| 利用GS-LightGBM机器学习模型识别致密砂岩地层岩性 |
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| 引用本文： | 谷宇峰,张道勇,鲍志东,郭海晓,周立明,任继红.利用GS-LightGBM机器学习模型识别致密砂岩地层岩性[J].地质科技通报,2021,40(4):224-234. |
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| 作者姓名： | 谷宇峰 张道勇 鲍志东 郭海晓 周立明 任继红 |
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| 摘 要： | 以交会图为代表的传统岩性识别图版无法适用于致密砂岩地层，其主要原因是大部分地层岩性的测井响应特征相似度高，难以基于图版分析被有效识别。LightGBM较传统模式识别模型能更高效地解决问题，为此采用该模型识别致密砂岩地层岩性。由于LightGBM在建模时利用了较多的超参数，导致预测结果难以保证为最优，所以采用GS算法进行优化，进而提出GS-LightGBM。实验目的层为姬塬油田西部长4+5段致密砂岩地层。提出模型的预测能力通过设计两个实验来验证。为突出验证效果，实验中加入SVM和XGBoost作为对比模型。实验结果显示，GS-XGBoost和GS-LightGBM的准确率、F1-score和AUC指标相接近，都最高，但GS-LightGBM的计算时间只有GS-XGBoost的约1/23。实验结果表明，GS-LightGBM模型可在不失精度的情况下，能快速给出预测结果，具备了在致密砂岩地层岩性识别研究上的应用价值和推广性。
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| 关 键 词： | 致密砂岩地层 岩性识别 SVM模型 XGBoost模型 LightGBM模型 GS优化算法 |
| 收稿时间： | 2020-11-30 |
Lithology prediction of tight sandstone formation using GS-LightGBM hybrid machine learning model |
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| Abstract: | Classic lithology predictors, represented by crossplot, are generally ineffective for tight sandstone formation, mainly due to a point that most lithologies present extremely similar logging responses and thus are rather difficult to be analyzed effectively via crossplot.Compared to classic pattern recognizers, LightGBM shows higher efficiency in data process, therefore it is employed to make a solution for lithology prediction of tight sandstone formation.As LightGBM utilizes many hyper-parameters during modeling, easily causing an issue that the predicted results are not reliable enough, GS algorithm is adopted to solve optimization and further a hybrid machine learning model GS-LightGBM is proposed.The tight sandstone formation of member of Chang 4+5 in western Jiyuan Oilfield is validation targets, and two experiments are designed to reveal prediction capability of the proposed model.In order to highlight validation effect, SVM and XGBoost are introduced as comparative predictors.Experimental results manifest GS-XGBoost and GS-LightGBM have the similar and also the highest marks in the prediction performance of accuracy, F1-score, and AUC, while computing time of GS-LightGBM is only 1/23 shorter than that of GS-XGBoost.The results demonstrate the proposed model is capable to rapidly figure out the predicted lithologies based on guarantee of high prediction accuracy, proving its better applicable prospect and generalization in the study field of lithology prediction of tight sandstone formation. |
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