Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method
The coal seams of the Permian Taiyuan-Shanxi formations in the Baode block of the northeastern margin of the Ordos Basin have abundant coalbed methane resources. However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in rese...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Petroleum Geology and Experiment
2025-01-01
|
| Series: | Shiyou shiyan dizhi |
| Subjects: | |
| Online Access: | https://www.sysydz.net/cn/article/doi/10.11781/sysydz2025010204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823864077115457536 |
|---|---|
| author | Qingfeng ZHANG Ziling LI Jikun ZHANG Shuai HAO Xiaoguang SUN Yanjie SHANG Yun ZUO |
| author_facet | Qingfeng ZHANG Ziling LI Jikun ZHANG Shuai HAO Xiaoguang SUN Yanjie SHANG Yun ZUO |
| author_sort | Qingfeng ZHANG |
| collection | DOAJ |
| description | The coal seams of the Permian Taiyuan-Shanxi formations in the Baode block of the northeastern margin of the Ordos Basin have abundant coalbed methane resources. However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in reservoir brittleness. Rock mechanical parameter method is commonly used to evaluate reservoir brittleness. Studying rock mechanical parameters and brittleness can provide an important basis for fracturing modification. However, current methods mostly rely on empirical formulas, leading to limited evaluation accuracy. In this study, a convolutional neural network (CNN) was utilized to construct a conversion model between experimentally obtained elastic modulus, Poisson's ratio, and multi-logging curves. Based on this method, rock mechanical profiles were further established, enabling quantitative evaluation of brittleness. The results indicated that CNN-based predictions of rock mechanical parameters had good applicability for coal-bearing layers. The brittleness indices the main coal seams, 4+5# and 8+9#, in the Baode block were generally low. The brittleness index of the 4+5# seam was slightly higher than that of the 8+9# seam. Both seams exhibited similar spatial distributions, with low brittleness values in the central and southeastern parts of the study area. Differences in mineral composition affected rock brittleness. Higher quartz content was linearly correlated with greater elastic modulus and brittleness index. |
| format | Article |
| id | doaj-art-543db02367394b51bcc977b16d39b0ae |
| institution | Kabale University |
| issn | 1001-6112 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Petroleum Geology and Experiment |
| record_format | Article |
| series | Shiyou shiyan dizhi |
| spelling | doaj-art-543db02367394b51bcc977b16d39b0ae2025-02-09T07:48:48ZzhoEditorial Office of Petroleum Geology and ExperimentShiyou shiyan dizhi1001-61122025-01-0147120421210.11781/sysydz2025010204sysydz-47-1-204Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network methodQingfeng ZHANG0Ziling LI1Jikun ZHANG2Shuai HAO3Xiaoguang SUN4Yanjie SHANG5Yun ZUO6PetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaPetroChina Coalbed Methane Co., Ltd., Beijing 100028, ChinaThe coal seams of the Permian Taiyuan-Shanxi formations in the Baode block of the northeastern margin of the Ordos Basin have abundant coalbed methane resources. However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in reservoir brittleness. Rock mechanical parameter method is commonly used to evaluate reservoir brittleness. Studying rock mechanical parameters and brittleness can provide an important basis for fracturing modification. However, current methods mostly rely on empirical formulas, leading to limited evaluation accuracy. In this study, a convolutional neural network (CNN) was utilized to construct a conversion model between experimentally obtained elastic modulus, Poisson's ratio, and multi-logging curves. Based on this method, rock mechanical profiles were further established, enabling quantitative evaluation of brittleness. The results indicated that CNN-based predictions of rock mechanical parameters had good applicability for coal-bearing layers. The brittleness indices the main coal seams, 4+5# and 8+9#, in the Baode block were generally low. The brittleness index of the 4+5# seam was slightly higher than that of the 8+9# seam. Both seams exhibited similar spatial distributions, with low brittleness values in the central and southeastern parts of the study area. Differences in mineral composition affected rock brittleness. Higher quartz content was linearly correlated with greater elastic modulus and brittleness index.https://www.sysydz.net/cn/article/doi/10.11781/sysydz2025010204convolutional neural networkcoal seam brittlenesstaiyuan-shanxi formationspermianbaode blockordos basin |
| spellingShingle | Qingfeng ZHANG Ziling LI Jikun ZHANG Shuai HAO Xiaoguang SUN Yanjie SHANG Yun ZUO Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method Shiyou shiyan dizhi convolutional neural network coal seam brittleness taiyuan-shanxi formations permian baode block ordos basin |
| title | Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method |
| title_full | Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method |
| title_fullStr | Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method |
| title_full_unstemmed | Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method |
| title_short | Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method |
| title_sort | brittleness evaluation of main coal seams in permian taiyuan shanxi formations baode block ordos basin based on a convolutional neural network method |
| topic | convolutional neural network coal seam brittleness taiyuan-shanxi formations permian baode block ordos basin |
| url | https://www.sysydz.net/cn/article/doi/10.11781/sysydz2025010204 |
| work_keys_str_mv | AT qingfengzhang brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT zilingli brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT jikunzhang brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT shuaihao brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT xiaoguangsun brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT yanjieshang brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod AT yunzuo brittlenessevaluationofmaincoalseamsinpermiantaiyuanshanxiformationsbaodeblockordosbasinbasedonaconvolutionalneuralnetworkmethod |