Pattern recognition methods, multiple linear regression model is used to interpret the real well logging data. The results, achieved with multiple linear ...
A discrimination model for identifying the properties of reservoir fluids is built by using the borehole log and reservoir parameters of oil (gas) layers, ...
The results, achieved with multiple linear regression model is basically in accord with the formation testing results, and no oil and gas layers are missed.
The model makes use of borehole log and reservoir parameters (Peng et al. 2016) . Pattern recognition methods with the multiple linear regression Complimentary ...
Article on Identification of low resistivity oil and gas reservoirs with multiple linear regression model, published in on 2016-08-01 by Zhen Peng+3.
The low-resistivity oil zones can be quickly and accurately identified through the relationship analysis of the five properties of reservoirs, cross plotting ...
Aug 25, 2023 · The findings demonstrated that the grey-correlation multiple regression method had poor accuracy in predicting gas content in low-resistivity ...
An optimized neural network classification model was established to identify low resistivity gas layers based on deep learning theory.
Missing: multiple linear
The SVM learning method was used to construct the SVM classification model and SVR regression model for fluid identification and reservoir parameter prediction.
Low resistivity contrast oil reservoirs are subtle reservoirs that have no obvious difference in physical and electrical properties from water layers.