低孔低渗裂缝性储层建模及预测研究——以新疆百31断块二叠系佳木河组为例
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摘要
新疆百31断块二叠系佳木河组低孔、低渗复杂断块裂缝性油藏裂缝成因主要受构造、岩性、地层曲率等多种因素控制。通过随机建模技术建立该区裂缝性储层的静态地质模型,利用裂缝孔隙度、裂缝段厚度和油井产能数据建立的裂缝指数曲线描述了裂缝的储集能力及井间裂缝连通信息;使用模糊逻辑技术筛选裂缝的主要控制参数,根据神经网络算法将一系列与裂缝密切相关的岩石物性、地震属性及油气井产能数据有机整合在一起;通过对裂缝规模及分布的定量化模拟手段建立了该区裂缝性储层的预测模型,尤其定量化的裂缝分布方位、分布概率的估值方法及其误差分析技术,在寻找潜在的裂缝发育部位、减少勘探开发风险方面具有较高的参考价值。
With low porosity, low permeability and complex structure, the Permian fractured glutenite reservoir in B31 fault block which is located in the western margin of Junggar Basin is mainly controlled by the structure and lithology as well as formation curvature, etc. The static geomodel of fractured reservoirs is built with stochastic modeling approach for simulation. The author integrates a set of rock matrix properties and fracture-related seismic attributes and production data to simulate a 3D fracture distribution model with fracture indicator, fuzzy logic and neural net techniques. It also shows that the use of quantitative fracture distribution (orientation and probability) estimations, together with their error bars (confidence bounds), is a valuable tool for fracture reservoir characterization and the sweet-spots identification with reduced risks.
With low porosity, low permeability and complex structure, the Permian fractured glutenite reservoir in B31 fault block which is located in the western margin of Junggar Basin is mainly controlled by the structure and lithology as well as formation curvature, etc. The static geomodel of fractured reservoirs is built with stochastic modeling approach for simulation. The author integrates a set of rock matrix properties and fracture-related seismic attributes and production data to simulate a 3D fracture distribution model with fracture indicator, fuzzy logic and neural net techniques. It also shows that the use of quantitative fracture distribution (orientation and probability) estimations, together with their error bars (confidence bounds), is a valuable tool for fracture reservoir characterization and the sweet-spots identification with reduced risks.
引文
[1]Orlando J, Ortega1, Randall A, et al. Laubach, A Scale-Independent Approach to Fracture Intensity and Average Spacing Measurement, AAPG Bulletin, 2006,90(2):193～208.
[2]Ahmed Quenes, Abdel M, Zellou, et al, Seismic Driven Improved Fractured Reservoir Characterization, SPE92031, 2004.
[3]潘保芝,薛林福,李舟波,等.裂缝性火成岩储层测井评价方法与应用[M].北京:石油工业出版社,2003.
[4]苏朝光,刘传虎,高秋菊.胜利罗家地区泥岩裂缝油气藏地震识别与描述[J].石油地球物理勘探,2001,36(3):371～377.
[5]王家禄,刘玉章,陈茂谦,等.低渗透油藏裂缝动态渗吸机理试验研究[J].石油勘探与开发,2009,36(1):86～90.
[6]Carlos F, Haro. The Perfect Permeability Transform Using Logs and Cores, SPE 89516, 2004.
[7]张景和,孙宗颀.地应力、裂缝测试技术在石油勘探开发中的应用[M].北京:石油工业出版社,2001.
[8]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
[2]Ahmed Quenes, Abdel M, Zellou, et al, Seismic Driven Improved Fractured Reservoir Characterization, SPE92031, 2004.
[3]潘保芝,薛林福,李舟波,等.裂缝性火成岩储层测井评价方法与应用[M].北京:石油工业出版社,2003.
[4]苏朝光,刘传虎,高秋菊.胜利罗家地区泥岩裂缝油气藏地震识别与描述[J].石油地球物理勘探,2001,36(3):371～377.
[5]王家禄,刘玉章,陈茂谦,等.低渗透油藏裂缝动态渗吸机理试验研究[J].石油勘探与开发,2009,36(1):86～90.
[6]Carlos F, Haro. The Perfect Permeability Transform Using Logs and Cores, SPE 89516, 2004.
[7]张景和,孙宗颀.地应力、裂缝测试技术在石油勘探开发中的应用[M].北京:石油工业出版社,2001.
[8]蒋宗礼.人工神经网络导论[M].北京:高等教育出版社,2001.
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