The issue of identifying stratigraphic units within a sedimentary succession is of prime importance for reservoir studies, because it allows splitting the reservoir into several units with specific parameters, thus reducing the vertical nonstationarity in simulations. A new method is proposed for semi-automatic determination of the sedimentary units from well logging that uses a customized geostatistical hierarchical clustering algorithm. A new linkage criteria derived from the Ward criteria (cluster minimum variance) is proposed to enforce the monotonic increase of dissimilarities. The discretized proportion of sand lithofacies calculated from the vertical proportion curve of the well is taken as input data. At each step of the procedure, the algorithm merges the most similar of two consecutive units of sand lithofacies, ensuring stratigraphic consistency. Finally, the number of units is deduced from the first most important step of the dissimilarity. The user can investigate a larger number of units by considering the clusters with lower levels of dissimilarities. The method is validated using two synthetic cases built for a fluvial meandering reservoir analog containing three and five units. The results from the synthetic cases show that the units are identified when the sand proportion contrast between units is larger than the internal variability within the units. For low sand contrasts between units or for a small number of wells, sedimentary unit limits may be found for lower clustering dissimilarities. Finally, the method is successfully applied to a field study, where the resulting cluster units are found to be comparable to the field interpretation, suggesting a limit between units defined by paleosols rather than close overlying lacustrine levels.

中文翻译：

---

根据油井数据自动确定沉积单元

识别沉积层序中的地层单位的问题对于储层研究至关重要，因为它可以将储层分成具有特定参数的几个单元，从而减少了模拟的垂直非平稳性。提出了一种新的方法，该方法使用定制的地统计分层聚类算法从测井中半自动确定沉积单元。提出了一种从Ward准则（集群最小方差）派生的新链接准则，以强制差异的单调增加。根据井的垂直比例曲线计算得出的砂岩相的离散比例作为输入数据。在该过程的每个步骤中，该算法都会合并两个连续的砂岩相中最相似的一个，确保地层的一致性。最后，从相异性的第一个最重要的步骤推导出单位数。用户可以通过考虑具有较低相似度的聚类来研究大量单元。使用两个合成案例验证了该方法，该案例针对包含三个和五个单元的河流曲折储层类似物而构建。综合案例的结果表明，当单元之间的砂比例对比度大于单元内部的内部变异性时，就可以识别出这些单元。对于单元之间或少数井之间的低砂差，可能会发现聚类差异较小的沉积单元界限。最后，该方法成功应用于野外研究，发现所产生的聚类单位与野外解释具有可比性，