Logging interpretation method based on Bayesian Optimization XGBoost
2022 16th IEEE International Conference on Signal Processing (ICSP)(2022)
摘要
With the increasing complexity of geological conditions and labor cost of oil exploitation, processing the obtained geological and logging interpretation information is vital before formal exploitation. The existing logging interpretation technology relies heavily on manual and expert experience, as well as traditional interpretation algorithm has inherent defects of easy over fitting and low accuracy, which is difficult to realize logging interpretation under complex geological conditions. To address these concerns, this paper presents a XGBoost model based on Bayesian optimization for logging interpretation, which used machine learning method to reduce manual dependence and can be used in complex geological conditions. The input features of the proposed model are selected through the correlation ranking of logging interpretation features, and the smaller correlation features are eliminated. The Bayesian optimization algorithm is introduced to optimize the model to the best state. To evaluate the performance of the proposed model, a series of experiments on logging data of sandy conglomerate reservoir are conducted. The results are compared with other machine learning algorithms and indicate that Bayesian optimization XGBoost (BO-XGBoost) is feasible and powerful in the field of logging interpretation.
更多查看译文
关键词
logging interpretation,XGBoost,bayesian optimization,machine learning,hyperparameter
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要