石油科学通报2023，Vol.8Issue(6)：767-774,8.DOI:10.3969/j.issn.2096-1693.2023.06.070
基于钻录测数据驱动的储层可压性无监督聚类模型及其压裂布缝优化
An unsupervised cluster model of formation fracability based on drill-log data and its application to fracture optimization
摘要
Abstract
Reservoir fracability evaluation is one of the prerequisites to improve the effect of balanced fracturing of uncon-ventional oil and gas fields.At present,reservoir fracability evaluation mainly depends on logging data theory to explain rock mechanics parameters,and the application effect on fracturing is uneven.In this paper,the characteristics of rock mechanical parameters are directly reflected by the bit rock breaking data and the reservoir fracability is clustered by drilling and logging data.We established a reservoir fracability clustering model based on a self-organizing map(SOM)unsupervised clustering algorithm.The elbow method is used to determine the optimal clustering number,and the parameter optimization method of fracture placement is formed.The optimal design of three-cluster perforation placement is carried out for typical vertical wells in the Tarim Basin with large thickness reservoirs.The results show that the drilling time,dc-exponent,weight on bit,torque,true formation resistivity,acoustic and neutron data are significantly correlated with reservoir fracability and can be used as character-istic parameters.The established model can effectively distinguish the difference of reservoir fracability along the wellbore axis,and select the fractures in the fracturable well section of the same type of reservoir,which is expected to improve the effect of balanced fracturing.关键词
机器学习/无监督学习/水力压裂/优化设计Key words
machine learning/unsupervised learning/hydraulic fracturing/optimization design引用本文复制引用
胡诗梦,盛茂,秦世勇,任登峰,彭芬,冯觉勇..基于钻录测数据驱动的储层可压性无监督聚类模型及其压裂布缝优化[J].石油科学通报,2023,8(6):767-774,8.基金项目
中国石油大学(北京)优秀青年学者科研基金项目(2462020QNXZ001)资助 （北京）
