A model-independent sensitivity analysis for (deep) neural network, Bilateral sensitivity
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …

(DL) We present a novel approach to rank Deep Learning hyper-parameters through the
application of Sensitivity Analysis (SA). DL hyper-parameter tuning is crucial to model …

Sensitivity analysis is a popular feature selection approach employed to identify the
important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at …

In petroleum production system, interwell connectivity evaluation is a significant process to
understand reservoir properties comprehensively, determine water injection rate …

Determining injector-producer relationships, ie, to quantify the inter-well connectivity
between injectors and producers in a reservoir, is a complex and non-stationary problem. In …

Classical sensitivity analysis of machine learning regression models is a topic sparse in
literature. Most of data-driven models are complex black boxes with limited potential of …

Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in
the petroleum industry. This investigation proposes an alternative way for evaluating …

Reservoir characterization is to find reservoir properties of interest by combining available
geological information. In channel reservoirs, flow responses are very sensitive depending …

Sensitivity analysis allows us to decompose the variance output into its source components.
Total sensitivity index represents the effects of varying a feature on the variance of the target …

Waterflooding is a significantly important process in the life of an oil field to sweep previously
unrecovered oil between injection and production wells and maintain reservoir pressure at …