The integration of elastic wave properties and machine learning for the distribution of petrophysical properties in reservoir modeling

7th AIC‐ICMR on Sciences and Engineering 2017

自然科学;工业技术

Ratnam, T.C.^1 ; Ghosh, D.P.^1 ; Negash, B.M.^2

Petroleum Geoscience Department, Universiti Teknologi PETRONAS, Malaysia^1

Petroleum Engineering Department, Universiti Teknologi PETRONAS, Malaysia^2

关键词: Geostatistical method;    Good correlations;    Neutron porosity;    Petrophysical properties;    Reservoir modeling;    Seismic inversion;    Spatial prediction;    Wave properties;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/352/1/012024/pdf
DOI  :  10.1088/1757-899X/352/1/012024

来源: IOP

【 摘 要 】

Conventional reservoir modeling employs variograms to predict the spatial distribution of petrophysical properties. This study aims to improve property distribution by incorporating elastic wave properties. In this study, elastic wave properties obtained from seismic inversion are used as input for an artificial neural network to predict neutron porosity in between well locations. The method employed in this study is supervised learning based on available well logs. This method converts every seismic trace into a pseudo-well log, hence reducing the uncertainty between well locations. By incorporating the seismic response, the reliance on geostatistical methods such as variograms for the distribution of petrophysical properties is reduced drastically. The results of the artificial neural network show good correlation with the neutron porosity log which gives confidence for spatial prediction in areas where well logs are not available.