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Hydrocarbon Generation from Low-Mature Saline Lacustrine Sediments Studied Using Machine Learning and Chemometric Methods: The Succession of the Sikeshu Sag, Junggar Basin, NW China
[Image: see text] 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(2-3)a) Formatio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10035012/ https://www.ncbi.nlm.nih.gov/pubmed/36969413 http://dx.doi.org/10.1021/acsomega.2c07991 |
Sumario: | [Image: see text] 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(2-3)a) 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(1)b) Formation. However, with deeper exploration of the study area, a growing number of reservoirs with geochemical characteristics different from the J(1)b oil source have been discovered, indicating that there are oil sources other than the J(1)b 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(1) + S(2)), and hydrogen index indicate that there are three main source rocks: the E(2-3)a, Lower Jurassic Sangonghe (J(1)s), and J(1)b formations. The thermal maturity of the E(2-3)a 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(2-3)a 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(2-3)a 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. |
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