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  

Geomodeling and Prediction Study of Permian Jiamuhe Fractured Glutenite Reservoir in B31 Fault Block, XinJiang Oilfield

作　　者：姚军辉[1] 张晓晖[2] 王延宾[1] 黄锋[2] 许长福[3] 戴灿星[3] 

机构地区：[1]中国矿业大学(北京),北京100083 [2]中国石油勘探开发研究院,北京100083 [3]中国石油新疆油田公司,新疆克拉马依市834000

出　　处：《中国石油勘探》2010年第5期37-40,47,共5页China Petroleum Exploration

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.

关 键 词：裂缝性储层建模 裂缝指数 模糊逻辑 神经网络 裂缝分布概率 

分 类 号：TE112.2[石油与天然气工程—油气勘探]