基于偏最小二乘回归法的储层厚度预测
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
首次采用偏最小二乘回归法进行储层厚度预测,推导其数学算法,总结其优势,建立正演模型分析其可行性。针对靶区的地震数据进行地震属性的提取,优选出可以较好地描述砂体分布情况的5种属性,分别为波峰数、平均振幅、平均瞬时相位、振幅立方差和能量半衰时。利用这5种属性分别对靶区应用主成分分析法、神经网络法和偏最小二乘回归法,得到井点处的砂体厚度预测值。根据各自绝对误差和相对误差,推断应用最小二乘回归法预测砂体厚度值更为准确。根据建立的回归方程,对靶区进行砂体厚度预测,得到砂体厚度分布情况。
On the basis of deducing the mathematical algorithm and summarizing the advantages,the feasibility of the partial least square regression method,to be the first in predicting reservoir thickness,was analyzed by building up the forward model.According to the seismic data of target areas,five kinds of seismic attributes,which can describe the sand body distribution well,were extracted. The five kinds of seismic attributes include the number of peaks,mean amplitude,average instantaneous phase,amplitude variance and energy half- life. Based on the five kinds of seismic attributes,the sandbody thicknesses of some wells in the study area were respectively predicted by adopting the principal component analysis method,the neural network method,and the partial least squares regression method. It was found in the comparison of the corresponding absolute and relative errors that the value predicted by the least squares regression method was more accurate. Based on the established regression equation,the sandbody thickness in the target area can be forecasted to obtain the sandbody thickness distribution.
引文
[1]陈晓东.川东南北部飞仙关组地震相分析及储层预测研究[D].成都理工大学,2012.
    [2]杨立强,邬长武,董宁.基于模拟退火算法的随机反演技术在砂体预测中的应用[J].地球物理学进展,2013,28(1):0287-0292.
    [3]许名文,姜瑞波,雷新华,等.稀疏脉冲波阻抗反演技术在储层预测中的应用[J].油气地球物理,2011,9(2):24-27.
    [4]张娟.基于多元线性回归分析的薄储层预测技术在胜利探区的研究与应用[J].工程地球物理学报,2013,10(1):91-94.
    [5]李飞,张萍,王赛英.BP神经网络在计算储层参数中的应用[J].中国西部科技,2013,1:38-40.
    [6]王婷,,杨斌,杨勇,等.利用BP神经网络预测储层参数[J].辽宁化工,2013,42(2):160-163.
    [7]陈冬.地震多属性分析及其在储层预测中的应用研究[D].中国地质大学(北京),2008.
    [8]罗批,郭继昌,李锵,等.基于偏最小二乘回归建模的探讨[J].天津大学学报,2002,35(6):783-786.
    [9]张忠诚.一类基于偏最小二乘回归分析的成分数据预测模型[J].华中师范大学学报(自然科学版),2006,40(2):161-163.
    [10]董玉才,李红燕,朱连军,等.基于偏最小二乘回归的油田产量预测[J].信息系统工程,2009,10:1-7.

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心