...Gabriel Gomes Vargas; Pablo Silva Ortiz; Silvio de Oliveira, Jr. This study assesses renewable hydrogen production via gasification of residual biomass, using artificial neural networks (ANNs) for predictive modeling. The process uses residues from sugarcane and orange harvests, sewage sludge, corn...

... is to develop an artificial neural network (ANN) model for predicting the drillstring vibration while drilling a horizontal section. The ANN model uses the surface drilling parameters as model inputs to predict the three types of drillstring vibrations. These surface drilling parameters are flowrate, mud...

... not be recorded for every well. However, drilling data are available in real-time for every well using real-time drilling sensors. The main objective of this paper is to predict sonic slowness logs in real-time based on the drilling data using artificial neural network (ANN). The data used in this study were...

... for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANNs) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively...

...’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs...