Seismic prediction of shale oil lithofacies associations based on sedimentary facies patterns: A case study of the shahejie formation in the Huanghekou Sag - He Zhao; Hongliang Wang; Guangyuan Wang; Lin Wan (PLoS ONE, 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.