The Horn River Basin is a major shale gas play in British Columbia, Canada. An important component for identifying productive zones is to determine lithofacies. Quantitative core-based lithofacies classifications distinguish reservoirs and non-reservoir quality zones. In this study, Elemental capture spectroscopy (ECS) log data, XRD, and TOC data were integrated with core-description data to define the shale gas development interval in the Horn River Shale. Six lithofacies were determined and used as a training data for the supervised electrofacies classification. The Multi-Resolution Graph-based Clustering method (MRGC) was useful for prediction of electrofacies. The MRGC facilitated estimation of the electrofacies from the well logs of the non-coring wells based on our current construction model. We constructed a supervised MRGC model from well A. The probability scores of the electrofacies were 83%. This method was applied to non-coring wells B and C. Lateral variations in the facies were inferred using an electrofacies correlation or seismic-scale spatial model. Stochastic methods were applied to build a 3D facies model due to the insufficient electrofacies data. To increase the accuracy and applicability we used seismically derived-trend data (density and P-wave sonic inversion, envelope attributes) for sequential indicator simulation (SIS) modeling. After constructing a facies model, the faintly laminated siliceous mudstone (FLSM) and homogeneous siliceous mudstone (HSM) facies were shown to be dominantly distributed throughout the Evie Member and Muskwa Formation. These facies contain high silica compared to clay and higher TOC contents which are generally the main targets of a shale gas play. Therefore, the FLSM and HSM facies of the Muskwa Formation and Evie Member are potentially productive facies for shale gas development in the Horn River Shale.

中文翻译：

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基于多分辨率图的聚类在电相预测中的应用：以加拿大不列颠哥伦比亚省的霍恩河页岩为例

霍恩河流域是加拿大不列颠哥伦比亚省的主要页岩气矿床。识别生产区的重要组成部分是确定岩相。基于岩心的定量岩相分类区分了储层和非储层质量带。在这项研究中，元素捕获光谱（ECS）日志数据，XRD和TOC数据与岩心描述数据集成在一起，以定义霍恩河页岩中的页岩气开发间隔。确定了六个岩相，并将其用作监督电相分类的训练数据。基于多分辨率图的聚类方法（MRGC）可用于预测电相。MRGC可以根据我们目前的构造模型，根据非取心井的测井数据估算电相。我们从A井构建了一个有监督的MRGC模型。电相的概率得分为83％。将该方法应用于非取芯井B和C。使用电相相关性或地震尺度空间模型推断出相的横向变化。由于电相数据不足，随机方法被用于建立3D相模型。为了提高准确性和适用性，我们将地震衍生趋势数据（密度和P波声波反演，包络属性）用于顺序指示器模拟（SIS）建模。在建立了一个相模型之后，表明了薄叠层硅质泥岩（FLSM）和均质硅质泥岩（HSM）相在整个埃维成员和马斯克瓦组中占主导地位。与粘土相比，这些相含有较高的二氧化硅，而较高的TOC含量通常是页岩气开采的主要目标。因此，Muskwa组和Evie成员的FLSM和HSM相可能是霍恩河页岩中页岩气开发的潜在生产相。