Zhao He, Wang Hongliang, Wang Guangyuan, Wan Lin
School of Energy Resources, China University of Geosciences, Beijing, China.
Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism, China University of Geosciences, Beijing, China.
PLoS One. 2025 Sep 18;20(9):e0332314. doi: 10.1371/journal.pone.0332314. eCollection 2025.
The lithofacies play a pivotal role in studying development patterns, reservoir characteristics, and sweet spot predictions of shale oil. Lithofacies classification typically relies on core observations and conventional well logging analyses, whereas seismic attribute extraction is often employed in regions with sparse or absent wells. However, seismic attribute extraction entails considerable computation and time, and exclusive reliance on seismic attribute analysis can result in multiple interpretations. This paper emphasizes predicting shale oil lithofacies associations based on seismic reflection characteristics and sedimentary facies patterns which can can help avoid these issues. The lithofacies classification scheme has identified seven lithofacies and associations by means of core observations, testing data, and logging curve analysis of the Shahejie Formation in the Huanghekou Sag. Through well-seismic calibration, the seismic reflections and sedimentary patterns of different lithofacies associations were examined to formulate a seismic facies identification chart and propose six models. For areas without wells, based on the distribution of sedimentary facies and in combination with seismic reflection characteristics, identification and delineation are conducted on a planar scale to analyze the distribution features of lithofacies associations. The results of predicting the distribution of shale oil lithofacies associations in the Shahejie Formation indicate that the development pattern of lithofacies associations is basically consistent with that of sedimentary facies units. The primary models developed in the study area encompass delta, sublacustrine fan, and shore-shallow lake. The approach of identifying shale oil lithofacies associations based on seismic reflection and sedimentary backgrounds offers a novel means for discerning lithofacies and associations in sections devoid of cores and specialized well logging data.
岩相在研究页岩油的发育模式、储层特征和甜点预测方面起着关键作用。岩相分类通常依赖于岩心观察和常规测井分析，而在井网稀疏或无井地区则常采用地震属性提取。然而，地震属性提取需要大量计算和时间，单纯依靠地震属性分析可能会导致多种解释。本文强调基于地震反射特征和沉积相模式预测页岩油岩相组合，这有助于避免这些问题。通过对黄河口凹陷沙河街组的岩心观察、测试数据和测井曲线分析，岩相分类方案确定了七种岩相和组合。通过井震标定，研究了不同岩相组合的地震反射和沉积模式，制定了地震相识别图，并提出了六种模型。对于无井地区，根据沉积相分布并结合地震反射特征，在平面尺度上进行识别和圈定，分析岩相组合的分布特征。沙河街组页岩油岩相组合分布预测结果表明，岩相组合的发育模式与沉积相单元基本一致。研究区主要发育模式包括三角洲、湖底扇和滨浅湖。基于地震反射和沉积背景识别页岩油岩相组合的方法，为在没有岩心和特殊测井资料的剖面中识别岩相和组合提供了一种新手段。
