This article integrates core and well log data to determine reservoir electrofacies of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. At the start, an unsupervised neural network was employed based on the selforganizing map (SOM) technique to identify and extract electrofacies groups of Asmari Formation in the Marun oilfield, which is a mixed siliciclastic and carbonate reservoir. Using inexpensive and accessible petrophysical wireline logs, such as gamma ray, sonic, density and neutron, along with calculated reservoir data (water saturation and effective porosity) from 12 reference wells and their correlation with reservoir core data, led to recognition of seven electrofacies including first four class as limestone rocks (EF1–EF4) and others three classes as sandstone rocks (EF5–EF7). Based on the porosity and permeability maps and water saturation at different depth levels, EF4 and EF7 with low porosity and permeability, high water saturation are considered as non-reservoir relating mostly to sedimentary textures of mudstone and argillaceous sandstones, respectively. By contrast, EF1 and EF5 with high values of porosity, permeability and low percent of water saturations characterize the best reservoir quality rocks, and EF2, EF3 and EF6 have medium reservoir quality. EF1 consists of dolomitic skeletal grainstone, in which biomoldic, vuggy and intercrystalline porosity is the dominant pore type, while the intergranular porosity is the major pore type in EF4 as an unconsolidated sand electrofacies. In general, the results obtained in this study indicate a satisfactory agreement between core data and log facies. This correlation allows rockfacies to be classified in the cored wells and predict those facies in the uncored wells. Using the methodology outlined here, it is possible to track reservoir electrofacies from conventional well log data.

中文翻译：

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伊朗西南部马鲁恩油田Asmari油藏的电相分析

本文整合了岩心和测井数据，以确定伊朗西南部Dezfol Embayment西部Oligo-中新世Asmari组的储层电相。开始时，基于自组织图（SOM）技术采用了无监督神经网络，以识别和提取Marun油田中的Asmari地层的电相群，该地区是硅质碎屑和碳酸盐混合储层。使用廉价且可访问的岩石物理测井曲线（例如伽马射线，声波，密度和中子），以及来自12个参考井的计算储层数据（水饱和度和有效孔隙度）及其与储层岩心数据的相关性，可识别出七个电相，包括前四类为石灰岩（EF1-EF4），其他三类为砂岩（EF5-EF7）。根据孔隙度和渗透率图以及不同深度水平的水饱和度，低孔隙度和渗透率的EF4和EF7，高水饱和度被认为是非储层，分别与泥岩和泥质砂岩的沉积质地有关。相反，具有高孔隙率，渗透率和低水饱和度值的EF1和EF5代表了最佳储层质量的岩石，而EF2，EF3和EF6具有中等储层质量。EF1由白云质骨架花岗石组成，其中生胶，孔洞和晶间孔隙是主要的孔隙类型，而粒间孔隙是EF4中的主要孔隙类型，是非固结的砂电相。通常，本研究中获得的结果表明核心数据和测井相之间令人满意的一致性。这种相关性使得岩相可以在有芯井中进行分类，并预测无芯井中的那些相。使用此处概述的方法，可以从常规测井数据跟踪储层电相。