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    非均质致密砂岩储层孔隙结构测井智能分级方法

    Intelligent pore structure classification method from well logs in heterogeneous tight sandstone reservoirs

    • 摘要: 致密砂岩气藏是油气勘探领域的主要目标之一,其储层孔隙度小,渗透率低,非均质性强,孔隙结构复杂,储层评价面临严峻挑战。目前对孔隙结构表征的测井评价方法具有局限性,迫切需要深入研究。压汞实验能够得到表征储层孔喉配置关系及渗流能力的数据,核磁共振能够准确描述不同组分孔隙分布,将两种手段联合,用于分析致密砂岩储层孔隙结构。根据岩心压汞及核磁共振实验结果,提取了对孔隙结构敏感的6个参数,分别建立了6个孔隙结构参数与核磁共振组分的孔隙度关系,实现了孔隙结构参数的测井解释。研究了K均值聚类及主成分分析、降维分析的算法和步骤,对孔隙结构参数测井解释结果进行聚类,实现了孔隙结构的智能分级。将该方法应用于鄂尔多斯盆地韦州地区羊虎沟组的孔隙结构分级,结果表明,该方法提高了孔隙结构分级的准确性,可以应用于其他低孔低渗致密砂岩储层的孔隙结构分级。

       

      Abstract: Tight sandstone gas reservoir, a significant resource in hydrocarbon exploration, is characterized by low porosity, low permeability, strong heterogeneity, and complex pore structures. The log interpretation and reservoir evaluation are facing challenges. Current pore structure evaluation methods from well logs are limited, new log interpretation methods require investigation. Mercury injection capillary pressure (MICP) experiment serves as an effective means to characterize pore-throat structure and flow capacity, and nuclear magnetic resonance (NMR) can describe the distribution of different pore components. Combining these two methods, the pore structure of tight sandstone reservoir is obtained. Based on core MICP and NMR experiments, six parameters sensitive to pore structure are extracted, relationships between pore structure parameters and NMR component porosity are established, and the pore structure evolution from NMR log interpretation is achieved. For pore structure classification, K-means clustering algorithm, principal component analysis, and dimensionality reduction techniques are investigated from pore structure parameters of log interpretation. The above methods are applied to the Yanghugou Formation in the Weizhou area of the Ordos Basin. The pore structure classification was completed. Some case studies indicate that these methods improve the accuracy of pore structure classification and are suitable for other low-porosity, low-permeability tight sandstone reservoirs.

       

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