TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China
文献类型：期刊论文
| 作者 | Yin, Jintao2,3; Gao, Chao2,3; Cheng, Ming1; Liang, Quansheng2,3; Xue, Pei2,3; Hao, Shiyan2,3; Zhao, Qianping2,3 |
| 刊名 | FRONTIERS IN EARTH SCIENCE
 |
| 出版日期 | 2023-01-20 |
| 卷号 | 10页码:18 |
| 关键词 | ordos basin Yan'an area lacustrine oil shale lithofacies classification regression TOC interpretation model |
| DOI | 10.3389/feart.2022.1106799 |
| 英文摘要 | In this paper, taking the shale of Chang 7-Chang 9 oil formation in Yanchang Formation in the southeastern Ordos Basin as an example, through the study of shale heterogeneity characteristics, starting from the preprocessing of supervision data set, a logging interpretation method of total organic carbon content (TOC) on the lithofacies-based Categorical regression model (LBCRM) is proposed. It is show that: 1) Based on core observation, and Differences of sedimentation and structure, five lithofacies developed in the Yanchang Formation: shale shale facies, siltstone/ultrafine sandstone facies, tuff facies, argillaceous shale facies with silty lamina and argillaceous shale facies with tuff lamina. 2) The strong heterogeneity of shale makes it difficult to accurately explain the TOC distribution of shale intervals in the application of model-based interpretation methods. The LBCRM interpretation method based on the understanding of shale heterogeneity can effectively reduce the influence of formation factors other than TOC on the prediction accuracy by studying the characteristics of shale heterogeneity and constructing a TOC interpretation model for each lithofacies category. At the same time, the degree of unbalanced distribution of data is reduced, so that the data mining algorithm achieves better prediction effect. 3) The interpretability of lithofacies logging ensures the wellsite application based on the classification and regression model of lithofacies. Compared with the traditional homogeneous regression model, the prediction performance has been greatly improved, TOC segment prediction is more accurate. 4) The LBCRM method based on shale heterogeneity can better understand the reasons for the deviation of the traditional model-based interpretation method. After being combined with the latter, it can make logging data provide more useful information. |
| WOS关键词 | APPALACHIAN DEVONIAN SHALES ; ORGANIC-MATTER ; NEURAL-NETWORK ; PREDICTION ; EXAMPLE ; LOGS |
| 资助项目 | Major National Science and Technology Projects[2017ZX05039001-005] ; key R&D plan of Shaanxi Province[41902136] ; key R&D plan of Shaanxi Province[ycsy2021jcts-B-06] ; National Natural Science Foundation of China[ycsy2022jcts-B-28] ; Research Project of Yanchang Oil Field Co., Ltd. ; [2022GY-138,2021GY-113] ; [S2022-YF-YBGY-0471] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000925746400001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; Major National Science and Technology Projects ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; key R&D plan of Shaanxi Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. ; Research Project of Yanchang Oil Field Co., Ltd. |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/106805]  |
| 专题 | 中国科学院地质与地球物理研究所 |
| 通讯作者 | Cheng, Ming |
| 作者单位 | 1.Chinese Acad Sci, Inst Geol & Geophys, Beijing, Peoples R China 2.Shaanxi Yanchang Petr Grp Corp Ltd, Xian, Peoples R China 3.Shaanxi Key Lab Lacustrine Shale Gas Accumulat & E, Xian, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Jintao,Gao, Chao,Cheng, Ming,et al. TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China[J]. FRONTIERS IN EARTH SCIENCE,2023,10:18. |
| APA | Yin, Jintao.,Gao, Chao.,Cheng, Ming.,Liang, Quansheng.,Xue, Pei.,...&Zhao, Qianping.(2023).TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China.FRONTIERS IN EARTH SCIENCE,10,18. |
| MLA | Yin, Jintao,et al."TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China".FRONTIERS IN EARTH SCIENCE 10(2023):18. |
入库方式： OAI收割
来源：地质与地球物理研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。