Shao Yuyang, Zhang Qiang, Li Yuanji, Luan Zhisheng, Tang Baiqiang
Department of Safety Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China.
National Professional Center Lab of Safety Basic Research for Hydrocarbon Gas Pipeline Transportation Network, Harbin 150022, China.
ACS Omega. 2023 Jun 14;8(25):23032-23043. doi: 10.1021/acsomega.3c02198. eCollection 2023 Jun 27.
The fluctuation of lake levels in semi-deep and deep lake environments has long been a central topic in the study of ancient lake evolution. This phenomenon has a significant impact on the enrichment of organic matter and the overall ecosystem. The study of lake-level changes in deep lake environments is hindered by the scarcity of records in continental strata. To address this issue, we conducted a study on the Eocene Jijuntun Formation in Fushun Basin, specifically focusing on the LFD-1 well. Our study involved finely sampling the extremely thick oil shale (about 80 m), which was deposited in the semi-deep to deep lake environment of the Jijuntun Formation. The TOC was predicted by multiple methods, and the lake level study was restored by combining logging INPEFA and Dynamic noise after orbital tuning (DYNOT) techniques. The oil shale of the target layer is type I kerogen, and the source of organic matter is basically the same. The γ ray (GR), resistivity (RT), acoustic (AC), and density (DEN) logging curves are in the normal distribution, indicating that the logging data are better. The accuracy of TOC simulated by improved Δlog , SVR, and XGBoost models is affected by the number of sample sets. The improved Δlog model is most affected by the change of sample size, followed by the SVR model, and the XGBoost model is the most stable. In addition, compared with the prediction accuracy of TOC by improved Δlog , SVR, and XGBoost models, it is shown that the improved Δlog method has limitations in the prediction of TOC in oil shale. The SVR model is more suitable for the prediction of oil shale resources with small sample size, and the XGBoost model is applicable when the sample size is relatively large. According to the DYNOT analysis of logging INPEFA and TOC, the lake level changes frequently during the deposition of ultra-thick oil shale, and the lake level has experienced five stages of rising-stabilizing-frequent fluctuation-stabilizing- decreasing. The research results provide a theoretical basis for revealing the plane change of stable deep lake lakes and provide a basis for the study of lake levels in faulted lake basins in Paleogene Northeast Asia.
半深湖和深湖环境中湖泊水位的波动长期以来一直是古湖泊演化研究的核心课题。这一现象对有机质的富集和整个生态系统有着重大影响。深湖环境中湖泊水位变化的研究受到陆相地层记录稀缺的阻碍。为解决这一问题，我们对抚顺盆地始新世计军屯组进行了研究，特别聚焦于LFD - 1井。我们的研究对沉积于计军屯组半深湖至深湖环境中的极厚油页岩（约80米）进行了精细采样。通过多种方法预测总有机碳（TOC），并结合测井INPEFA和轨道调谐后的动态噪声（DYNOT）技术恢复湖泊水位研究。目标层的油页岩为Ⅰ型干酪根，有机质来源基本相同。自然伽马（GR）、电阻率（RT）、声波（AC）和密度（DEN）测井曲线呈正态分布，表明测井数据质量较好。改进的Δlog 、支持向量回归（SVR）和极端梯度提升（XGBoost）模型模拟TOC的精度受样本集数量影响。改进的Δlog 模型受样本量变化影响最大，其次是SVR模型，XGBoost模型最稳定。此外，对比改进的Δlog 、SVR和XGBoost模型对TOC的预测精度表明，改进的Δlog 方法在油页岩TOC预测方面存在局限性。SVR模型更适合小样本量油页岩资源的预测，XGBoost模型适用于样本量相对较大的情况。根据测井INPEFA和TOC的DYNOT分析，在超厚油页岩沉积期间湖泊水位频繁变化，湖泊水位经历了上升 - 稳定 - 频繁波动 - 稳定 - 下降五个阶段。研究结果为揭示稳定深湖湖泊的平面变化提供了理论依据，为研究东北亚古近纪断陷湖盆的湖泊水位提供了依据。
