Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield
Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the e...
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000152 |
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| author | Xiaoshuai Wu Yuanliang Zhao Jianpeng Zhao Shichen Shuai Bing Yu Junqing Rong Hui Chen |
| author_facet | Xiaoshuai Wu Yuanliang Zhao Jianpeng Zhao Shichen Shuai Bing Yu Junqing Rong Hui Chen |
| author_sort | Xiaoshuai Wu |
| collection | DOAJ |
| description | Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the existence of overlaps in the cross plot for different categories of interlayers, it is difficult to establish a determined model to classify the type of interlayer; (2) Traditional identification methods only use two or three logging curves to identify the types of interlayers, making it difficult to fully utilize the information of the logging curves, the recognition accuracy will be greatly reduced; (3) For a large number of complex logging data, interlayer identification is time-consuming and labor-intensive. Based on the existing well area data such as logging data and core data, this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CⅢ sandstone group in the M oilfield. Through the comparison of various classifiers, it is found that the decision tree method has the best applicability and the highest accuracy in the study area. Based on single well identification of interlayers, the continuity of well interval interlayers in the study area is analyzed according to the horizontal well. Finally, the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield. |
| format | Article |
| id | doaj-art-818d2c5aebe84345ac89569eeae3ca6b |
| institution | Kabale University |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-818d2c5aebe84345ac89569eeae3ca6b2025-08-20T03:27:51ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110011910.1016/j.aiig.2025.100119Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfieldXiaoshuai Wu0Yuanliang Zhao1Jianpeng Zhao2Shichen Shuai3Bing Yu4Junqing Rong5Hui Chen6School of Earth Sciences and Engineering, Xi 'an Shiyou University, Xi 'an, 710065, China; Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi 'an Shiyou University, Xi 'an, 710065, ChinaExploration Department, Petrochina Tarim Oilfield Company, Korla, 841000, ChinaSchool of Earth Sciences and Engineering, Xi 'an Shiyou University, Xi 'an, 710065, China; Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi 'an Shiyou University, Xi 'an, 710065, China; Corresponding author. 18 Electronic Road, Yanta, Xi 'an, 710065, China.Exploration Department, Petrochina Tarim Oilfield Company, Korla, 841000, ChinaGeological Research Institute, China Petroleum Logging Co Ltd, Xi 'an, 710077, ChinaGeological Research Institute, China Petroleum Logging Co Ltd, Xi 'an, 710077, ChinaGeological Research Institute, China Petroleum Logging Co Ltd, Xi 'an, 710077, ChinaInterlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the existence of overlaps in the cross plot for different categories of interlayers, it is difficult to establish a determined model to classify the type of interlayer; (2) Traditional identification methods only use two or three logging curves to identify the types of interlayers, making it difficult to fully utilize the information of the logging curves, the recognition accuracy will be greatly reduced; (3) For a large number of complex logging data, interlayer identification is time-consuming and labor-intensive. Based on the existing well area data such as logging data and core data, this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CⅢ sandstone group in the M oilfield. Through the comparison of various classifiers, it is found that the decision tree method has the best applicability and the highest accuracy in the study area. Based on single well identification of interlayers, the continuity of well interval interlayers in the study area is analyzed according to the horizontal well. Finally, the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.http://www.sciencedirect.com/science/article/pii/S2666544125000152InterlayerMachine learningRemaining oil distributionProduction development |
| spellingShingle | Xiaoshuai Wu Yuanliang Zhao Jianpeng Zhao Shichen Shuai Bing Yu Junqing Rong Hui Chen Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield Artificial Intelligence in Geosciences Interlayer Machine learning Remaining oil distribution Production development |
| title | Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield |
| title_full | Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield |
| title_fullStr | Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield |
| title_full_unstemmed | Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield |
| title_short | Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield |
| title_sort | identification of interlayer and connectivity analysis based on machine learning and production data a case study from m oilfield |
| topic | Interlayer Machine learning Remaining oil distribution Production development |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000152 |
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