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基于新型集成学习算法的基岩潜山油藏储层裂缝开度预测算法
孙致学,姜宝胜,肖康,李吉康
0
(1.中国石油大学(华东)石油工程学院,山东青岛266580;2.非常规油气开发教育部重点实验室中国石油大学(华东),山东青岛266580;3.中国石油勘探开发研究院非洲研究所,北京100083)
摘要:
天然裂缝是基岩潜山油藏油气存储及运输的重要场所,而裂缝开度是表征潜山油藏储层品质、油气储量及产能评价的关键参数。为此,提出一种基于集成学习算法的新型裂缝开度预测算法。以B盆地中非乍得某基岩潜山油藏岩心描述、关键井成像测井、裂缝参数解释获取开度数据,以相同深度测井数据作为特征变量构成学习样本。利用K 均值聚类算法对学习样本进行降噪,剔除异常数据,以支持向量机回归和XGBoost回归算法为基础模型,再利用随机搜索进行参数优化,通过岭回归算法对基础模型进行集成组合,再进行裂缝开度预测。结果表明所提出的新型集成学习算法比基础模型性能有明显提升。测试集样本预测值与实际值均方根误差为0.047,相关系数达0.931。该算法弥补了单一回归算法不稳定的特点,提高了泛化能力,为裂缝开度预测提供了新思路。
关键词:  裂缝开度  K 均值聚类算法  支持向量回归  XGBoost回归  集成学习
DOI:10.13673/j.cnki.cn37-1359/te.2020.03.004
基金项目:国家自然科学基金项目“基于离散-连续介质模型的水-EGS传质传热机理及数值模拟研究”(51774317)和“乍得潜山产能评价及开发技术政策研究”(2019D-3210)。
Prediction of fracture aperture in bedrock buried hill oil reservoir based on novel ensemble learning algorithm
SUN Zhixue,JIANG Baosheng,XIAO Kang,LI Jikang
(1.School of Petroleum Engineering,China University of Petroleum(East China),Qingdao City,Shandong Province,266580,China;2.Key Laboratory of Unconventional Oil & Gas Development,Ministry of Education,China University of Petroleum (East China),Qingdao City,Shandong Province,266580,China;3.African Research Institute,PetroChina Research Institute of Petroleum Exploration & Development,Beijing City,100083,China)
Abstract:
The natural fractures are important for oil and gas storage and transportation in the bedrock buried hill reservoirs.The fracture aperture is the key parameter for the reservoir quality characterization as well as reserves and productivity evaluation of buried hill reservoirs. In this study,a new fracture aperture prediction algorithm is proposed based on the ensemble learning algorithm. The samples are collected from the bedrock buried hill reservoirs in Basin B of Chad,Central Africa,and their fracture aperture data are extracted from the sample description,key well imaging logging,and fracture parameter interpretation. The same depth logging data are used as the feature variables to constitute the learning sample,and the K-means clustering algorithm is applied to reduce noise of the learning sample and eliminate abnormal data. Based on Support Vector Machine(SVM)regression and XGBoost regression algorithm,and by using random search to optimize model parameters,the fracture apertures are estimated according to the basic models combined by the ridge regression. The results show that the performance of the novel ensemble learning algorithm is better than that of the basic model,the root mean square error between the predicted and actual values of the test set is 0.047,and the correlation coefficient is 0.931.The algorithm improves the instability of the single regression algorithm,improves the generalization ability,and provides a new way for aperture prediction.
Key words:  fracture aperture  K-means clustering  SVM regression  XGBoost regression  ensemble learning

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