测井技术2025，Vol.49Issue(3)：346-354,9.DOI:10.16489/j.issn.1004-1338.2025.03.003
神经网络驱动的多极子模式波频散正演及其现场应用
Neural Network-Driven Forward Modeling of Multipole Mode Wave Dispersion and Its Field Applications
摘要
Abstract
In acoustic logging,multipole waves(such as dipole waves and quadrupole waves)generated by the sound source are dispersive mode waves,and their dispersion effects can lead to inaccurate wave velocity measurement.Traditional physics-driven methods for forward modeling dispersion curves are computationally inefficient,affecting the reliability and timeliness of wave velocity applications in pore pressure prediction and wellbore stability analysis.A neural network-driven method for forward modeling the dispersion of mode wave is proposed to address this issue.This method combines equivalent tool theory with borehole acoustic field propagation theory to forward model dispersion datasets.Using a multi-layer fully connected neural network architecture and dispersion calculation model training,it can rapidly output theoretical dispersion curves.Applications show that using the proposed neural network model reduces computational costs by four orders of magnitude.Theoretical simulations and field data validation confirm the stability and effectiveness of the method.This study develops a novel approach for forward modeling the theoretical dispersion of multipole mode waves and inverting formation parameters.关键词
声波测井/频散正演模拟/神经网络/地层参数反演Key words
acoustic logging/dispersion forward modeling/neural network/formation parameter inversion分类
天文与地球科学引用本文复制引用
郑凯尹,苏远大,张恒建,古希浩,李盛清,庄春喜,唐晓明..神经网络驱动的多极子模式波频散正演及其现场应用[J].测井技术,2025,49(3):346-354,9.基金项目
国家自然科学基金项目"随钻声波远探测成像测井理论与实验方法"(U21B2064) （U21B2064）
山东省自然科学基金项目"声波测井"(ZR2024YQ062) （ZR2024YQ062）
崂山国家实验室科技创新项目"南海高温高压条件下测井响应机理与油气评价"(LSKJ202203407) （LSKJ202203407）
