摘要
地震属性技术是砂体厚度预测的重要手段,由于目前可从地震数据中提取的地震属性种类较多,在利用地震属性技术前,必须优化出对砂体厚度最敏感的地震属性组合,以减少地震属性信息的重复与冗余。为此提出了一种联合关联规则与随机森林回归算法的地震多属性砂体厚度预测方法。随机森林回归算法能够建立地震多属性与砂体厚度之间的非线性关系,并能进行属性选择,但是该算法无法识别地震多种属性中的冗余特征。关联规则能够发现地震属性之间的非线性关联,并能借助卡方检验消除地震属性间的冗余性。分别采用了随机森林回归算法(RFR)、联合关联规则与随机森林回归(AR-RFR)及BP神经网络回归的算法(AR-BP)对滩坝砂岩合成模型和某实际工区进行了砂体厚度预测。对比结果表明,基于关联规则的属性优选得到的属性间相关性低,关联规则与随机森林算法的结合提高了砂体厚度的预测精度。数值实验证明了该方法的有效性。
Seismic attributes analysis technique is an important tool for sand thickness prediction.Due to the varieties of seismic attributes,the best seismic attributes need to be optimized before the seismic attributes analysis technique is applied to reduce the repeatability and redundancy of the attributes.Therefore,we present an improved random forest regression algorithm combined with associate rules for sand thickness prediction(AR-RFR).Although random forest regression algorithm(RFR)is powerful for the problem characterized for nonlinearity and high dimension in reservoir prediction,it cannot solve attribute reduction problems.The associate rules can find the non-linear relationship among the multi-attributes and can reduce some redundant attributes by means of Chi-squared Test.We apply ordinary RFR,AR-RFR and Neural network regression algorithm combined with associate rules(AR-BP)to a synthetic geological model and a real dataset.The results prove that the selection attributes from associate rules is more efficient than that from random forest.Compared to the drilled wells,AR-RFR has higher precision than RFR and AR-BP.And AR-RFR also can improve the lateral distribution of sand bodies.The method proposed is able to choose efficient seismic attributes and improve prediction of sand thickness.
作者
曲志鹏
王芳芳
张云银
李晓晨
Qu Zhipeng;Wang Fangfang;Zhang Yunyin;Li Xiaochen(Geophysical Research Institute of Shengli Oilfield Branch Company,SINOPEC,Dongying Shandong 257022,China;Institute of Geophysics and Geomatics,China University of Geosciences(Wuhan),Wuhan 430074,China;Hubei Subsurface Multi-scale Imaging Key Laboratory,China University of Geosciences(Wuhan),Wuhan 430074,China)
出处
《地质科技通报》
CAS
CSCD
北大核心
2021年第3期211-218,共8页
Bulletin of Geological Science and Technology
基金
国家重大科技专项(2017ZX05072)。
关键词
地震属性优选
关联规则
频繁模式树
随机森林
储层预测
seismic attribute selection
association rule
frequent pattern tree
random forest
reservoir prediction
