表征井间地层参数及油水动态的连通性计算方法
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摘要
为研究井间连通性,利用注采数据建立了可模拟井间油水动态的新连通性计算模型。该模型对油藏注采系统进行了简化表征,将其离散成井与井之间的连通单元,每个单元由传导率和控制体积两个特征参数表征;基于连通单元通过物质平衡和水驱前缘推进方程进行井点压力计算和饱和度追踪,最终得出井点处的油水产出动态;在此基础上结合投影梯度算法通过拟合实际动态建立了模型参数自动反演方法。该模型相比传统连通性模型可以进行油水动态指标的预测、实时反映井间相互作用和连通状况、有效表征油藏井间地层参数。应用实例表明,其反演结果可靠,并与示踪剂测试结果吻合,进一步验证了该方法的有效性。
To study interwell connectivity,a novel numeric simulation model(ICNS)is established for oil-water two-phase flow using injection-production data.The ICNS model simplifies reservoir injection-production system and divides it into a number of interwell connective units,each of which is characterized by two parameters,transmissibility and control pore volume.Well pressure calculation and saturation tracking are implemented by solving material balance and front tracking equations for the connective units.Finally,oilwater dynamic production data of the well are obtained.Combined with the projection gradient algorithm,a model parameter inversion method for the ICNS model is established by fitting the actual dynamic data.Compared with conventional interwell connectivity estimation models,the ICNS model is able to predict oil-water two-phase dynamic production,reflect real-time interwell interaction and connectivity,and effectively characterize interwell geological parameters of reservoir.Field application of the ICNS model indicates that the result of inversion is reliable and coincident with tracer test data,further verifying the validity of the proposed method.
To study interwell connectivity,a novel numeric simulation model(ICNS)is established for oil-water two-phase flow using injection-production data.The ICNS model simplifies reservoir injection-production system and divides it into a number of interwell connective units,each of which is characterized by two parameters,transmissibility and control pore volume.Well pressure calculation and saturation tracking are implemented by solving material balance and front tracking equations for the connective units.Finally,oilwater dynamic production data of the well are obtained.Combined with the projection gradient algorithm,a model parameter inversion method for the ICNS model is established by fitting the actual dynamic data.Compared with conventional interwell connectivity estimation models,the ICNS model is able to predict oil-water two-phase dynamic production,reflect real-time interwell interaction and connectivity,and effectively characterize interwell geological parameters of reservoir.Field application of the ICNS model indicates that the result of inversion is reliable and coincident with tracer test data,further verifying the validity of the proposed method.
引文
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[15]袁亚湘,孙文瑜.最优化理论与方法[M].北京:科学出版社,1997.Yuan Yaxiang,Sun Wenyu.Optimization theory and methods[M].Beijing:Science Press,1997.
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[17]赵辉,曹琳,李阳,等.基于改进随机扰动近似算法的油藏生产优化[J].石油学报,2011,32(6):1031-1036.Zhao Hui,Cao Lin,Li Yang,et al.Production optimization of oil reservoirs based on an improved simultaneous perturbation stochastic approximation algorithm[J].Acta Petrolei Sinca,2011,32(6):1031-1036.
[18]Zhao Hui,Li Yang,Yao Jun,et al.Theoretical research on reservoir closed-loop production management[J].Science China Technological Sciences,2011,54(10):2815-2824.
[2]廖红伟,王琛,左代荣.应用不稳定试井判断井间连通性[J].石油勘探与开发,2002,29(4):87-89.Liao Hongwei,Wang Chen,Zuo Dairong.Applying transient testing to the judgment of inter-well connectivity[J].Petroleum Exploration and Development,2002,29(4):87-89.
[3]杜娟,杨树敏.井间微地震监测技术现场应用效果分析[J].大庆石油地质与开发,2007,26(4):120-122.Du Juan,Yang Shumin.Analysis to the field application effect of inner well microseismic monitoring technique[J].Petroleum Geology&Oilfield Development in Daqing,2007,26(4):120-122.
[4]Albertoni A,Lake L W.Inferring interwell connectivity only from well-rate fluctuations in waterfloods[R].SPE83381,2003.
[5]Gentil P H.The use of multilinear regression models in patterned waterfloods:physical meaning of the regression coefficients[D].Austin:The University of Texas,2005.
[6]张明安.油藏井间动态连通性反演方法研究[J].油气地质与采收率,2011,18(3):70-73.Zhang Ming’an.Study on inversion for reservoir inter-well dynamic connectivity[J].Petroleum Geology and Recovery Efficiency,2011,18(3):70-73.
[7]Yousef A A,Gentil P H,Jensen J L,et al.A capacitance model to infer interwell connectivity from production and injection rate fluctuations[R].SPE95322,2006.
[8]Yousef A A,Lake L W,Jensen J L.Analysis and interpretation of interwell connectivity from production and injection rate fluctuation using a capacitance model[R].SPE99998,2006.
[9]Sayarpour M,Kabir S,Sepehrnoori K,et al.Probabilistic history matching with the capacitance-resistance model in waterfloods:a precursor to numerical modeling[R].SPE129604,2010.
[10]Nguyen A P,Kim J S,Lake L W,et al.Integrated capacitance resistive model for reservoir characterization in primary and secondary recovery[R].SPE147344,2011.
[11]Salazar-Bustamante M,Gonzalez-Gomez H,Matringe S F,et al.Combining decline-curve analysis and capacitance/resistance models to understand and predict the behavior of a mature naturally fractured carbonate reservoir under gas injection[R].SPE153252,2012.
[12]赵辉,李阳,高达,等.基于系统分析方法的油藏井间动态连通性研究[J].石油学报,2010,31(4):633-636.Zhao Hui,Li Yang,Gao Da,et al.Research on reservoir interwell dynamic connectivity using systemic analysis method[J].Acta Petrolei Sinica,2010,31(4):633-636.
[13]Gherabati S A,Hughes R G,Zhang Hongchao,et al.A large scale network model to obtain interwell formation characteristics[R].SPE153386,2012.
[14]宋考平,吴玉树,计秉玉.水驱油藏剩余油饱和度分布预测的φ函数法[J].石油学报,2006,27(3):91-95.Song Kaoping,Wu Yushu,Ji Bingyu.Aφ-fanction method for estimating distribution of residual oil saturation in water drive reservoir[J].Acta Petrolei Sinica,2006,27(3):91-95.
[15]袁亚湘,孙文瑜.最优化理论与方法[M].北京:科学出版社,1997.Yuan Yaxiang,Sun Wenyu.Optimization theory and methods[M].Beijing:Science Press,1997.
[16]赵辉,李阳,康志江.油藏开发生产鲁棒优化方法[J].石油学报,2013,34(5):947-953.Zhao Hui,Li Yang,Kang Zhijiang.Robust optimization in oil reservoir production[J].Acta Petrolei Sinica,2013,34(5):947-953.
[17]赵辉,曹琳,李阳,等.基于改进随机扰动近似算法的油藏生产优化[J].石油学报,2011,32(6):1031-1036.Zhao Hui,Cao Lin,Li Yang,et al.Production optimization of oil reservoirs based on an improved simultaneous perturbation stochastic approximation algorithm[J].Acta Petrolei Sinca,2011,32(6):1031-1036.
[18]Zhao Hui,Li Yang,Yao Jun,et al.Theoretical research on reservoir closed-loop production management[J].Science China Technological Sciences,2011,54(10):2815-2824.
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