New and Highly Accurate Static Young's Modulus Model Using Machine Learning Techniques.

ABSTRACT: Static Young's modulus (E _s) is a critical property required in numerous petroleum calculations. Various models to forecast E _s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E _s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using tren

SUBMITTER: Alakbari FS 

PROVIDER: S-EPMC11447732 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature