用测井数据无监督聚类，识别加纳海上基塔盆地岩相与孔隙度特征。
Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs
- 基于六类测井曲线在多维空间聚类，无须岩心数据。
- 识别出4个电性岩相，平均轮廓系数0.50，分离效果中等但合理。
- 适合缺乏岩心的海域盆地早期评价，可为后续研究奠基。
本研究提出一种无监督机器学习流程，用于加纳海上基塔盆地的电性岩相分析，该区域岩心数据稀缺。对井C的六个标准测井曲线在约11,195个样本的深度范围内进行分析。采用K均值聚类方法，在多变量测井空间中划分聚类结构，并通过惯性与轮廓系数诊断评估。最终识别出4个聚类，平均轮廓系数约为0.50，表明存在中等但有意义的分离。所得电性岩相呈现系统性、连续的深度分布模式，与黏土含量、孔隙度及岩石骨架性质变化相关，形成从泥岩主导到清洁砂岩主导的地质连续体。结果表明，仅依赖测井数据的无监督聚类结合定量指标，可提供可靠且可复现的地下表征框架。该工作流程为前缘海上盆地的早期储层评价提供实用工具，并为未来集成研究奠定基础。
原文摘要 · Abstract (English)
This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce. Six standard wireline logs from Well~C were analysed over a depth interval comprising approximately $11{,}195$ samples. K-means clustering was applied in multivariate log space, with the clustering structure evaluated using inertia and silhouette diagnostics. Four clusters were identified, supported by an average silhouette coefficient of approximately $0.50$, indicating moderate but meaningful separation. The resulting electrofacies exhibit systematic, depth-continuous patterns associated with variations in clay content, porosity, and rock framework properties, forming a geological continuum from shale-dominated to cleaner sandstone-dominated units. The results demonstrate that log-only, unsupervised clustering supported by quantitative metrics provides a robust and reproducible framework for subsurface characterisation. The proposed workflow offers a practical tool for early-stage formation evaluation in frontier offshore basins and a foundation for future integrated studies.
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