摘要
储层参数与横波速度之间存在一定的相关关系,但是这种复杂关系很难得到解析解。为此,构建了GRU(gated recurrent unit)神经网络方法,主要包括神经网络构建、数据预处理、样本训练和数据预测四个部分,通过训练神经网络逼近横波速度与储层参数之间的关系,利用纵波速度、密度和自然伽马等储层参数直接预测横波速度。采用D区的30口井的测井数据训练和测试神经网络,结果表明:①纵波速度、密度和电阻率对数与横波速度呈较好的正相关关系,自然伽马值、孔隙度与横波速度呈负相关关系。②对于多数井训练、少数井验证,训练数据预测的横波速度与真实值的相对误差和相关系数分别约为3.00%和0.9837,测试数据预测的横波速度与真实值的相对误差和相关系数分别约3.19%和0.9805;对于少数井训练、多数井验证,训练数据预测的横波速度与真实值的相对误差和相关系数分别约为2.49%和0.9867,测试数据预测的横波速度与真实值的相对误差和相关系数分别约3.92%和0.9686。因此所提方法具有较高预测精度和较强泛化能力。
There is a relationship between reservoir parameters and shear wave velocity.But it is too complex to get analytic solutions.This paper proposes a GRU(gated recurrent unit)neural network which includes building a neural network,data preprocessing,samples training and data predication.After approximating the relationship between Swave velocity and reservoir parameters by training a neural network,S-wave velocity can be predicted directly from P-wave velocity,density,gamma ray,porosity and logarithm of resistivity.The neural network was trained and tested by logging data from 30 wells in Block D.The results show that:(1)The P-wave velocity,density and logarithm of resistivity are positively related to the S-wave velocity,while the gamma ray and porosity are negatively related to the S-wave velocity;(2)In the case of being trained by more wells and tested by less wells,the relative error between the S-wave velocity and the real one is about 3.00%,and the correlation coefficient is 0.9837 for training data,while they are 3.19%and 0.9805 for tested data.In the case of being trained by less wells and tested by more wellsg,the relative error is 2.49%and the correlation coefficient is 0.9867 for training data,while they are 3.92%and 0.9686 for testing data.The new method has a high prediction accuracy and generalization ability.
作者
孙宇航
刘洋
SUN Yuhang;LIU Yang(State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing 102249,China;CNPC Key Laboratory of Geophysical Prospecting,China University of Petroleum(Beijing),Beijing102249,China;Karamay Campus,China University of Petroleum(Beijing),Karamay,Xinjiang 834000,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2020年第3期484-492,503,467,共11页
Oil Geophysical Prospecting
基金
国家科技重大专项“多波地震勘探配套技术”(2017ZX05018-005)资助
关键词
横波速度预测
GRU神经网络
储层参数
prediction of S-wave velocity
GRU neural network
reservoir parameters
作者简介
刘洋,新疆维吾尔自治区克拉玛依市中国石油大学(北京)克拉玛依校区,834000。Email:wliuyang@vip.sina.com;孙宇航,博士研究生,1991年生,2015年获中国石油大学(北京)勘查技术与工程专业学士学位,2018年获中国石油大学(北京)地质工程专业硕士学位,现在中国石油大学(北京)攻读地质资源与地质工程专业博士学位,主要从事流体识别和深度学习方面的研究。
