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A mathematical correlation of porosity and permeability for Niger Delta depobelts formation using core analysis

机译:基于岩心分析的尼日尔三角洲变形带孔隙度与渗透率的数学关联

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There has been no universal mathematical relationship developed for porosity andpermeability for Niger Delta Depobelts formation using Core Analysis. The existingmathematical relationship between porosity and permeability developed from wirelinelogs has been complex due to the incorporation of several variables. Accurateestimation of rock property from core analysis is essential and needed for efficientreservoir characterization. A common practice to draw relationship between these twoparameters (porosity & permeability) is usually graphical, for instance using picketplots for general trends in the well logs data. Trends from picket plots are mostlyexplicit and subjective, that is, limited to each well and cannot be extrapolated to fullfield if the degree of heterogeneity is significant. However, core analysis gives a betterrepresentation of the sample; therefore, developing a mathematical relationship for itbecomes necessary. Statistical methods, that is regression analysis and probabilitydistribution were developed using appropriate core data. These techniques ultimatelyhelped in developing a linear mathematical model for prediction. This work focusedon correlating porosity and permeability using data obtained from core analysis in two(2) depobelts in Niger Delta. Each depobelt had several reservoirs along with theircorresponding pressures. Also, for best estimates, semi-log graphs were used togenerate decent goodness of fits. Also, the use of residual plots was presented to dealwith outliers that normally come from heterogeneity. Conclusively, from thecorrelations generated, more precise estimates of reserves/resources and production forecast can be made.
机译:尚未通过岩心分析为尼日尔三角洲Depobelts地层的孔隙度和渗透率开发通用的数学关系。由于结合了几个变量,由测井测井得出的孔隙度和渗透率之间的数学关系已经很复杂。从岩心分析准确估算岩石性质对于有效地进行储层表征至关重要,也是必需的。绘制这两个参数(孔隙度和渗透率)之间关系的常用方法通常是图形化的,例如,使用测井曲线对测井数据的总体趋势进行分析。纠察曲线的趋势主要是显性的和主观的,也就是说,仅限于每口井,并且如果异质性程度很大,则不能外推到全场。但是,核心分析可以更好地代表样本。因此,有必要建立数学关系。统计方法,即回归分析和概率分布是使用适当的核心数据开发的。这些技术最终有助于开发用于预测的线性数学模型。这项工作着重于使用从尼日尔三角洲两(2)个沉积带的岩心分析获得的数据来关联孔隙度和渗透率。每个depobelt都有几个储层及其相应的压力。同样,为了获得最佳估计,使用半对数图来生成体面的拟合优度。此外,提出了使用残差图来处理通常来自异质性的异常值。结论是,根据生成的相关性,可以对储量/资源和产量预测进行更精确的估计。

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