基于岩石物理相分类确定致密储层孔隙度——以苏里格气田东区为例  

Porosity estimation of tight reservoirs based on petrophysical facies classification: A case study from the east area of the Sulige Gas Field,Ordos Basin

作　　者：宋子齐[1] 景成[2] 庞玉东[1] 田新[1] 张景皓[3] 

机构地区：[1]西安石油大学石油工程学院 [2]中国石油大学(华东)石油工程学院 [3]西安石油大学地球科学与工程学院

出　　处：《天然气工业》2013年第8期31-37,共7页Natural Gas Industry

基　　金：中国石油天然气股份有限公司科学研究与技术开发项目(编号:2010E-2304);国家自然科学基金项目"变形介质复杂储层应力敏感性的岩石流变学机理及动态模型"(编号:51104119)

摘　　要：针对鄂尔多斯盆地苏里格气田东区致密储层受多期不同类型沉积、成岩作用及构造等因素影响,储层孔隙空间小、孔隙类型结构和测井响应复杂等问题,分析了致密储层岩石物理相分类评价体系,从而提出利用岩石物理相分类确定致密储层孔隙度的技术思路。利用研究区致密储层各类测井、岩心及试气资料,建立了不同类别岩石物理相储层孔隙度参数解释模型;在分类岩心刻度测井解释模型的数据点分布拟合中,分类模型具有相对集中分布趋势及较好的线性关系;特别是分别利用分类的密度、声波时差孔隙度参数模型的综合拟合值来求取有效孔隙度参数,这集中地体现出岩性、物性、孔隙类型结构及测井响应特征与差异,明显改善和提高了致密储层孔隙度参数的计算精度和效果,克服了致密储层低信噪比、低分辨率的评价缺陷。现场应用效果表明,致密储层分类建模技术实现了将非均质、非线性的问题转化为相对均质、线性的问题来解决,为准确建立致密储层参数模型提供了有效方法。Tight reservoirs in the Sulige Gas Field, Ordos Basin, are influenced by multistage deposits with different types, diagenesis and structures, and are characterized by small pore space, complex pore types and structures and complex logging responses. In view of this, we analyzed the classification evaluation system of petrophysical facies of tight reservoirs, and proposed a technical solution to the estimation of tight reservoir porosity via petrophysical facies classification. Reservoir parameter interpretation models were built for different types of petrophysical facies by using the available logging, core and formation test data obtained from the tight reservoirs in the study area. During fitting of data point distribution obtained from the calibration of logging interpretation models with classification core data, the classification models showed relatively clustered distribution trend and relatively good linear relationship. Especially when effective porosity parameters were estimated by using the comprehensive fitting values of classification density models and those of interval transit time porosity parameter models separately, the features and differences of lithology, physical property, pore type and structure, as well as logging responses were highlighted, and the accuracy of porosity estimation of tight reservoirs was significantly improved.The problems of low signaltonoise ratio and low resolution of tight reservoirs are successfully solved. Field application shows that classification modeling of tight reservoirs can convert a heterogeneous nonlinear problem to a relatively homogeneous and linear one, thus providing an effective method for building tight reservoir parameter models of high quality.

关 键 词：致密砂岩 储集层 岩石物理相分类 数学模型 拟合 分类特征 非均质性 计算 苏里格气田 

分 类 号：P618.13[天文地球—矿床学]