致密油藏体积压裂水平井产能评价新方法
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摘要
针对致密油藏体积压裂水平井产能评价困难等问题,运用层次分析法与灰色理论相结合的方法,建立产能评价模型,将储层物性参数和完井参数相结合,形成了新的产能评价方法。定量计算影响因素的权重系数和综合评价因子,明确影响产能主控因素,通过拐点法确定储层分类的阈值并对水平井压裂效果排序,并将研究成果运用于松辽盆地致密油藏试验区。结果表明:含油砂岩长度、地层压力、压裂簇数、单簇砂量、渗透率为影响产能的主控因素;研究区块储层质量分为:Ⅰ类、Ⅱ类、Ⅲ类,Ⅰ类储层质量最好,其次为Ⅱ类、Ⅲ类,其分类阈值为0. 35、0. 42和0. 48。现场应用表明,水平井初期产能和综合评价因子有很好的相关性,可对压裂后产能、储层质量、完井效率进行综合评价。该研究对同类油藏压裂优化设计和重复压裂选井具有重要借鉴意义。
The analytic hierarchy process and grey correlation were combined to establish a productivity evaluation model to improve the production evaluation of horizontal well with volume-fracturing in the tight oil reservoir. The reservoir physical properties and completion parameters were both considered to develop a new productivity evaluation method. The weighting and comprehensive evaluation factors were quantitatively calculated to identify the keycontrolling factors. The inflection point method was adopted to determine the reservoir classification threshold and rank the horizontal well volume-fracturing performance. The research was applied to a pilot tight oil area in Songliao Basin. Result indicates that the key-controlling factors for productivity include the oil-bearing sandstone length,formation pressure,cluster number,individual-cluster sand volume and reservoir permeability. The tight oil reservoirs in the area were classified into Class I,Class II and Class III. Class I is the most favorable reservoir,followed by Class II and Class III,and the corresponding classification thresholds are 0. 35,0. 42 and 0. 48 respectively.Field application indicates that the initial horizontal well productivity is significantly correlated with the comprehensive evaluation actor,which can be used to comprehensively evaluate the post-fracturing productivity,reservoir quality and completion efficiency. This research could provide certain reference for the fracturing optimization design and re-fracturing well selection of similar reservoirs.
The analytic hierarchy process and grey correlation were combined to establish a productivity evaluation model to improve the production evaluation of horizontal well with volume-fracturing in the tight oil reservoir. The reservoir physical properties and completion parameters were both considered to develop a new productivity evaluation method. The weighting and comprehensive evaluation factors were quantitatively calculated to identify the keycontrolling factors. The inflection point method was adopted to determine the reservoir classification threshold and rank the horizontal well volume-fracturing performance. The research was applied to a pilot tight oil area in Songliao Basin. Result indicates that the key-controlling factors for productivity include the oil-bearing sandstone length,formation pressure,cluster number,individual-cluster sand volume and reservoir permeability. The tight oil reservoirs in the area were classified into Class I,Class II and Class III. Class I is the most favorable reservoir,followed by Class II and Class III,and the corresponding classification thresholds are 0. 35,0. 42 and 0. 48 respectively.Field application indicates that the initial horizontal well productivity is significantly correlated with the comprehensive evaluation actor,which can be used to comprehensively evaluate the post-fracturing productivity,reservoir quality and completion efficiency. This research could provide certain reference for the fracturing optimization design and re-fracturing well selection of similar reservoirs.
引文
[1]李昂,卢双舫,黄文彪,等.松辽盆地南部致密油源岩下限与分级评价标准[J].石油勘探与开发,2017,44(3):473-480.
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[11]张春光,姜瑞忠,乔欣,等.双重介质致密分形气藏水平井压力动态分析[J].东北石油大学学报,2018,42(6):95-103.
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[13]ZENG J,WANG X,GUO J,et al.Composite linear flow model for multi-fractured horizontal wells in tight sand reservoirs with threshold pressure gradient[J].Journal of Petroleum Science&Engineering,2018,165:890-912.
[14]JI J,YAO Y,HUANG S,et al.Analytical model for production performance analysis of multi-fractured horizontal well in tight oil reservoirs[J].Journal of Petroleum Science&Engineering,2017,158:380-397.
[15]WANG Y and SALEHI S.Refracture candidate selection using hybrid simulation with neural network and data analysis techniques[J].Journal of Petroleum Science&Engineering,2014,123:138-146.
[16]GUO J,XIAO Y,ZHU H.A new method for fracturing wells reservoir evaluation in fractured gas reservoir[J].Mathematical Problems in Engineering,2014,36(2):1-12.
[17]陶亮,郭建春,李凌铎.致密油藏水平井重复压裂多级选井方法研究[J].特种油气藏,2018,25(4):67-71.
[18]文华,刘义坤,孙娜.基于正交灰关联法的气藏水平井体积压裂影响因素研究[J].数学的实践与认识,2015,45(7):133-140.
[19]王威.致密油藏体积压裂初期产能预测[J].新疆石油地质,2016,37(5):575-579.
[20]周志军,薛江龙,李慧敏.压后增油量预测模型[J].特种油气藏,2013,20(3):76-78.
[21]朱兆群,林承焰,张苏杰,等.改进的模糊-灰色综合评判方法在储层定量评价中的应用[J].石油与天然气地质,2017,38(1):197-208.
[22]ZENG F,GUO J.A hybrid model of fuzzy logic and grey relation analysis to evaluate tight gas formation quality comprehensively[J].Journal of Grey System,2015,27(3):87-98.
[2]杜金虎,刘合,马德胜,等.试论中国陆相致密油有效开发技术[J].石油勘探与开发,2014,41(2):198-205.
[3]马旭,郝瑞芬,来轩昂,等.苏里格气田致密砂岩气藏水平井体积压裂矿场试验[J].石油勘探与开发,2014,41(6):742-747.
[4]才博,赵贤正,沈华,等.束鹿凹陷致密油复合体积压裂技术[J].石油学报,2015,36(1):76-90.
[5]汪道兵,周福建,葛洪魁,等.纤维强制裂缝转向规律实验及现场试验[J].东北石油大学学报,2016,40(3):80-88.
[6]梁涛,常毓文.巴肯致密油藏单井产能参数影响程度排序[J].石油学报,2013,40(3):357-362.
[7]ZENG F,CHENG X,GUO J,et al.Hybridising human judgment,AHP,grey theory,and fuzzy expert systems for candidate well selection in fractured reservoirs[J].Energier,2017,10(4):1-22.
[8]张利军,郭伟,王帅,等.海上断块油田水平井初期产能的评价方法研究[J].重庆科技学院学报,2019,21(1):46-50.
[9]BEHMANESH H,HAMDI H.Production data analysis of tight gas condensate reservoirs[J].Journal of Natural Gas Science&Engineering,2015,22(3):22-34.
[10]曾保全,程林松,李春兰,等.特低渗透油藏压裂水平井开发效果分析[J].石油学报,2010,31(5):791-796.
[11]张春光,姜瑞忠,乔欣,等.双重介质致密分形气藏水平井压力动态分析[J].东北石油大学学报,2018,42(6):95-103.
[12]田冷,申智强,王猛,等.基于滑脱、应力敏感和非达西效应的页岩气压裂水平井产能模型[J].东北石油大学学报,2016,40(6):106-113.
[13]ZENG J,WANG X,GUO J,et al.Composite linear flow model for multi-fractured horizontal wells in tight sand reservoirs with threshold pressure gradient[J].Journal of Petroleum Science&Engineering,2018,165:890-912.
[14]JI J,YAO Y,HUANG S,et al.Analytical model for production performance analysis of multi-fractured horizontal well in tight oil reservoirs[J].Journal of Petroleum Science&Engineering,2017,158:380-397.
[15]WANG Y and SALEHI S.Refracture candidate selection using hybrid simulation with neural network and data analysis techniques[J].Journal of Petroleum Science&Engineering,2014,123:138-146.
[16]GUO J,XIAO Y,ZHU H.A new method for fracturing wells reservoir evaluation in fractured gas reservoir[J].Mathematical Problems in Engineering,2014,36(2):1-12.
[17]陶亮,郭建春,李凌铎.致密油藏水平井重复压裂多级选井方法研究[J].特种油气藏,2018,25(4):67-71.
[18]文华,刘义坤,孙娜.基于正交灰关联法的气藏水平井体积压裂影响因素研究[J].数学的实践与认识,2015,45(7):133-140.
[19]王威.致密油藏体积压裂初期产能预测[J].新疆石油地质,2016,37(5):575-579.
[20]周志军,薛江龙,李慧敏.压后增油量预测模型[J].特种油气藏,2013,20(3):76-78.
[21]朱兆群,林承焰,张苏杰,等.改进的模糊-灰色综合评判方法在储层定量评价中的应用[J].石油与天然气地质,2017,38(1):197-208.
[22]ZENG F,GUO J.A hybrid model of fuzzy logic and grey relation analysis to evaluate tight gas formation quality comprehensively[J].Journal of Grey System,2015,27(3):87-98.