陆相页岩气资源评价中人工智能算法的探索——以苏北盆地溱潼凹陷为例
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谈迎, 杨伟松, 李振生
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The tentative application of artificial intelligence algorithm to evaluating continental shale gas resources: A case study of Qintong sag in Subei (North Jiangsu) Basin
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Ying TAN, Wei-Song YANG, Zhen-Sheng LI
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| 表2 溱潼凹陷成岩相评分结果 |
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| 成岩相(权值) | 样品区号 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | | 压实相(0.25) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 16 | 0 | | 胶结相(0.25) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 8 | 1 | 0 | 0 | 12 | 0 | | 溶蚀相(0.75) | 0 | 0 | 0 | 57 | 70 | 0 | 66 | 72 | 0 | 13 | 33 | 10 | 0 | 0 | | 填相(0.25) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 72 | 22 | 0 | 0 | | 有效面积/km2 | 0 | 0 | 0 | 57 | 70.0 | 0 | 66 | 90 | 26 | 14 | 105 | 32 | 38 | 0 | | 总评分 | 0 | 0 | 0 | 0.75 | 0.75 | 0 | 0.75 | 0.65 | 0.25 | 0.72 | 0.41 | 0.42 | 0.25 | 0 |
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