exploration practice and cognitions of shale oil in Jiyang depression[J]. China Petroleum Exploration, 2017, 22(4): 1-14. doi: 10.3969/j.issn.1672-7703.2017.04.001
[4]	梁涛, 常毓文, 郭晓飞, 等. 巴肯致密油藏单井产能参数影响程度排序[J]. 石油勘探与开发, 2013, 40(3): 357-362.
	LIANG Tao, CHANG Yuwen, GUO Xiaofei, et al. Influence factors of single well’s productivity in the Bakken tight oil reservoir[J]. Petroleum Exploration and Development, 2013, 40(3): 357-362.
[5]	MA X H, LI X Z, LIANG F, et al. Dominating factors on well productivity and development strategies optimization in Weiyuan shale gas play, Sichuan Basin, SW China[J]. Petroleum Exploration and Development, 2020, 47(3): 594-602.
[6]	LUO G F, YAO T, BYCHINA M, et al. Production-strategy insights using machine learning: Application for Bakken shale[J]. SPE Reservoir Evaluation & Engineering, 2019, 22(3): 800-816.
[7]	郭建成, 林伯韬, 向建华, 等. 四川盆地龙马溪组页岩压后返排率及产能影响因素分析[J]. 石油科学通报, 2019, 4(3): 273-287.
	GUO Jiancheng, LIN Botao, XIANG Jianhua, et al. Study of factors affecting the flowback ratio and productive capacity of Longmaxi Formation shale in the Sichuan basin after fracturing[J]. Petroleum Science Bulletin, 2019, 4(3): 273-287.
[8]	KIM G, LEE H, CHEN Z, et al. Effect of reservoir characteristics on the productivity and production forecasting of the Montney shale gas in Canada[J]. Journal of Petroleum Science and Engineering, 2019, 182: 106276.
[9]	张艳茹, 王朝阳, 杨雷, 等. 大庆油田致密油水平井产能影响因素分析及预测方法[J]. 录井工程, 2018, 29(3): 55-59.
	ZHANG Yanru, WANG Chaoyang, YANG Lei, et al. Analysis and prediction of factors affecting of tight oil horizontal wells in Daqing Oilfield[J]. Mud Logging Engineering, 2018, 29(3): 55-59.
[10]	刘巍, 刘威, 谷建伟. 基于机器学习方法的油井日产油量预测[J]. 石油钻采工艺, 2020, 42(1): 70-75.
	LIU Wei, LIU Wei, GU Jianwei. Oil production prediction based on a machine learning method[J]. Oil Drilling & Production Technology, 2020, 42(1): 70-75.
[11]	徐加祥, 杨立峰, 丁云宏, 等. 致密油水平井体积压裂产能影响因素[J]. 大庆石油地质与开发, 2020, 39(1): 162-168.
	XU Jiaxiang, YANG Lifeng, DING Yunhong, et al. Influencing factors on the productivity of the volume-fractured horizontal well in the tight oil reservoir[J]. Petroleum Geology & Oilfield Development in Daqing, 2020, 39(1): 162-168.
[12]	马文礼, 李治平, 高闯, 等. 页岩气井初期产能主控因素“Pearson-MIC”分析方法[J]. 中国科技论文, 2018, 13(15): 1765-1771.
	MA Wenli, LI Zhiping, GAO Chuang, et al. “Pearson-MIC” analysis method for the initial production key controlling factors of shale gas wells[J]. China Sciencepaper, 2018, 13(15): 1765-1771.
[13]	吴林洪, 郭小哲, 罗威, 等. 致密油压裂水平井产能影响因素研究: 以辽河油田大民屯致密油水平井为例[J]. 非常规油气, 2018, 5(3): 56-62.
	WU Linhong, GUO Xiaozhe, LUO Wei, et al. Influence factors controlling the productivity of horizontal well by volume fracturing in tight oil reservoirs: A case study of dense oil horizontal well in Damintun, Liaohe Oilfield[J]. Unconventional Oil & Gas, 2018, 5(3): 56-62.
[14]	纪磊, 李菊花, 肖佳林. 随机森林算法在页岩气田多段压裂改造中的应用[J]. 大庆石油地质与开发, 2020, 39(6): 168-174.
	JI Lei, LI Juhua, XIAO Jialin. Application of random forest algorithm in the multistage fracturing stimulation of shale gas field[J]. Petroleum Geology & Oilfield Development in Daqing, 2020, 39(6): 168-174.
[15]	孙敬, 刘德华, 张亮, 等. 低渗透油藏递减影响因素的灰色关联分析[J]. 特种油气藏, 2012, 19(2): 90-93.
	SUN Jing, LIU Dehua, ZHANG Liang, et al. Grey correlation analysis of the influencing factors on production decline in low permeability reservoirs[J]. Special Oil & Gas Reservoir, 2012, 19(2): 90-93.
[16]	卫嘉鑫, 张妍, 尚教辉, 等. 页岩油开发初期产能控制因素分析: 以长庆油田里151区为例[J]. 油气藏评价与开发, 2021, 11(4): 550-558.
	WEI Jiaxin, ZHANG Yan, SHANG Jiaohui, et al. Principal factor analysis on initial productivity in shale oil development: A case study of Block Li-151 in Changqing Oilfield[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 550-558.
[17]	郝炳英, 马继业, 叶博, 等. 鄂尔多斯盆地致密油水平井产量影响因素分析[J]. 油气井测试, 2017, 26(4): 16-18.
	HAO Bingying, MA Jiye, YE Bo, et al. Influencing factors analysis of the horizontal well productivity in the tight reservoir of the Ordos Basin[J]. Well Testing, 2017, 26(4): 16-18.
[18]	王俊超, 李嘉成, 陈希, 等. 准噶尔盆地吉木萨尔凹陷二叠系芦草沟组页岩油立体井网整体压裂设计技术研究与实践[J]. 石油科技论坛, 2022, 41(2): 62-68.
	WANG Junchao, LI Jiacheng, CHEN Xi, et al. Research and practice of integrated fracturing design technology for 3D well pattern of permian Lucaogou Formation in Jimsar depression in Junggar Basin[J]. Petroleum Science and Technology Forum, 2022, 41(2): 62-68.
[19]	黄飞, 孙东升. 灰色关联分析在难动用潜力区块筛选中的应用[J]. 复杂油气藏, 2022, 15(2): 67-71.
	HUANG Fei, SUN Dongsheng. Application of grey correlation analysis in screening of hard-to-produce potential blocks[J]. Complex Hydrocarbon Reservoirs, 2022, 15(2): 67-71.
[20]	刘晓叙. 灰色预测与一元线性回归预测的比较[J]. 四川理工学院学报(自然科学版), 2009, 22(1): 107-109.
	LIU Xiaoxu. Comparing for grey forecast and forecast of one element linear regression[J]. Journal of University of Science & Engineering(Natural Science Edition), 2009, 22(1): 107-109.
[21]	KARAMIZADEH S, ABDULLAH S M, MANAF A A, et al. An overview of principal component analysis[J]. Journal of Signal and Information Processing, 2013, 4(3B): 173-175.
[22]	檀朝东, 贺甲元, 周彤, 等. 基于PCA-BNN 的页岩气压裂施工参数优化[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 56-62. doi: 10.11885/j.issn.1674-5086.2020.05.12.05
	TAN Chaodong, HE Jiayuan, ZHOU Tong, et al. A Study on the optimization of fracturing operation parameters based on PCA-BNN[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 56-62.
[23]	LUNDBERG S M, LEE S I. A unified approach to interpreting model predictions[C]// Paper SPE-211410-MS presented at the 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, December 2017.
[24]	齐春生, 丁磊, 王一, 等. 基于极端梯度提升树算法的测井曲线重构方法[J]. 科技和产业, 2022, 22(10): 405-410.
	QI Chunsheng, DING Lei, WANG Yi, et al. Reconstruction method of logging curves based on extreme gradient boosting method[J]. Science Technology and Industry, 2022, 22(10): 405-410.
[25]	韩益东, 尹洪军, 徐国涵, 等. 基于机器学习的高含水期油井产量预测方法[J]. 河南科学, 2022, 40(10): 1569-1575.
	HAN Yidong, YIN Hongjun, XU Guohan, et al. Prediction method of oil well production in high water cut period based on machine learning[J]. Henan Sciences, 2022, 40(10): 1569-1575.

成分类型	总有机碳含量	游离烃含量	孔隙度	自然伽马	声波时差	补偿中子	补偿密度	灰质含量	泥质含量	砂质含量
主成分1	0.64	0.01	-0.07	-0.04	-0.41	-0.10	-0.08	0.20	-0.24	0.00
主成分2	-0.09	-0.18	0.39	0.12	-0.16	0.36	0.12	-0.27	0.32	0.01
主成分3	-0.05	-0.36	-0.26	0.34	-0.20	0.13	-0.03	0.61	0.25	0.26
主成分4	0.19	-0.17	0.33	-0.37	-0.14	-0.20	0.57	0.17	0.14	-0.46
主成分5	-0.26	-0.23	-0.04	0.02	0.41	-0.27	0.23	-0.07	0.03	0.07

主成分类型	压裂液用量	加砂量	CO₂用量	破裂压力	段长	簇数	砂液比	微地震事件数
主成分1	0.92	0.34	-0.26	0.45	0.08	0.05	0.63	0.32
主成分2	0.78	0.38	0.17	0.16	0.01	0.36	0.75	0.52
主成分3	0.03	0.94	0.03	0.28	0.03	0.17	0.09	0.30
主成分4	0.03	0.17	0.10	-0.25	0.20	0.05	0.12	0.81