石油地球物理勘探2023，Vol.58Issue(6)：1436-1445,10.DOI:10.13810/j.cnki.issn.1000-7210.2023.06.015
基于粒子群优化的相关向量机算法的井震联合储层参数预测与不确定性评估
Seismic and well logs integration for reservoir parameter prediction and uncertainty evaluation based on relevance vector machine opti-mized by particle swarm optimization
摘要
Abstract
There are three types of methods for predicting porosity and saturation,which are rock physics,geostatis-tics,and seismic multi-attribute.The first type with clear physical meaning is widely used,but it has certain limita-tions.The second type can improve resolution compared with conventional methods,yet it is difficult to predict reservoir parameters in complex structural areas.The support vector machine(SVM)belongs to the third type.Its computational complexity increases with the rise of sample quantity.Meanwhile,it is difficult to evaluate the uncer-tainty.The relevance vector machine(RVM)in the third type lacks a clear theory for selecting kernel parameters.To improve this,particle swarm optimization(PSO)is applied to guide the selection of kernel parameters.The reservoir parameters are quantitatively predicted on the basis of obtaining the optimal kernel parameters.Then,the coefficient of variation is introduced to eliminate the influence of dimension and quantify the uncertainty of prediction results.With the help of a stepwise regression algorithm to screen seismic attributes,this paper proposes a quantita-tive porosity and saturation prediction method based on RVM optimized by PSO(PSO-RVM).The results of nu-merical simulation and field application show that:①PSO RVM has good learning performance,satisfying genera lization ability,and a certain ability of anti-noise.The RMS error of PSO RVM prediction results is lower than that of RVM,and the prediction accuracy is higher,which indicates that PSO can effectively guide the selection of RVM kernel parameters and improve the algorithm performance.②PSO-RVM provides a posterior probability,and it can quantify uncertainty by introducing a coefficient of variation.③From seismic and well logs data,the porosity and gas saturation are quantitatively predicted by PSO-RVM with high prediction accuracy.Additionally,the accuracy of porosity prediction is higher,and the uncertainty is lower.关键词
储层参数/地震属性/PSO/RVM/变异系数/不确定性评估Key words
reservoir parameter/seismic attribute/particle swarm optimization/relevance vector machine/coefficient of variation/uncertainty evaluation分类
天文与地球科学引用本文复制引用
代仕明,李敏,唐金良,朱童,李京南,胡华锋..基于粒子群优化的相关向量机算法的井震联合储层参数预测与不确定性评估[J].石油地球物理勘探,2023,58(6):1436-1445,10.基金项目
本项研究受国家自然科学基金项目"海相深层油气富集机理与关键工程技术基础研究"(U19B6003)资助. （U19B6003）
