Eftekhari Seyedeh Hajar, Memariani Mahmoud, Maleki Zahra, Aleali Mohsen, Kianoush Pooria
Department of Earth Sciences, Science and Research Branch, Islamic Azad University, 1477893855, Tehran, Iran.
Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Sci Rep. 2024 Feb 29;14(1):5003. doi: 10.1038/s41598-024-55741-y.
Rock types are the reservoir's most essential properties for special facies modeling in a defined range of porosity and permeability. This study used clustering techniques to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units (HFUs) were determined for studied data utilizing histogram analysis, normal probability analysis, and the sum of squared errors (SSE) statistical methods. Then, two flow zone index (FZI) and fuzzy c-means (FCM) clustering methods were used to determine the rock types in the given well according to the results obtained from the HFU continuity index acts in-depth. The FCM method, with a continuity number of 3.12, compared to the FZI, with a continuity number of 2.77, shows more continuity in depth. The relationship between permeability and porosity improved considerably by utilizing HFU techniques. This improvement is achieved using the FZI method study. Generally, all samples increased from 0.55 to 0.81 in the first HFU and finally to 0.94 in the fourth HFU. Similar flow properties in an HFU characterized the samples. In comparison, the correlation coefficients obtained in the FCM method are less than those in the general case of all HFUs. This study aims to determine the flowing fluid in the porous medium of the Asmari reservoir employing the c-mean fuzzy logic. Also, by determining the facies of the rock units, especially the siliceous-clastic facies and log data in the Asmari Formation, the third and fourth flow units have the highest reservoir quality and permeability. Results can be compared to determining HFU in nearby wellbores without cores.
岩石类型是储层在特定孔隙度和渗透率范围内进行特殊相建模的最基本属性。本研究采用聚类技术，对伊朗西南部曼苏里油田阿斯马里储层一口井所钻取的280个岩芯样本进行岩石类型识别。利用直方图分析、正态概率分析和误差平方和（SSE）统计方法，为研究数据确定了四个水力流动单元（HFU）。然后，根据从HFU连续性指数深入分析得到的结果，使用两种流动带指数（FZI）和模糊c均值（FCM）聚类方法来确定给定井中的岩石类型。与连续性数值为2.77的FZI方法相比，连续性数值为3.12的FCM方法在深度上显示出更高的连续性。通过使用HFU技术，渗透率与孔隙度之间的关系得到了显著改善。这种改善是通过FZI方法研究实现的。一般来说，所有样本在第一个HFU中从0.55增加到0.81，最终在第四个HFU中增加到0.94。HFU中具有相似的流动特性表征了这些样本。相比之下，FCM方法中获得的相关系数小于所有HFU一般情况下的相关系数。本研究旨在采用c均值模糊逻辑确定阿斯马里储层多孔介质中的流动流体。此外，通过确定岩石单元的相，特别是阿斯马里组中的硅质碎屑相和测井数据，第三和第四流动单元具有最高的储层质量和渗透率。可以将结果与确定附近无岩芯井筒中的HFU进行比较。
