Prediction of Poisson's ratio for a petroleum engineering application: Machine learning methods.

ABSTRACT: Static Poisson's ratio (νs) is an essential property used in petroleum calculations, namely fracture pressure (FP). The νs is often determined in the laboratory; however, due to time and cost constraints, quicker and cheaper alternatives are sought, such as data-driven models. However, existing methods lack the accuracy needed for critical applications, necessitating the need to explore more accurate methods. In addition, the previous studies used limited datasets and they do not show the relationships between the inputs and output. Therefore, this study developed a reliable model to predict the νs accurately using the nineteen most common learning methods. The proposed models were created based on a large data of 1691 datasets from different countries. The best-performing model of the nin

SUBMITTER: Alakbari FS 

PROVIDER: S-EPMC11844917 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature