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《Petroleum Geology and Recovery Efficiency》 2020-03
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Prediction of fracture aperture in bedrock buried hill oil reservoir based on novel ensemble learning algorithm

SUN Zhixue;JIANG Baosheng;XIAO Kang;LI Jikang;School of Petroleum Engineering,China University of Petroleum(East China);Key Laboratory of Unconventional Oil & Gas Development,Ministry of Education,China University of Petroleum(East China);African Research Institute,PetroChina Research Institute of Petroleum Exploration & Development;  
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.
【Fund】: 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)
【CateGory Index】: P618.13
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