基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

邵俊哲; 李阳; 吴楠; 冯文杰; 魏薇

doi:10.14027/j.issn.1000-0550.2024.056

ISSN 1000-0550 CN 62-1038/P

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基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

邵俊哲, 李阳, 吴楠, 冯文杰, 魏薇

文章导航 > 沉积学报 > 2024 > 优先发表

邵俊哲, 李阳, 吴楠, 冯文杰, 魏薇. 基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】[J]. 沉积学报. doi: 10.14027/j.issn.1000-0550.2024.056

引用本文:

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

引用本文:

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

基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

1.
长江大学地球科学学院
2.
长江大学资源与环境学院

基金项目: 其它-海洋分散有机质沉积聚集模拟实验(CCL2021RCPS0231RSN)

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文章访问数: 23
HTML全文浏览量: 3
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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

摘要

摘要: 陆源海相烃源岩是我国多个近海盆地的主力烃源岩，其差异性展布特征制约着烃源岩的分布预测精度和油气勘探成效。而陆源有机质的搬运沉积过程决定着陆源海相烃源岩质量和分布。运用水槽沉积模拟结合三维激光扫描技术，从“正演”的角度动态记录并定量表征不同水体盐度条件下陆源分散有机质的搬运过程并运用机器学习算法建立TOC预测模型。结果表明，海陆过渡环境中的陆源有机质主要富集在三角洲前缘和前三角洲沉积相带内，随着搬运距离的增大，海陆过渡环境中的陆源有机质丰度呈现先增大后减小的趋势。在盐絮凝作用影响下，咸水环境中陆源有机质搬运距离更靠近物源区，沉积厚度更大。基于三种深度学习算法建立了实验条件下的TOC预测模型，最终优选出基于随机森林算法的预测模型为最优模型。将实验条件下的TOC预测模型与地质条件相结合，完成了崖南凹陷崖城组三角洲内烃源岩的TOC预测。结果显示，崖南凹陷陆源有机质搬运距离可达50 km，在距离物源区31 km左右处有机质富集程度最高。
- 海陆过渡相 /
- 陆源有机质 /
- 机器学习 /
- 沉积模拟 /
- 预测模型
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.
- Marine-terrestrial transitional environment /
- Terrestrial organic matter /
- Machine learing /
- Sedimentary simulation /
- Prediction model

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基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

1. 长江大学地球科学学院

2. 长江大学资源与环境学院

基金项目: 其它-海洋分散有机质沉积聚集模拟实验(CCL2021RCPS0231RSN)

收稿日期: 2024-02-27
网络出版日期: 2024-06-21

关键词:

摘要: 陆源海相烃源岩是我国多个近海盆地的主力烃源岩，其差异性展布特征制约着烃源岩的分布预测精度和油气勘探成效。而陆源有机质的搬运沉积过程决定着陆源海相烃源岩质量和分布。运用水槽沉积模拟结合三维激光扫描技术，从“正演”的角度动态记录并定量表征不同水体盐度条件下陆源分散有机质的搬运过程并运用机器学习算法建立TOC预测模型。结果表明，海陆过渡环境中的陆源有机质主要富集在三角洲前缘和前三角洲沉积相带内，随着搬运距离的增大，海陆过渡环境中的陆源有机质丰度呈现先增大后减小的趋势。在盐絮凝作用影响下，咸水环境中陆源有机质搬运距离更靠近物源区，沉积厚度更大。基于三种深度学习算法建立了实验条件下的TOC预测模型，最终优选出基于随机森林算法的预测模型为最优模型。将实验条件下的TOC预测模型与地质条件相结合，完成了崖南凹陷崖城组三角洲内烃源岩的TOC预测。结果显示，崖南凹陷陆源有机质搬运距离可达50 km，在距离物源区31 km左右处有机质富集程度最高。

English Abstract

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

Received Date: 2024-02-27
Available Online: 2024-06-21

Keywords:

Marine-terrestrial transitional environment /
Terrestrial organic matter /
Machine learing /
Sedimentary simulation /
Prediction model

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.

引用本文:

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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基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

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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

计量

目录

基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

1. 长江大学地球科学学院

2. 长江大学资源与环境学院

English Abstract

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

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基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

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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

计量

出版历程

目录

基于机器学习的海陆过渡环境中陆源有机质分布预测模型——从沉积模拟实验到地质应用【水槽沉积模拟实验专辑】

doi: 10.14027/j.issn.1000-0550.2024.056

1. 长江大学地球科学学院 2. 长江大学资源与环境学院

English Abstract

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

全文HTML

目录

友情链接

期刊在线

联系我们

1. 长江大学地球科学学院

2. 长江大学资源与环境学院