Yu Tengfei, Wu Zhiping, Guo Ruichao, Zhang Guanlong, Zhang Yuejing, Shang Fengkai, Chen Lin
School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China.
Exploration and Development Research Institute, Shengli Oilfield Company, SINOPEC, Dongying 257000, China.
ACS Omega. 2023 Mar 7;8(11):10314-10334. doi: 10.1021/acsomega.2c07991. eCollection 2023 Mar 21.
Significant attention has been given to the extensive development of saline environments in petroliferous basins. Further exploration and studies have discovered that saline environments, such as those for the deposition of source rocks in the Paleogene Anjihaihe (E ) Formation of the Sikeshu Sag, are ubiquitous in terrestrial lake basins. Previous studies have suggested that the oil reservoirs in the Sikeshu Sag and its peripheral regions are predominantly derived from the black mudstone and coal measures of the Lower Jurassic Badaowan (J ) Formation. However, with deeper exploration of the study area, a growing number of reservoirs with geochemical characteristics different from the J oil source have been discovered, indicating that there are oil sources other than the J source rocks. In this study, various machine learning algorithms were used (random forest, RF; convolutional neural networks, CNN; extreme gradient boosting, XGBoost; ElasticNetCV; Bayesian Ridge; and particle swarm optimization-support vector regression) to select the most suitable algorithm for predicting and comparing the quality of potential source rocks. A violin plot and Taylor diagram were applied to visually compare the reliability and application effectiveness of the models. The results demonstrated that XGBoost and RF can become essential tools for predicting the quality of potential source rocks. Moreover, the measured and predicted values of total organic carbon (TOC), hydrocarbon potential (S + S), and hydrogen index indicate that there are three main source rocks: the E , Lower Jurassic Sangonghe (J ), and J formations. The thermal maturity of the E source rocks is still early mature because of the saline-brackish water nature of these rocks, although large-scale hydrocarbon generation and expulsion can be achieved in the early mature stage. Based on their geochemical characteristics and stepwise discriminant analysis, the oils in the Sikeshu Sag and its peripheral regions can be categorized into two types: groups A and B. Comprehensive organic geochemical evidence suggests that genetically, group A oils are originated from E less-mature saline lacustrine sedimentary rocks, while group B oils indicate similar affinity to the Jurassic source. Fluid inclusion microthermometry and one-dimensional basin modeling showed that the oil charging periods of group A and B oils were Middle-Late Miocene (13-8 Ma) and Late Oligocene (23-20 Ma), respectively. Quantitative grain fluorescence (QGF) analysis further propose that the hydrocarbon supply region of the E sources is mainly located east of the Western Chepaizi Uplift and the interior area of the Sikeshu Sag, which breaks through the previous understanding that the Jurassic coal-derived oil source is the only main contributor in this study area. The research results can be widely applied to assess the petroleum resources of source rocks in similar areas worldwide.
含油气盆地盐环境的广泛发育受到了广泛关注。进一步的勘探和研究发现,盐环境在陆相湖盆中普遍存在,比如准噶尔盆地四棵树凹陷古近系安集海河组(E)烃源岩的沉积环境。以往研究表明,四棵树凹陷及其周边地区的油藏主要来自下侏罗统八道湾组(J)的黑色泥岩和煤系地层。然而,随着研究区勘探深度的增加,发现了越来越多地球化学特征与J油源不同的油藏,这表明除了J烃源岩之外还存在其他油源。在本研究中,使用了多种机器学习算法(随机森林,RF;卷积神经网络,CNN;极端梯度提升,XGBoost;弹性网络交叉验证,ElasticNetCV;贝叶斯岭回归;以及粒子群优化支持向量回归)来选择最适合预测和比较潜在烃源岩质量的算法。应用小提琴图和泰勒图直观地比较模型的可靠性和应用效果。结果表明,XGBoost和RF可以成为预测潜在烃源岩质量的重要工具。此外,总有机碳(TOC)、烃潜力(S + S)和氢指数的实测值和预测值表明,存在三组主要烃源岩:E组、下侏罗统三工河组(J)和J组。由于E烃源岩的咸淡水性质,其热成熟度仍处于早成熟阶段,尽管在早成熟阶段可以实现大规模的生烃和排烃。根据其地球化学特征和逐步判别分析,四棵树凹陷及其周边地区的油可分为两类:A组和B组。综合有机地球化学证据表明,从成因上看,A组油源自E组欠成熟的咸水湖相沉积岩,而B组油与侏罗系油源具有相似的亲缘关系。流体包裹体显微测温及一维盆地模拟表明,A组和B组油的充注期分别为中新世中晚期(13 - 8 Ma)和渐新世晚期(23 - 20 Ma)。定量颗粒荧光(QGF)分析进一步表明,E组烃源岩的供油区域主要位于车排子西凸起以东和四棵树凹陷内部区域,这突破了以往认为侏罗系煤成油源是该研究区唯一主要贡献者的认识。该研究成果可广泛应用于全球类似地区烃源岩石油资源的评估。