Modeling liquid rate through wellhead chokes using machine learning techniques.

ABSTRACT: Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg-Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (P_wh), gas-to-liquid ratio (GLR), and choke size (D_c

SUBMITTER: Dabiri MS 

PROVIDER: S-EPMC10960849 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature