摘要
随着勘探程度的逐步深入,隐蔽油气藏的勘探日益被重视,寻找新的储层已成为当前各油田所面临的重要研究课题。砂砾岩体油气藏一直是寻找隐蔽性油气藏的有利地区,而该类油气藏一般具有埋藏深、岩性复杂、低孔低渗、孔隙结构复杂、非均质性强等特点,导致有效储层与非有效储层难以划分,油水层无法准确判断。砂砾岩体测井储层评价、油气层解释、地质和工程应用在国内外石油勘探领域均属评价难题,都需要新的理论和先进的工程技术来支撑。
砂砾岩体油气藏的复杂性使该类油气藏储层测井评价受到了严重的挑战,造成用传统的测井评价方法难以准确计算储层参数和确定储层流体性质,进而使得测井解释结果与测试、试油试采结果不吻合。为此提出了采用测井相分析技术,根据测井响应特征首先将储集层按其岩石物理特征划分为不同类型的岩石物理相,然后按岩石物理相类型建立相应的测井解释模型的方法。该方法使同类岩石物理相具有相似的岩石学特征,孔-渗关系呈现出规律性变化,表现出相似的岩电关系和测井响应特征,从而将地层非均质性问题转变为相对均质性问题。在此基础上,选取和油水层关系密切的测井参数或计算的参数,采用主成分分析等数学方法,提取反映油水层特征的综合特征参数,进行油水层判别。
应用提出的方法及开发的软件对研究区14口井的资料进行了实际处理,取得了良好的效果,有效地提高了储层参数解释的精度和油水层判别的准确度。
With the development of oil prospecting, it is very important to find new oil-gas reservoir, we pay more attention on conceal oil-gas reservoir and glutenite reservoir is one type of good conceal oil-gas reservoir. Glutenite reservoir which has strong aeolotropism and complex lithology and ultra low porosity is difficult to evaluate with well logging data. It’s hard to identify effective reservoir and oil-water layer. The reservoir evaluation with well logging data and the discrimination of oil-water and the application of geology are all difficult problems to the field of oil prospecting at home and aboard, so it requires new theories and advanced technologies to support.
It is such a serious challenge for reservoir logging evaluation because of the complexity of glutenite reservoir, and reservoir parameters can not be calculated accurately and reservoir fluid properties also can not be determined accurately by traditional logging evaluation methods which lead to that the logging interpretation results are different from the test results. The paper makes use of electrofacies analysis technique, divides the whole reservoir into several lithophases according to logging response, then builds models for different lithophases. The method makes every lithophase have similar logging response, lithology characteristic and regular for porosity, permeability. We translate aeolotropic, nonlinear issues into homogeneous, linear issues. On basis of reservoir parameter calculation, we select the well logging or the reservoir parameters which closely relate to the oil-water, and we form the new parameters by electrofacies analysis to identify oil–water layer.
Applying the methods and the newly developed software, logging data of 14 wells of the region have been processed and the results are satisfactory. The accuracy of reservoir parameters and oil–water layer identification are obviously improved.
引文
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