Enhancing the resolution of sparse rock property measurements using machine learning and random field theory

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外文摘要：The travel time of rock compressional waves is an essential parameter used for estimating important rock properties,such as porosity,permeability,and lithology.Current methods,like wireline logging tests,provide broad measurements but lack finer resolution.Laboratory-based rock core measurements offer higher resolution but are resource-intensive.Conventionally,wireline logging and rock core measure-ments have been used independently.This study introduces a novel approach that integrates both data sources.The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data.By combining machine learning with random field theory,the method allows for probabilistic predictions in regions with sparse data sampling.In this framework,12 parameters from wireline tests are used to predict trends in rock core data.The residuals are modeled using random field theory.The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual.By utilizing unconditional and conditional random field the-ories,this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates.This integrated approach optimizes the use of existing core and logging data.Its applicability is confirmed in an oil project in West China.

外文关键词：

Wireline logsCore characterizationCompressional wave travel timeMachine learningRandom field theory

作者：

Jiawei Xie、Jinsong Huang、Fuxiang Zhang、Jixiang He、Kaifeng Kang、Yunqiang Sun

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作者单位：

Discipline of Civil,Surveying and Environmental Engineering,Priority Research Centre for Geotechnical Science and Engineering,The University of Newcastle,Callaghan,NSW,2308,Australia

Intercontinental Strait Energy Technology Co.,Ltd.,Beijing,China

Institute of Exploration and Development of Xinjiang Oilfield Company,PetroChina,Karamay,China

基金：

Australian Government through the Australian Research Council's Discovery Projects funding schemeNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

DP1901015924197228052179103

出版年：

2024

DOI：

10.1016/j.jrmge.2024.03.016

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(10)