致密砂岩储层复杂油水层成因分析与测井评价——以鄂尔多斯盆地宁县地区延长组6段为例

Cause analysis and logging evaluation of complex oil-water layer in tight sandstone reservoir: a case study of the 6th member of Yanchang Formation in Ningxian Area, Ordos Basin

摘要

致密砂岩储层非均质性强,孔隙结构复杂,微弱地质信号增加测井解释的多解性,油水层识别难度大。以鄂尔多斯盆地宁县地区延长组6段砂岩储层为研究对象,采用岩性、物性、孔隙结构、流体性质及其赋存状态实验,分析研究区复杂油水层成因;采用BP神经网络法建立储层物性、含油性测井解释模型,采用交会图版法和随机森林法建立流体性质识别模型。结果表明:黏土矿物附加导电、碳酸盐胶结物和炭质沥青堵塞孔隙、原油以束缚态和半束缚态形式存在,是研究区延长组6段致密油储层油水关系复杂的主要因素;采用BP神经网络法建立的储层物性、含油性测井解释模型,可提高储层参数计算精度;采用交会图版法可有效识别油层、油水同层和水层(包括含油水层);采用随机森林法建立的流体性质识别模型,可精确区分油层、一类油水同层(日产油2t以上)、二类油水同层(日产油2t以下)及水层(包括含油水层),提高测井解释符合率。该结果为鄂尔多斯盆地宁县地区延长组6段储层的经济开发和有效动用提供支持。

Abstract

The tight sandstone reservoir has strong heterogeneity and complex pore structure. Weak geological signals increase the multiplicity of logging interpretation, and it is difficult to identify oil and water layers. Taking the sandstone reservoir of the 6th member of Yanchang Formation in Ningxian Area of Ordos Basin as the research object, the genesis of complex oil and water layers in the study area was analyzed by experiments on lithology, physical property, pore structure, fluid property and fluid occurrence state of the cores. The BP neural network method is used to establish reservoir physical property and oiliness logging interpretation models, the fluid property identification model is established by cross-plot method and random forest method. The results show that the additional conductivity of rock clay minerals, the blockage of pores by carbonate cements and carbonaceous asphalt, the crude oil in the form of bound state and semi-bound state are the main factors for the complex oil-water relationship in the tight oil reservoirs of the 6th member of Yanchang Formation in the study area. The interpretation models of reservoir physical property and oiliness established by BP neural network method, which can improve the calculation accuracy of reservoir parameters. The intersection chart of porosity and oil saturation can effectively identify oil layers, oil-water layers and water layers (including oil-bearing water layers). The fluid property identification model based on random forest algorithm can accurately distinguish oil layer, such as class Ⅰ oil-water layer (more than 2 t/d), class Ⅱ oil-water layer (less than 2 t/d) and water layer (including oil-bearing water layer). The results can improve the coincidence rate of logging interpretation greatly and provide technical support for the economic development and effective utilization of reservoirs in the study area.

关键词

致密砂岩 / 复杂油水层 / 孔隙结构 / BP神经网络 / 随机森林 / 延长组6段 / 鄂尔多斯盆地

Key words

tight sandstone / complex oil-water layer / pore structure / BP neural network / random forest / 6th member of Yangchang Formation / Ordos Basin

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基金资助

国家自然科学基金项目(42172163)