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Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications[J]. Acta Sedimentologica Sinica. doi: 10.14027/j.issn.1000-0550.2024.056
Citation: Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications[J]. Acta Sedimentologica Sinica. doi: 10.14027/j.issn.1000-0550.2024.056

Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications

doi: 10.14027/j.issn.1000-0550.2024.056
  • Received Date: 2024-02-27
    Available Online: 2024-06-21
  • Marine-terrestrial transitional source rocks are the main source rocks in several offshore basins in China, and their differential distribution characteristics restrict the prediction accuracy of source rocks and the effectiveness of oil and gas exploration. And the transport and sedimentation process of terrestrial organic matter determines the quality and distribution of source rock in marine-terrestrial transitional environment. Using a combination of flume sedimentary simulation and 3D laser scanning technology, the dynamic recording and quantitative characterization of the transport process of terrestrial dispersed organic matter under different water salinity conditions are carried out from the perspective of forward modeling. Machine learning algorithms are used to establish a TOC prediction model. The results show that terrestrial organic matter in the marine-terrestrial transitional environment is mainly enriched in the delta front and prodelta. As the transportation distance increases, the abundance of terrestrial organic matter shows a trend of first increasing and then decreasing. Under the influence of salt flocculation, the transportation distance of terrestrial organic matter in saltwater environment is closer to the source area, and the sediment thickness is larger. A TOC prediction model was established under experimental conditions based on three deep learning algorithms, and ultimately selects the prediction model based on random forest algorithm with outlier removal and experience based sedimentary facies assignment as input features as the optimal model. The TOC prediction model under experimental conditions is combined with geological conditions to complete the TOC prediction of source rocks in the Yacheng Formation of the Yanan depression. The results show that the transportation distance of terrestrial organic matter in the Yanan Depression can reach 50 km, and the highest degree of organic matter enrichment occurs at a distance of about 31 km from the source area.
  • 实验条件.xlsx
  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040102030405060
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 14.1 %FULLTEXT: 14.1 %META: 74.0 %META: 74.0 %PDF: 11.9 %PDF: 11.9 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 16.7 %其他: 16.7 %上海: 1.3 %上海: 1.3 %兰州: 0.4 %兰州: 0.4 %北京: 22.9 %北京: 22.9 %十堰: 0.4 %十堰: 0.4 %南京: 1.8 %南京: 1.8 %台州: 1.3 %台州: 1.3 %哥伦布: 0.4 %哥伦布: 0.4 %城南: 2.2 %城南: 2.2 %大庆: 1.8 %大庆: 1.8 %大连: 2.6 %大连: 2.6 %天津: 0.4 %天津: 0.4 %宣城: 0.4 %宣城: 0.4 %巴音郭楞: 0.9 %巴音郭楞: 0.9 %常州: 0.4 %常州: 0.4 %广州: 0.9 %广州: 0.9 %张家口: 0.9 %张家口: 0.9 %悉尼: 0.4 %悉尼: 0.4 %成都: 1.3 %成都: 1.3 %扬州: 0.9 %扬州: 0.9 %晋城: 0.4 %晋城: 0.4 %武汉: 5.7 %武汉: 5.7 %漯河: 0.9 %漯河: 0.9 %盘锦: 0.4 %盘锦: 0.4 %芒廷维尤: 11.5 %芒廷维尤: 11.5 %芝加哥: 3.1 %芝加哥: 3.1 %苏州: 0.4 %苏州: 0.4 %荆州: 0.4 %荆州: 0.4 %衡水: 0.4 %衡水: 0.4 %西宁: 10.1 %西宁: 10.1 %西安: 0.9 %西安: 0.9 %西雅图: 1.3 %西雅图: 1.3 %资阳: 0.4 %资阳: 0.4 %郑州: 0.4 %郑州: 0.4 %鄂州: 0.4 %鄂州: 0.4 %长沙: 0.4 %长沙: 0.4 %青岛: 2.2 %青岛: 2.2 %香港: 0.9 %香港: 0.9 %齐齐哈尔: 0.9 %齐齐哈尔: 0.9 %其他上海兰州北京十堰南京台州哥伦布城南大庆大连天津宣城巴音郭楞常州广州张家口悉尼成都扬州晋城武汉漯河盘锦芒廷维尤芝加哥苏州荆州衡水西宁西安西雅图资阳郑州鄂州长沙青岛香港齐齐哈尔
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  • Received:  2024-02-27

Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications

doi: 10.14027/j.issn.1000-0550.2024.056

Abstract: Marine-terrestrial transitional source rocks are the main source rocks in several offshore basins in China, and their differential distribution characteristics restrict the prediction accuracy of source rocks and the effectiveness of oil and gas exploration. And the transport and sedimentation process of terrestrial organic matter determines the quality and distribution of source rock in marine-terrestrial transitional environment. Using a combination of flume sedimentary simulation and 3D laser scanning technology, the dynamic recording and quantitative characterization of the transport process of terrestrial dispersed organic matter under different water salinity conditions are carried out from the perspective of forward modeling. Machine learning algorithms are used to establish a TOC prediction model. The results show that terrestrial organic matter in the marine-terrestrial transitional environment is mainly enriched in the delta front and prodelta. As the transportation distance increases, the abundance of terrestrial organic matter shows a trend of first increasing and then decreasing. Under the influence of salt flocculation, the transportation distance of terrestrial organic matter in saltwater environment is closer to the source area, and the sediment thickness is larger. A TOC prediction model was established under experimental conditions based on three deep learning algorithms, and ultimately selects the prediction model based on random forest algorithm with outlier removal and experience based sedimentary facies assignment as input features as the optimal model. The TOC prediction model under experimental conditions is combined with geological conditions to complete the TOC prediction of source rocks in the Yacheng Formation of the Yanan depression. The results show that the transportation distance of terrestrial organic matter in the Yanan Depression can reach 50 km, and the highest degree of organic matter enrichment occurs at a distance of about 31 km from the source area.

Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications[J]. Acta Sedimentologica Sinica. doi: 10.14027/j.issn.1000-0550.2024.056
Citation: Machine Learning Based Model for Predicting the Distribution of Terrestrial Organic Matter in Marine-terrestrial Transitional Environmen—From Sedimentary Simulation Experiments to Geological Applications[J]. Acta Sedimentologica Sinica. doi: 10.14027/j.issn.1000-0550.2024.056
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